From 4532604e6f7efa19602294f3c84da8063c694e0c Mon Sep 17 00:00:00 2001
From: cgoyal23 <167020791+cgoyal23@users.noreply.github.com>
Date: Fri, 24 May 2024 22:34:00 +0530
Subject: [PATCH 1/3] Created using Colab

---
 230335.ipynb | 761 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 761 insertions(+)
 create mode 100644 230335.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/cgoyal23/Customer_Churn_Analysis/blob/main/230335.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3eJmduQENaek"
+      },
+      "source": [
+        "# Assignment 1.1 - Pandas Data Analysis Practice\n",
+        "\n",
+        "*This assignment is a part of the project [\"Customer Churn Analysis: From Data To Strategy\"]*\n",
+        "\n",
+        "In this assignment, you'll get to practice some of the concepts and skills covered in pandas.\n",
+        "\n",
+        "As you go through this notebook, you will find a **???** in certain places. To complete this assignment, you must replace all the **???** with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.\n",
+        "\n",
+        "Some things to keep in mind:\n",
+        "\n",
+        "* Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.\n",
+        "* In some cases, you may need to add some code cells or new statements before or after the line of code containing the **???**.\n",
+        "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n",
+        "\n",
+        "\n",
+        "If you are stuck, you can ask for help on the Whatsapp group. Please do not use **ChatGPT or any other LLM to get solutions to the questions.**\n",
+        "\n",
+        "\n",
+        "Deadline : **22 May, 11:59 pm**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "nzCoPd_zNae4"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yKwMduIXNae5"
+      },
+      "source": [
+        "In this assignment, we're going to analyze an operate on data from a CSV file. Let's begin by downloading the CSV file."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xa18DHWvNae8"
+      },
+      "source": [
+        "Let's load the data from the CSV file into a Pandas data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "2QPUhz19Nae-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "outputId": "9aa4e1b9-a78a-4ab6-f09c-3ad9fd4bb91d"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "FileNotFoundError",
+          "evalue": "[Errno 2] No such file or directory: 'countries.csv'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-2-4107ec6dae3f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountries_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'countries.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'countries.csv'"
+          ]
+        }
+      ],
+      "source": [
+        "countries_df = pd.read_csv('countries.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7_kR3k1wNae_"
+      },
+      "outputs": [],
+      "source": [
+        "countries_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KE8FlOjINafA"
+      },
+      "source": [
+        "**Q1: How many countries does the dataframe contain?**\n",
+        "\n",
+        "Hint: Use the `.shape` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZD4-AM-CNafB"
+      },
+      "outputs": [],
+      "source": [
+        "num_countries = countries_df.shape[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Iy8SMfkwNafC"
+      },
+      "outputs": [],
+      "source": [
+        "print('There are {} countries in the dataset'.format(num_countries))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m9eCXKMCNafE"
+      },
+      "source": [
+        "**Q2: Retrieve a list of continents from the dataframe?**\n",
+        "\n",
+        "*Hint: Use the `.unique` method of a series.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xL8zFetJNafF"
+      },
+      "outputs": [],
+      "source": [
+        "continents = countries_df['continent'].unique()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "40j7HkWZNafG"
+      },
+      "outputs": [],
+      "source": [
+        "continents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yr9rdD5ZNafK"
+      },
+      "source": [
+        "**Q3: What is the total population of all the countries listed in this dataset?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IrGeG0vwNafL"
+      },
+      "outputs": [],
+      "source": [
+        "total_population = countries_df['population'].sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FUJRPPMcNafM"
+      },
+      "outputs": [],
+      "source": [
+        "print('The total population is {}.'.format(int(total_population)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nvs9htXuNafO"
+      },
+      "source": [
+        "**Q: (Optional) What is the overall life expectancy across in the world?**\n",
+        "\n",
+        "*Hint: You'll need to take a weighted average of life expectancy using populations as weights.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8TXTdZ0ENafP"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "97C4RlcPNafP"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H1H_0zo8NafR"
+      },
+      "source": [
+        "**Q4: Create a dataframe containing 10 countries with the highest population.**\n",
+        "\n",
+        "*Hint: Chain the `sort_values` and `head` methods.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3BBebkbyNafR"
+      },
+      "outputs": [],
+      "source": [
+        "most_populous_df = countries_df.sort_values('population', ascending=False).head(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L83PYtCaNafS"
+      },
+      "outputs": [],
+      "source": [
+        "most_populous_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RldAR4WJNafT"
+      },
+      "source": [
+        "**Q5: Add a new column in `countries_df` to record the overall GDP per country (product of population & per capita GDP).**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R_mdT6GMNafU"
+      },
+      "outputs": [],
+      "source": [
+        "countries_df['gdp'] = countries_df['population'] * countries_df['gdp_per_capita']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FLydP8pHNafV"
+      },
+      "outputs": [],
+      "source": [
+        "countries_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RXtXfNfBNafX"
+      },
+      "source": [
+        "**Q: (Optional) Create a dataframe containing 10 countries with the lowest GDP per capita, among the counties with population greater than 100 million.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EkCt9Wl7NafX"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nM5flE-QNafY"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_NA6oQnVNafZ"
+      },
+      "source": [
+        "**Q6: Create a data frame that counts the number countries in each continent?**\n",
+        "\n",
+        "*Hint: Use `groupby`, select the `location` column and aggregate using `count`.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Co-tfE7BNafv"
+      },
+      "outputs": [],
+      "source": [
+        "country_counts_df = countries_df.groupby('continent')['location'].count()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6fVwjKFFNafw"
+      },
+      "outputs": [],
+      "source": [
+        "country_counts_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ut6cNp0yNafy"
+      },
+      "source": [
+        "**Q7: Create a data frame showing the total population of each continent.**\n",
+        "\n",
+        "*Hint: Use `groupby`, select the population column and aggregate using `sum`.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hqnwb5V0Nafy"
+      },
+      "outputs": [],
+      "source": [
+        "continent_populations_df = countries_df.groupby('continent')['population'].sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D5gAu5IANafz"
+      },
+      "outputs": [],
+      "source": [
+        "continent_populations_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Am9h8-R4Naf1"
+      },
+      "source": [
+        "Let's download another CSV file containing overall Covid-19 stats for various countires, and read the data into another Pandas data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sOybiAeVNaf6"
+      },
+      "outputs": [],
+      "source": [
+        "covid_data_df = pd.read_csv('covid-countries-data.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": true,
+        "id": "k9PuPoYUNaf7"
+      },
+      "outputs": [],
+      "source": [
+        "covid_data_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weKZowz0Naf8"
+      },
+      "source": [
+        "**Q8: Count the number of countries for which the `total_tests` data is missing.**\n",
+        "\n",
+        "*Hint: Use the `.isna` method.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UybZmvdFNaf9"
+      },
+      "outputs": [],
+      "source": [
+        "total_tests_missing = covid_data_df['total_tests'].isna().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0_jz32NxNaf-"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"The data for total tests is missing for {} countries.\".format(int(total_tests_missing)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bK9yyH3GNagA"
+      },
+      "source": [
+        "Let's merge the two data frames, and compute some more metrics.\n",
+        "\n",
+        "**Q9: Merge `countries_df` with `covid_data_df` on the `location` column.**\n",
+        "\n",
+        "*Hint: Use the `.merge` method on `countries_df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6qu0tGtJNagB"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df = countries_df.merge(covid_data_df, on='location')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yNR9WKaENagC"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DMFIKAXvNagG"
+      },
+      "source": [
+        "**Q10: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_KdWa_juNagH"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IDdn-5CONagI"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "93n1py5BNagK"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['deaths_per_million'] = combined_df['total_deaths'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IRMupBBUNagL"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VNjlGq-uNagO"
+      },
+      "source": [
+        "**Q11: Create a dataframe with 10 countires that have highest number of tests per million people.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HBcHEMJZNagO"
+      },
+      "outputs": [],
+      "source": [
+        "highest_tests_df = combined_df.sort_values('tests_per_million', ascending=False).head(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R57UrLhxNagR"
+      },
+      "outputs": [],
+      "source": [
+        "highest_tests_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ug-VTfalNagT"
+      },
+      "source": [
+        "**Q12: Create a dataframe with 10 countires that have highest number of positive cases per million people.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HxjUJq8oNagU"
+      },
+      "outputs": [],
+      "source": [
+        "highest_cases_df = ???"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7EuO7nPENagY"
+      },
+      "outputs": [],
+      "source": [
+        "highest_cases_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GX-WdUFHNagb"
+      },
+      "source": [
+        "**Q13: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hH19qct0Nagc"
+      },
+      "outputs": [],
+      "source": [
+        "highest_deaths_df = ???"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lWhydo7ENagd"
+      },
+      "outputs": [],
+      "source": [
+        "highest_deaths_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nUTDc8q4Nage"
+      },
+      "source": [
+        "**(Optional) Q: Count number of countries that feature in both the lists of \"highest number of tests per million\" and \"highest number of cases per million\".**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nXat9wCrNagf"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z7y8D9OtNagg"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aJXjCbJnNagg"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uHWghQHUNagh"
+      },
+      "source": [
+        "**(Optional) Q: Count number of countries that feature in both the lists \"20 countries with lowest GDP per capita\" and \"20 countries with the lowest number of hospital beds per thousand population\". Only consider countries with a population higher than 10 million while creating the list.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5C7khtW3Nagi"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RKk0r3umNagj"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XH7POtliNagj"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wvjafOzENagk"
+      },
+      "source": [
+        "## Submission\n",
+        "\n",
+        "Congratulations on making it this far! You've reached the end of this assignment, and you just completed your first real-world data analysis problem.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LBOb5DhHNagl"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.6"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From 89d6f35b9e8dae04d53ab1bc34fe1ad16d07d239 Mon Sep 17 00:00:00 2001
From: cgoyal23 <167020791+cgoyal23@users.noreply.github.com>
Date: Sat, 25 May 2024 06:47:19 +0530
Subject: [PATCH 2/3] Created using Colab

---
 Copy_of_Assignment_1_1_by_chirag.ipynb | 5709 ++++++++++++++++++++++++
 1 file changed, 5709 insertions(+)
 create mode 100644 Copy_of_Assignment_1_1_by_chirag.ipynb

diff --git a/Copy_of_Assignment_1_1_by_chirag.ipynb b/Copy_of_Assignment_1_1_by_chirag.ipynb
new file mode 100644
index 0000000..900e1db
--- /dev/null
+++ b/Copy_of_Assignment_1_1_by_chirag.ipynb
@@ -0,0 +1,5709 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/cgoyal23/Customer_Churn_Analysis/blob/main/Copy_of_Assignment_1_1_by_chirag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3eJmduQENaek"
+      },
+      "source": [
+        "# Assignment 1.1 - Pandas Data Analysis Practice\n",
+        "\n",
+        "*This assignment is a part of the project [\"Customer Churn Analysis: From Data To Strategy\"]*\n",
+        "\n",
+        "In this assignment, you'll get to practice some of the concepts and skills covered in pandas.\n",
+        "\n",
+        "As you go through this notebook, you will find a **???** in certain places. To complete this assignment, you must replace all the **???** with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.\n",
+        "\n",
+        "Some things to keep in mind:\n",
+        "\n",
+        "* Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.\n",
+        "* In some cases, you may need to add some code cells or new statements before or after the line of code containing the **???**.\n",
+        "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n",
+        "\n",
+        "\n",
+        "If you are stuck, you can ask for help on the Whatsapp group. Please do not use **ChatGPT or any other LLM to get solutions to the questions.**\n",
+        "\n",
+        "\n",
+        "Deadline : **22 May, 11:59 pm**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qmBvIMj63uUa",
+        "outputId": "1394f8fc-0d5b-4791-d9ea-8f8e39920e78"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nzCoPd_zNae4"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yKwMduIXNae5"
+      },
+      "source": [
+        "In this assignment, we're going to analyze an operate on data from a CSV file. Let's begin by downloading the CSV file."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xa18DHWvNae8"
+      },
+      "source": [
+        "Let's load the data from the CSV file into a Pandas data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2QPUhz19Nae-"
+      },
+      "outputs": [],
+      "source": [
+        "countries_df = pd.read_csv('countries.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7_kR3k1wNae_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "199cfd5f-aba7-4404-9415-69194ed8cf81"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location continent  population  life_expectancy  \\\n",
+              "0       Afghanistan      Asia    38928341            64.83   \n",
+              "1           Albania    Europe     2877800            78.57   \n",
+              "2           Algeria    Africa    43851043            76.88   \n",
+              "3           Andorra    Europe       77265            83.73   \n",
+              "4            Angola    Africa    32866268            61.15   \n",
+              "..              ...       ...         ...              ...   \n",
+              "205         Vietnam      Asia    97338583            75.40   \n",
+              "206  Western Sahara    Africa      597330            70.26   \n",
+              "207           Yemen      Asia    29825968            66.12   \n",
+              "208          Zambia    Africa    18383956            63.89   \n",
+              "209        Zimbabwe    Africa    14862927            61.49   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita  \n",
+              "0                          0.50        1803.987  \n",
+              "1                          2.89       11803.431  \n",
+              "2                          1.90       13913.839  \n",
+              "3                           NaN             NaN  \n",
+              "4                           NaN        5819.495  \n",
+              "..                          ...             ...  \n",
+              "205                        2.60        6171.884  \n",
+              "206                         NaN             NaN  \n",
+              "207                        0.70        1479.147  \n",
+              "208                        2.00        3689.251  \n",
+              "209                        1.70        1899.775  \n",
+              "\n",
+              "[210 rows x 6 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-97e8b15c-171c-43eb-b6cc-deb662eafe5f\" class=\"colab-df-container\">\n",
+              "    <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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>38928341</td>\n",
+              "      <td>64.83</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1803.987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>2877800</td>\n",
+              "      <td>78.57</td>\n",
+              "      <td>2.89</td>\n",
+              "      <td>11803.431</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>43851043</td>\n",
+              "      <td>76.88</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>13913.839</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>77265</td>\n",
+              "      <td>83.73</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Angola</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>32866268</td>\n",
+              "      <td>61.15</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5819.495</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>Vietnam</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>97338583</td>\n",
+              "      <td>75.40</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>6171.884</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>597330</td>\n",
+              "      <td>70.26</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>29825968</td>\n",
+              "      <td>66.12</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>1479.147</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>18383956</td>\n",
+              "      <td>63.89</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>3689.251</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Zimbabwe</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>14862927</td>\n",
+              "      <td>61.49</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>1899.775</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>210 rows × 6 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97e8b15c-171c-43eb-b6cc-deb662eafe5f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
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+              "      gap: 12px;\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-97e8b15c-171c-43eb-b6cc-deb662eafe5f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-97e8b15c-171c-43eb-b6cc-deb662eafe5f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-46926fa3-ef96-407a-8409-fd14b065c9fb button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_52cce475-4a35-4bea-86cb-6877a3ca9b01\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('countries_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_52cce475-4a35-4bea-86cb-6877a3ca9b01 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('countries_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "countries_df",
+              "summary": "{\n  \"name\": \"countries_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205,\n        \"min\": 809,\n        \"max\": 1439323774,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445,\n          2078932,\n          786559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "countries_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KE8FlOjINafA"
+      },
+      "source": [
+        "**Q1: How many countries does the dataframe contain?**\n",
+        "\n",
+        "Hint: Use the `.shape` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZD4-AM-CNafB"
+      },
+      "outputs": [],
+      "source": [
+        "num_countries = countries_df.shape[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Iy8SMfkwNafC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6f00f29d-84f6-46d2-99dc-00bad9eb4e61"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "There are 210 countries in the dataset\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('There are {} countries in the dataset'.format(num_countries))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m9eCXKMCNafE"
+      },
+      "source": [
+        "**Q2: Retrieve a list of continents from the dataframe?**\n",
+        "\n",
+        "*Hint: Use the `.unique` method of a series.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xL8zFetJNafF"
+      },
+      "outputs": [],
+      "source": [
+        "continents = countries_df[\"continent\"].unique()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "40j7HkWZNafG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c5fe26e1-f9f9-4b04-c106-d2282616d1ef"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Asia', 'Europe', 'Africa', 'North America', 'South America',\n",
+              "       'Oceania'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "continents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yr9rdD5ZNafK"
+      },
+      "source": [
+        "**Q3: What is the total population of all the countries listed in this dataset?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IrGeG0vwNafL"
+      },
+      "outputs": [],
+      "source": [
+        "total_population = countries_df[\"population\"].sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FUJRPPMcNafM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ccb6d9df-ab33-4e53-fcbd-4494a3adc11e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The total population is 7757980095.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('The total population is {}.'.format(int(total_population)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nvs9htXuNafO"
+      },
+      "source": [
+        "**Q: (Optional) What is the overall life expectancy across in the world?**\n",
+        "\n",
+        "*Hint: You'll need to take a weighted average of life expectancy using populations as weights.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8TXTdZ0ENafP"
+      },
+      "outputs": [],
+      "source": [
+        "x= countries_df[\"population\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "97C4RlcPNafP"
+      },
+      "outputs": [],
+      "source": [
+        "y= countries_df[\"life_expectancy\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mean= ((x*y).sum())/x.sum()"
+      ],
+      "metadata": {
+        "id": "Cp6-hlNS7kvg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mean"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9xesg93-_Ypv",
+        "outputId": "f99e34c8-9d36-4c19-8af6-6244a01463d1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "72.72165193409664"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H1H_0zo8NafR"
+      },
+      "source": [
+        "**Q4: Create a dataframe containing 10 countries with the highest population.**\n",
+        "\n",
+        "*Hint: Chain the `sort_values` and `head` methods.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3BBebkbyNafR"
+      },
+      "outputs": [],
+      "source": [
+        "most_populous_df = countries_df.sort_values(by=\"population\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L83PYtCaNafS"
+      },
+      "outputs": [],
+      "source": [
+        "a=most_populous_df.tail(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a.iloc[::-1]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "AXsWwb3nAmlg",
+        "outputId": "d7b17b4d-b566-444c-8188-3dd6908e6744"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          location      continent  population  life_expectancy  \\\n",
+              "41           China           Asia  1439323774            76.91   \n",
+              "90           India           Asia  1380004385            69.66   \n",
+              "199  United States  North America   331002647            78.86   \n",
+              "91       Indonesia           Asia   273523621            71.72   \n",
+              "145       Pakistan           Asia   220892331            67.27   \n",
+              "27          Brazil  South America   212559409            75.88   \n",
+              "141        Nigeria         Africa   206139587            54.69   \n",
+              "15      Bangladesh           Asia   164689383            72.59   \n",
+              "157         Russia         Europe   145934460            72.58   \n",
+              "125         Mexico  North America   128932753            75.05   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita  \n",
+              "41                         4.34       15308.712  \n",
+              "90                         0.53        6426.674  \n",
+              "199                        2.77       54225.446  \n",
+              "91                         1.04       11188.744  \n",
+              "145                        0.60        5034.708  \n",
+              "27                         2.20       14103.452  \n",
+              "141                         NaN        5338.454  \n",
+              "15                         0.80        3523.984  \n",
+              "157                        8.05       24765.954  \n",
+              "125                        1.38       17336.469  "
+            ],
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+              "      <th>41</th>\n",
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+              "      <td>0.60</td>\n",
+              "      <td>5034.708</td>\n",
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+              "      <th>27</th>\n",
+              "      <td>Brazil</td>\n",
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+              "      <td>14103.452</td>\n",
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+              "      <th>141</th>\n",
+              "      <td>Nigeria</td>\n",
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+              "      <td>NaN</td>\n",
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+              "      <td>72.59</td>\n",
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+              "      <th>157</th>\n",
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+              "      <td>8.05</td>\n",
+              "      <td>24765.954</td>\n",
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+              "    <tr>\n",
+              "      <th>125</th>\n",
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+              "      <td>North America</td>\n",
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+              "      <td>1.38</td>\n",
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"a\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Russia\",\n          \"India\",\n          \"Brazil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"North America\",\n          \"Europe\",\n          \"South America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 509275248,\n        \"min\": 128932753,\n        \"max\": 1439323774,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          145934460,\n          1380004385,\n          212559409\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.8433072738584855,\n        \"min\": 54.69,\n        \"max\": 78.86,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          72.58,\n          69.66,\n          75.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4507283497859254,\n        \"min\": 0.53,\n        \"max\": 8.05,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8.05,\n          0.53,\n          2.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15083.054118576796,\n        \"min\": 3523.984,\n        \"max\": 54225.446,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          24765.954,\n          6426.674,\n          14103.452\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RldAR4WJNafT"
+      },
+      "source": [
+        "**Q5: Add a new column in `countries_df` to record the overall GDP per country (product of population & per capita GDP).**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R_mdT6GMNafU"
+      },
+      "outputs": [],
+      "source": [
+        "countries_df['gdp'] = countries_df[\"gdp_per_capita\"]*countries_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FLydP8pHNafV",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "330fc9ec-8a86-4d92-88dd-f90be7865b54"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location continent  population  life_expectancy  \\\n",
+              "0       Afghanistan      Asia    38928341            64.83   \n",
+              "1           Albania    Europe     2877800            78.57   \n",
+              "2           Algeria    Africa    43851043            76.88   \n",
+              "3           Andorra    Europe       77265            83.73   \n",
+              "4            Angola    Africa    32866268            61.15   \n",
+              "..              ...       ...         ...              ...   \n",
+              "205         Vietnam      Asia    97338583            75.40   \n",
+              "206  Western Sahara    Africa      597330            70.26   \n",
+              "207           Yemen      Asia    29825968            66.12   \n",
+              "208          Zambia    Africa    18383956            63.89   \n",
+              "209        Zimbabwe    Africa    14862927            61.49   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  \n",
+              "0                          0.50        1803.987  7.022622e+10  \n",
+              "1                          2.89       11803.431  3.396791e+10  \n",
+              "2                          1.90       13913.839  6.101364e+11  \n",
+              "3                           NaN             NaN           NaN  \n",
+              "4                           NaN        5819.495  1.912651e+11  \n",
+              "..                          ...             ...           ...  \n",
+              "205                        2.60        6171.884  6.007624e+11  \n",
+              "206                         NaN             NaN           NaN  \n",
+              "207                        0.70        1479.147  4.411699e+10  \n",
+              "208                        2.00        3689.251  6.782303e+10  \n",
+              "209                        1.70        1899.775  2.823622e+10  \n",
+              "\n",
+              "[210 rows x 7 columns]"
+            ],
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+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>38928341</td>\n",
+              "      <td>64.83</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1803.987</td>\n",
+              "      <td>7.022622e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>2877800</td>\n",
+              "      <td>78.57</td>\n",
+              "      <td>2.89</td>\n",
+              "      <td>11803.431</td>\n",
+              "      <td>3.396791e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>43851043</td>\n",
+              "      <td>76.88</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>13913.839</td>\n",
+              "      <td>6.101364e+11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>77265</td>\n",
+              "      <td>83.73</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Angola</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>32866268</td>\n",
+              "      <td>61.15</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5819.495</td>\n",
+              "      <td>1.912651e+11</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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>Vietnam</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>97338583</td>\n",
+              "      <td>75.40</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>6171.884</td>\n",
+              "      <td>6.007624e+11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>597330</td>\n",
+              "      <td>70.26</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>29825968</td>\n",
+              "      <td>66.12</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>1479.147</td>\n",
+              "      <td>4.411699e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>18383956</td>\n",
+              "      <td>63.89</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>3689.251</td>\n",
+              "      <td>6.782303e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Zimbabwe</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>14862927</td>\n",
+              "      <td>61.49</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>1899.775</td>\n",
+              "      <td>2.823622e+10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>210 rows × 7 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00d5397a-aaaf-43c6-85f9-4b14bd4b780d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-00d5397a-aaaf-43c6-85f9-4b14bd4b780d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-00d5397a-aaaf-43c6-85f9-4b14bd4b780d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-70bb55ad-9a6d-4a55-ba97-bc55eb0c6374\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70bb55ad-9a6d-4a55-ba97-bc55eb0c6374')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-70bb55ad-9a6d-4a55-ba97-bc55eb0c6374 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_476a1cd5-1de1-41ba-bcaf-5a1e7909c632\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('countries_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_476a1cd5-1de1-41ba-bcaf-5a1e7909c632 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('countries_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "countries_df",
+              "summary": "{\n  \"name\": \"countries_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205,\n        \"min\": 809,\n        \"max\": 1439323774,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445,\n          2078932,\n          786559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.954,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "countries_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RXtXfNfBNafX"
+      },
+      "source": [
+        "** ***bold text***Q: (Optional) Create a dataframe containing 10 countries with the lowest GDP per capita, among the counties with population greater than 100 million.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EkCt9Wl7NafX"
+      },
+      "outputs": [],
+      "source": [
+        "drop_down= countries_df[countries_df[\"population\"]>100000000]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nM5flE-QNafY"
+      },
+      "outputs": [],
+      "source": [
+        "cc= drop_down.sort_values(by=\"gdp_per_capita\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cc.head(10)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "jawAmGewHXZr",
+        "outputId": "a4ed4b6b-d1d3-4aaa-fd3e-0352b12e3bff"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        location      continent  population  life_expectancy  \\\n",
+              "63      Ethiopia         Africa   114963583            66.60   \n",
+              "15    Bangladesh           Asia   164689383            72.59   \n",
+              "145     Pakistan           Asia   220892331            67.27   \n",
+              "141      Nigeria         Africa   206139587            54.69   \n",
+              "90         India           Asia  1380004385            69.66   \n",
+              "151  Philippines           Asia   109581085            71.23   \n",
+              "58         Egypt         Africa   102334403            71.99   \n",
+              "91     Indonesia           Asia   273523621            71.72   \n",
+              "27        Brazil  South America   212559409            75.88   \n",
+              "41         China           Asia  1439323774            76.91   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  \n",
+              "63                         0.30        1729.927  1.988786e+11  \n",
+              "15                         0.80        3523.984  5.803628e+11  \n",
+              "145                        0.60        5034.708  1.112128e+12  \n",
+              "141                         NaN        5338.454  1.100467e+12  \n",
+              "90                         0.53        6426.674  8.868838e+12  \n",
+              "151                        1.00        7599.188  8.327273e+11  \n",
+              "58                         1.60       10550.206  1.079649e+12  \n",
+              "91                         1.04       11188.744  3.060386e+12  \n",
+              "27                         2.20       14103.452  2.997821e+12  \n",
+              "41                         4.34       15308.712  2.203419e+13  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b345080b-f8c5-410c-9d03-c90e081252f5\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
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+              "        text-align: right;\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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>63</th>\n",
+              "      <td>Ethiopia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>114963583</td>\n",
+              "      <td>66.60</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>1729.927</td>\n",
+              "      <td>1.988786e+11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Bangladesh</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>164689383</td>\n",
+              "      <td>72.59</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>3523.984</td>\n",
+              "      <td>5.803628e+11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>Pakistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>220892331</td>\n",
+              "      <td>67.27</td>\n",
+              "      <td>0.60</td>\n",
+              "      <td>5034.708</td>\n",
+              "      <td>1.112128e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>141</th>\n",
+              "      <td>Nigeria</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>206139587</td>\n",
+              "      <td>54.69</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5338.454</td>\n",
+              "      <td>1.100467e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>90</th>\n",
+              "      <td>India</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>1380004385</td>\n",
+              "      <td>69.66</td>\n",
+              "      <td>0.53</td>\n",
+              "      <td>6426.674</td>\n",
+              "      <td>8.868838e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>151</th>\n",
+              "      <td>Philippines</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>109581085</td>\n",
+              "      <td>71.23</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>7599.188</td>\n",
+              "      <td>8.327273e+11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>58</th>\n",
+              "      <td>Egypt</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>102334403</td>\n",
+              "      <td>71.99</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>10550.206</td>\n",
+              "      <td>1.079649e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>91</th>\n",
+              "      <td>Indonesia</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>273523621</td>\n",
+              "      <td>71.72</td>\n",
+              "      <td>1.04</td>\n",
+              "      <td>11188.744</td>\n",
+              "      <td>3.060386e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Brazil</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>212559409</td>\n",
+              "      <td>75.88</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>14103.452</td>\n",
+              "      <td>2.997821e+12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>China</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>1439323774</td>\n",
+              "      <td>76.91</td>\n",
+              "      <td>4.34</td>\n",
+              "      <td>15308.712</td>\n",
+              "      <td>2.203419e+13</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      fill: #FFFFFF;\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "  </button>\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    90% {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e2e04f50-1eec-4e2a-a084-f284a4f3050b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "cc",
+              "summary": "{\n  \"name\": \"cc\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"China\",\n          \"Russia\",\n          \"Ethiopia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"South America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 452256768,\n        \"min\": 102334403,\n        \"max\": 1439323774,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          1439323774,\n          145934460,\n          114963583\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.865998562753727,\n        \"min\": 54.69,\n        \"max\": 84.63,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          76.91,\n          72.58,\n          66.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.7111574668275322,\n        \"min\": 0.3,\n        \"max\": 13.05,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          13.05,\n          1.38,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14875.982836683967,\n        \"min\": 1729.927,\n        \"max\": 54225.446,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          15308.712,\n          24765.954,\n          1729.927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6769601807621.744,\n        \"min\": 198878606248.44098,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          22034193130919.086,\n          3614206123374.8403,\n          198878606248.44098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_NA6oQnVNafZ"
+      },
+      "source": [
+        "**Q6: Create a data frame that counts the number countries in each continent?**\n",
+        "\n",
+        "*Hint: Use `groupby`, select the `location` column and aggregate using `count`.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Co-tfE7BNafv"
+      },
+      "outputs": [],
+      "source": [
+        "country_counts_df = countries_df.groupby([\"continent\"])[\"continent\"].count()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6fVwjKFFNafw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9a5f513c-2b5d-4a7a-d251-4fbc6d58459e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "continent\n",
+              "Africa           55\n",
+              "Asia             47\n",
+              "Europe           51\n",
+              "North America    36\n",
+              "Oceania           8\n",
+              "South America    13\n",
+              "Name: continent, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "country_counts_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ut6cNp0yNafy"
+      },
+      "source": [
+        "**Q7: Create a data frame showing the total population of each continent.**\n",
+        "\n",
+        "*Hint: Use `groupby`, select the population column and aggregate using `sum`.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hqnwb5V0Nafy"
+      },
+      "outputs": [],
+      "source": [
+        "continent_populations_df = countries_df.groupby([\"continent\"])[\"population\"].sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D5gAu5IANafz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f5c1aaf6-3097-4507-dc09-8ddbb56b9144"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "continent\n",
+              "Africa           1339423921\n",
+              "Asia             4607388081\n",
+              "Europe            748506210\n",
+              "North America     591242473\n",
+              "Oceania            40958320\n",
+              "South America     430461090\n",
+              "Name: population, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ],
+      "source": [
+        "continent_populations_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Am9h8-R4Naf1"
+      },
+      "source": [
+        "Let's download another CSV file containing overall Covid-19 stats for various countires, and read the data into another Pandas data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sOybiAeVNaf6"
+      },
+      "outputs": [],
+      "source": [
+        "covid_data_df = pd.read_csv('covid-countries-data.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": true,
+        "id": "k9PuPoYUNaf7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "56598118-2557-45fb-e20e-44633f2633b5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location  total_cases  total_deaths  total_tests\n",
+              "0       Afghanistan      38243.0        1409.0          NaN\n",
+              "1           Albania       9728.0         296.0          NaN\n",
+              "2           Algeria      45158.0        1525.0          NaN\n",
+              "3           Andorra       1199.0          53.0          NaN\n",
+              "4            Angola       2729.0         109.0          NaN\n",
+              "..              ...          ...           ...          ...\n",
+              "207  Western Sahara        766.0           1.0          NaN\n",
+              "208           World   26059065.0      863535.0          NaN\n",
+              "209           Yemen       1976.0         571.0          NaN\n",
+              "210          Zambia      12415.0         292.0          NaN\n",
+              "211        Zimbabwe       6638.0         206.0      97272.0\n",
+              "\n",
+              "[212 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4ef10c29-6d3f-4bb7-937f-fd72cb40ab43\" class=\"colab-df-container\">\n",
+              "    <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>location</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>38243.0</td>\n",
+              "      <td>1409.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Albania</td>\n",
+              "      <td>9728.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>45158.0</td>\n",
+              "      <td>1525.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>1199.0</td>\n",
+              "      <td>53.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Angola</td>\n",
+              "      <td>2729.0</td>\n",
+              "      <td>109.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>766.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>World</td>\n",
+              "      <td>26059065.0</td>\n",
+              "      <td>863535.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>1976.0</td>\n",
+              "      <td>571.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>210</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>12415.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>211</th>\n",
+              "      <td>Zimbabwe</td>\n",
+              "      <td>6638.0</td>\n",
+              "      <td>206.0</td>\n",
+              "      <td>97272.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>212 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ef10c29-6d3f-4bb7-937f-fd72cb40ab43')\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4ef10c29-6d3f-4bb7-937f-fd72cb40ab43 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4ef10c29-6d3f-4bb7-937f-fd72cb40ab43');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "</svg>\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-color: transparent;\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9498a317-9aa5-48bf-bbb9-cbfa9e143982 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_7e9d521d-a425-4ee8-93ca-588c6daf463d\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid_data_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7e9d521d-a425-4ee8-93ca-588c6daf463d button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('covid_data_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "covid_data_df",
+              "summary": "{\n  \"name\": \"covid_data_df\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 212,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Nicaragua\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1875762.2640246067,\n        \"min\": 3.0,\n        \"max\": 26059065.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          16454.0,\n          3310.0,\n          1382.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61654.54433404689,\n        \"min\": 0.0,\n        \"max\": 863535.0,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          42.0,\n          626.0,\n          329.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ],
+      "source": [
+        "covid_data_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "weKZowz0Naf8"
+      },
+      "source": [
+        "**Q8: Count the number of countries for which the `total_tests` data is missing.**\n",
+        "\n",
+        "*Hint: Use the `.isna` method.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UybZmvdFNaf9"
+      },
+      "outputs": [],
+      "source": [
+        "total_tests_missing = covid_data_df[\"total_tests\"].isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0_jz32NxNaf-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "51a5f900-be50-435e-aea4-ee168acfd662"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The data for total tests is missing for 122 countries.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"The data for total tests is missing for {} countries.\".format(int(total_tests_missing)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bK9yyH3GNagA"
+      },
+      "source": [
+        "Let's merge the two data frames, and compute some more metrics.\n",
+        "\n",
+        "**Q9: Merge `countries_df` with `covid_data_df` on the `location` column.**\n",
+        "\n",
+        "*Hint: Use the `.merge` method on `countries_df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6qu0tGtJNagB"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df = pd.merge(countries_df,covid_data_df,on=\"location\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yNR9WKaENagC",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 441
+        },
+        "outputId": "9f3cb0f7-dd54-49cc-cd2d-f1bef7aa6e2e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location continent  population  life_expectancy  \\\n",
+              "0       Afghanistan      Asia    38928341            64.83   \n",
+              "1           Albania    Europe     2877800            78.57   \n",
+              "2           Algeria    Africa    43851043            76.88   \n",
+              "3           Andorra    Europe       77265            83.73   \n",
+              "4            Angola    Africa    32866268            61.15   \n",
+              "..              ...       ...         ...              ...   \n",
+              "205         Vietnam      Asia    97338583            75.40   \n",
+              "206  Western Sahara    Africa      597330            70.26   \n",
+              "207           Yemen      Asia    29825968            66.12   \n",
+              "208          Zambia    Africa    18383956            63.89   \n",
+              "209        Zimbabwe    Africa    14862927            61.49   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
+              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
+              "1                          2.89       11803.431  3.396791e+10       9728.0   \n",
+              "2                          1.90       13913.839  6.101364e+11      45158.0   \n",
+              "3                           NaN             NaN           NaN       1199.0   \n",
+              "4                           NaN        5819.495  1.912651e+11       2729.0   \n",
+              "..                          ...             ...           ...          ...   \n",
+              "205                        2.60        6171.884  6.007624e+11       1046.0   \n",
+              "206                         NaN             NaN           NaN        766.0   \n",
+              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
+              "208                        2.00        3689.251  6.782303e+10      12415.0   \n",
+              "209                        1.70        1899.775  2.823622e+10       6638.0   \n",
+              "\n",
+              "     total_deaths  total_tests  \n",
+              "0          1409.0          NaN  \n",
+              "1           296.0          NaN  \n",
+              "2          1525.0          NaN  \n",
+              "3            53.0          NaN  \n",
+              "4           109.0          NaN  \n",
+              "..            ...          ...  \n",
+              "205          35.0     261004.0  \n",
+              "206           1.0          NaN  \n",
+              "207         571.0          NaN  \n",
+              "208         292.0          NaN  \n",
+              "209         206.0      97272.0  \n",
+              "\n",
+              "[210 rows x 10 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f5a11811-6632-42e8-8a17-85a0f090cec9\" class=\"colab-df-container\">\n",
+              "    <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",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>38928341</td>\n",
+              "      <td>64.83</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1803.987</td>\n",
+              "      <td>7.022622e+10</td>\n",
+              "      <td>38243.0</td>\n",
+              "      <td>1409.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>2877800</td>\n",
+              "      <td>78.57</td>\n",
+              "      <td>2.89</td>\n",
+              "      <td>11803.431</td>\n",
+              "      <td>3.396791e+10</td>\n",
+              "      <td>9728.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>43851043</td>\n",
+              "      <td>76.88</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>13913.839</td>\n",
+              "      <td>6.101364e+11</td>\n",
+              "      <td>45158.0</td>\n",
+              "      <td>1525.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>77265</td>\n",
+              "      <td>83.73</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1199.0</td>\n",
+              "      <td>53.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Angola</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>32866268</td>\n",
+              "      <td>61.15</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5819.495</td>\n",
+              "      <td>1.912651e+11</td>\n",
+              "      <td>2729.0</td>\n",
+              "      <td>109.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>Vietnam</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>97338583</td>\n",
+              "      <td>75.40</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>6171.884</td>\n",
+              "      <td>6.007624e+11</td>\n",
+              "      <td>1046.0</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>261004.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>597330</td>\n",
+              "      <td>70.26</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>766.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>29825968</td>\n",
+              "      <td>66.12</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>1479.147</td>\n",
+              "      <td>4.411699e+10</td>\n",
+              "      <td>1976.0</td>\n",
+              "      <td>571.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>18383956</td>\n",
+              "      <td>63.89</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>3689.251</td>\n",
+              "      <td>6.782303e+10</td>\n",
+              "      <td>12415.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Zimbabwe</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>14862927</td>\n",
+              "      <td>61.49</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>1899.775</td>\n",
+              "      <td>2.823622e+10</td>\n",
+              "      <td>6638.0</td>\n",
+              "      <td>206.0</td>\n",
+              "      <td>97272.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>210 rows × 10 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5a11811-6632-42e8-8a17-85a0f090cec9')\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f5a11811-6632-42e8-8a17-85a0f090cec9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f5a11811-6632-42e8-8a17-85a0f090cec9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "\n",
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+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
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+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-823dc2ce-7ba7-427d-a557-b9531f1cf33c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0dc97c85-d27f-494d-8473-7d39aed8f5a4\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('combined_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0dc97c85-d27f-494d-8473-7d39aed8f5a4 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('combined_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "combined_df",
+              "summary": "{\n  \"name\": \"combined_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205,\n        \"min\": 809,\n        \"max\": 1439323774,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445,\n          2078932,\n          786559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.954,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577759.0509830201,\n        \"min\": 3.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          896.0,\n          317528.0,\n          246116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17424.11835293718,\n        \"min\": 0.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          29.0,\n          67376.0,\n          577.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ],
+      "source": [
+        "combined_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DMFIKAXvNagG"
+      },
+      "source": [
+        "**Q10: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_KdWa_juNagH"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IDdn-5CONagI"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "93n1py5BNagK"
+      },
+      "outputs": [],
+      "source": [
+        "combined_df['deaths_per_million'] = combined_df['total_deaths'] * 1e6 / combined_df['population']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IRMupBBUNagL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "outputId": "b48b5032-ae77-4d99-8831-8ae62db8143a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location continent  population  life_expectancy  \\\n",
+              "0       Afghanistan      Asia    38928341            64.83   \n",
+              "1           Albania    Europe     2877800            78.57   \n",
+              "2           Algeria    Africa    43851043            76.88   \n",
+              "3           Andorra    Europe       77265            83.73   \n",
+              "4            Angola    Africa    32866268            61.15   \n",
+              "..              ...       ...         ...              ...   \n",
+              "205         Vietnam      Asia    97338583            75.40   \n",
+              "206  Western Sahara    Africa      597330            70.26   \n",
+              "207           Yemen      Asia    29825968            66.12   \n",
+              "208          Zambia    Africa    18383956            63.89   \n",
+              "209        Zimbabwe    Africa    14862927            61.49   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
+              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
+              "1                          2.89       11803.431  3.396791e+10       9728.0   \n",
+              "2                          1.90       13913.839  6.101364e+11      45158.0   \n",
+              "3                           NaN             NaN           NaN       1199.0   \n",
+              "4                           NaN        5819.495  1.912651e+11       2729.0   \n",
+              "..                          ...             ...           ...          ...   \n",
+              "205                        2.60        6171.884  6.007624e+11       1046.0   \n",
+              "206                         NaN             NaN           NaN        766.0   \n",
+              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
+              "208                        2.00        3689.251  6.782303e+10      12415.0   \n",
+              "209                        1.70        1899.775  2.823622e+10       6638.0   \n",
+              "\n",
+              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
+              "0          1409.0          NaN                NaN         982.394806   \n",
+              "1           296.0          NaN                NaN        3380.359997   \n",
+              "2          1525.0          NaN                NaN        1029.804468   \n",
+              "3            53.0          NaN                NaN       15518.022390   \n",
+              "4           109.0          NaN                NaN          83.033462   \n",
+              "..            ...          ...                ...                ...   \n",
+              "205          35.0     261004.0        2681.403324          10.745996   \n",
+              "206           1.0          NaN                NaN        1282.373228   \n",
+              "207         571.0          NaN                NaN          66.250993   \n",
+              "208         292.0          NaN                NaN         675.317108   \n",
+              "209         206.0      97272.0        6544.605918         446.614587   \n",
+              "\n",
+              "     deaths_per_million  \n",
+              "0             36.194710  \n",
+              "1            102.856349  \n",
+              "2             34.776824  \n",
+              "3            685.950948  \n",
+              "4              3.316470  \n",
+              "..                  ...  \n",
+              "205            0.359570  \n",
+              "206            1.674116  \n",
+              "207           19.144391  \n",
+              "208           15.883415  \n",
+              "209           13.859989  \n",
+              "\n",
+              "[210 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4d75ab01-8df6-4ce3-bad0-51fcc2cb511d\" class=\"colab-df-container\">\n",
+              "    <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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "      <th>tests_per_million</th>\n",
+              "      <th>cases_per_million</th>\n",
+              "      <th>deaths_per_million</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>38928341</td>\n",
+              "      <td>64.83</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>1803.987</td>\n",
+              "      <td>7.022622e+10</td>\n",
+              "      <td>38243.0</td>\n",
+              "      <td>1409.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>982.394806</td>\n",
+              "      <td>36.194710</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>2877800</td>\n",
+              "      <td>78.57</td>\n",
+              "      <td>2.89</td>\n",
+              "      <td>11803.431</td>\n",
+              "      <td>3.396791e+10</td>\n",
+              "      <td>9728.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3380.359997</td>\n",
+              "      <td>102.856349</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>43851043</td>\n",
+              "      <td>76.88</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>13913.839</td>\n",
+              "      <td>6.101364e+11</td>\n",
+              "      <td>45158.0</td>\n",
+              "      <td>1525.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1029.804468</td>\n",
+              "      <td>34.776824</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>77265</td>\n",
+              "      <td>83.73</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1199.0</td>\n",
+              "      <td>53.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>15518.022390</td>\n",
+              "      <td>685.950948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Angola</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>32866268</td>\n",
+              "      <td>61.15</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5819.495</td>\n",
+              "      <td>1.912651e+11</td>\n",
+              "      <td>2729.0</td>\n",
+              "      <td>109.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>83.033462</td>\n",
+              "      <td>3.316470</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>205</th>\n",
+              "      <td>Vietnam</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>97338583</td>\n",
+              "      <td>75.40</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>6171.884</td>\n",
+              "      <td>6.007624e+11</td>\n",
+              "      <td>1046.0</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>261004.0</td>\n",
+              "      <td>2681.403324</td>\n",
+              "      <td>10.745996</td>\n",
+              "      <td>0.359570</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>597330</td>\n",
+              "      <td>70.26</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>766.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1282.373228</td>\n",
+              "      <td>1.674116</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>29825968</td>\n",
+              "      <td>66.12</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>1479.147</td>\n",
+              "      <td>4.411699e+10</td>\n",
+              "      <td>1976.0</td>\n",
+              "      <td>571.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>66.250993</td>\n",
+              "      <td>19.144391</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>18383956</td>\n",
+              "      <td>63.89</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>3689.251</td>\n",
+              "      <td>6.782303e+10</td>\n",
+              "      <td>12415.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>675.317108</td>\n",
+              "      <td>15.883415</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Zimbabwe</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>14862927</td>\n",
+              "      <td>61.49</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>1899.775</td>\n",
+              "      <td>2.823622e+10</td>\n",
+              "      <td>6638.0</td>\n",
+              "      <td>206.0</td>\n",
+              "      <td>97272.0</td>\n",
+              "      <td>6544.605918</td>\n",
+              "      <td>446.614587</td>\n",
+              "      <td>13.859989</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>210 rows × 13 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d75ab01-8df6-4ce3-bad0-51fcc2cb511d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\n",
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+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "\n",
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+              "        document.querySelector('#df-4d75ab01-8df6-4ce3-bad0-51fcc2cb511d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    20% {\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
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+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
+              "  <div id=\"id_254f1aff-c292-4429-aefe-8b76c4111f7c\">\n",
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+              "      .colab-df-generate {\n",
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+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
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+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('combined_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_254f1aff-c292-4429-aefe-8b76c4111f7c button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('combined_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "combined_df",
+              "summary": "{\n  \"name\": \"combined_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205,\n        \"min\": 809,\n        \"max\": 1439323774,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.954,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577759.0509830201,\n        \"min\": 3.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          896.0,\n          317528.0,\n          246116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17424.11835293718,\n        \"min\": 0.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          29.0,\n          67376.0,\n          577.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 137501.5426053333,\n        \"min\": 1969.1317223799424,\n        \"max\": 725696.6351209254,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          8289.217482188455,\n          7562.655732467907,\n          131192.26184700849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5920.784473156789,\n        \"min\": 3.0238238837004348,\n        \"max\": 41375.74364990663,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          2368.011835741666,\n          1437.2764477144995,\n          1757.0201345353623\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181.9372798469054,\n        \"min\": 0.0,\n        \"max\": 1237.5508279804349,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          0.3595696477315681,\n          576.2792756308129,\n          854.0403754533369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ],
+      "source": [
+        "combined_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VNjlGq-uNagO"
+      },
+      "source": [
+        "**Q11: Create a dataframe with 10 countires that have highest number of tests per million people.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HBcHEMJZNagO"
+      },
+      "outputs": [],
+      "source": [
+        "highest_tests_df = combined_df.sort_values(by=\"tests_per_million\").tail(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "R57UrLhxNagR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 539
+        },
+        "outputId": "54cf7dcd-37f8-4adc-cdc6-e0a9bd288dde"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         location      continent  population  life_expectancy  \\\n",
+              "189                         Timor           Asia     1318442            69.50   \n",
+              "191           Trinidad and Tobago  North America     1399491            73.51   \n",
+              "194      Turks and Caicos Islands  North America       38718            80.22   \n",
+              "200  United States Virgin Islands  North America      104423            80.58   \n",
+              "202                    Uzbekistan           Asia    33469199            71.72   \n",
+              "203                       Vatican         Europe         809            75.12   \n",
+              "204                     Venezuela  South America    28435943            72.06   \n",
+              "206                Western Sahara         Africa      597330            70.26   \n",
+              "207                         Yemen           Asia    29825968            66.12   \n",
+              "208                        Zambia         Africa    18383956            63.89   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
+              "189                         5.9        6570.102  8.662298e+09         27.0   \n",
+              "191                         3.0       28763.071  4.025366e+10       1920.0   \n",
+              "194                         NaN             NaN           NaN        555.0   \n",
+              "200                         NaN             NaN           NaN       1144.0   \n",
+              "202                         4.0        6253.104  2.092864e+11      42540.0   \n",
+              "203                         NaN             NaN           NaN         12.0   \n",
+              "204                         0.8       16745.022  4.761605e+11      48883.0   \n",
+              "206                         NaN             NaN           NaN        766.0   \n",
+              "207                         0.7        1479.147  4.411699e+10       1976.0   \n",
+              "208                         2.0        3689.251  6.782303e+10      12415.0   \n",
+              "\n",
+              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
+              "189           0.0          NaN                NaN          20.478717   \n",
+              "191          28.0          NaN                NaN        1371.927365   \n",
+              "194           4.0          NaN                NaN       14334.418100   \n",
+              "200          15.0          NaN                NaN       10955.440851   \n",
+              "202         329.0          NaN                NaN        1271.019363   \n",
+              "203           0.0          NaN                NaN       14833.127318   \n",
+              "204         398.0          NaN                NaN        1719.056759   \n",
+              "206           1.0          NaN                NaN        1282.373228   \n",
+              "207         571.0          NaN                NaN          66.250993   \n",
+              "208         292.0          NaN                NaN         675.317108   \n",
+              "\n",
+              "     deaths_per_million  \n",
+              "189            0.000000  \n",
+              "191           20.007274  \n",
+              "194          103.311121  \n",
+              "200          143.646515  \n",
+              "202            9.829933  \n",
+              "203            0.000000  \n",
+              "204           13.996371  \n",
+              "206            1.674116  \n",
+              "207           19.144391  \n",
+              "208           15.883415  "
+            ],
+            "text/html": [
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+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "      <th>tests_per_million</th>\n",
+              "      <th>cases_per_million</th>\n",
+              "      <th>deaths_per_million</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>189</th>\n",
+              "      <td>Timor</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>1318442</td>\n",
+              "      <td>69.50</td>\n",
+              "      <td>5.9</td>\n",
+              "      <td>6570.102</td>\n",
+              "      <td>8.662298e+09</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>20.478717</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>191</th>\n",
+              "      <td>Trinidad and Tobago</td>\n",
+              "      <td>North America</td>\n",
+              "      <td>1399491</td>\n",
+              "      <td>73.51</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>28763.071</td>\n",
+              "      <td>4.025366e+10</td>\n",
+              "      <td>1920.0</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1371.927365</td>\n",
+              "      <td>20.007274</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>194</th>\n",
+              "      <td>Turks and Caicos Islands</td>\n",
+              "      <td>North America</td>\n",
+              "      <td>38718</td>\n",
+              "      <td>80.22</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>555.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>14334.418100</td>\n",
+              "      <td>103.311121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>200</th>\n",
+              "      <td>United States Virgin Islands</td>\n",
+              "      <td>North America</td>\n",
+              "      <td>104423</td>\n",
+              "      <td>80.58</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1144.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>10955.440851</td>\n",
+              "      <td>143.646515</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>202</th>\n",
+              "      <td>Uzbekistan</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>33469199</td>\n",
+              "      <td>71.72</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6253.104</td>\n",
+              "      <td>2.092864e+11</td>\n",
+              "      <td>42540.0</td>\n",
+              "      <td>329.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1271.019363</td>\n",
+              "      <td>9.829933</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>203</th>\n",
+              "      <td>Vatican</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>809</td>\n",
+              "      <td>75.12</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>14833.127318</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>204</th>\n",
+              "      <td>Venezuela</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>28435943</td>\n",
+              "      <td>72.06</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>16745.022</td>\n",
+              "      <td>4.761605e+11</td>\n",
+              "      <td>48883.0</td>\n",
+              "      <td>398.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1719.056759</td>\n",
+              "      <td>13.996371</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>206</th>\n",
+              "      <td>Western Sahara</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>597330</td>\n",
+              "      <td>70.26</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>766.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1282.373228</td>\n",
+              "      <td>1.674116</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>207</th>\n",
+              "      <td>Yemen</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>29825968</td>\n",
+              "      <td>66.12</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1479.147</td>\n",
+              "      <td>4.411699e+10</td>\n",
+              "      <td>1976.0</td>\n",
+              "      <td>571.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>66.250993</td>\n",
+              "      <td>19.144391</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>208</th>\n",
+              "      <td>Zambia</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>18383956</td>\n",
+              "      <td>63.89</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3689.251</td>\n",
+              "      <td>6.782303e+10</td>\n",
+              "      <td>12415.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>675.317108</td>\n",
+              "      <td>15.883415</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c55f8a1a-ccc0-45d9-b1ab-e35cd8b2c5f7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c55f8a1a-ccc0-45d9-b1ab-e35cd8b2c5f7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c55f8a1a-ccc0-45d9-b1ab-e35cd8b2c5f7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-98f12fca-17cb-4365-a1d1-eb7d4d5f86a4\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98f12fca-17cb-4365-a1d1-eb7d4d5f86a4')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-98f12fca-17cb-4365-a1d1-eb7d4d5f86a4 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_ae61fd60-b721-416d-af04-80930813fc3c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('highest_tests_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_ae61fd60-b721-416d-af04-80930813fc3c button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('highest_tests_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "highest_tests_df",
+              "summary": "{\n  \"name\": \"highest_tests_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Yemen\",\n          \"Trinidad and Tobago\",\n          \"Vatican\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"North America\",\n          \"Africa\",\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14416436,\n        \"min\": 809,\n        \"max\": 33469199,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          29825968,\n          1399491,\n          809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.39565009984895,\n        \"min\": 63.89,\n        \"max\": 80.58,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          66.12,\n          73.51,\n          75.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0056586615540213,\n        \"min\": 0.7,\n        \"max\": 5.9,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.9,\n          3.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10331.899734931489,\n        \"min\": 1479.147,\n        \"max\": 28763.071,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6570.102,\n          28763.071,\n          3689.251\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 178549628190.39688,\n        \"min\": 8662298421.084,\n        \"max\": 476160491125.74603,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          8662298421.084,\n          40253658996.861,\n          67823028056.956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18702.00588053473,\n        \"min\": 12.0,\n        \"max\": 48883.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1976.0,\n          1920.0,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 213.61221355010994,\n        \"min\": 0.0,\n        \"max\": 571.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          571.0,\n          28.0,\n          398.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6124.436199092139,\n        \"min\": 20.47871654574111,\n        \"max\": 14833.127317676144,\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.32516121758338,\n        \"min\": 0.0,\n        \"max\": 143.6465146567327,\n        \"num_unique_values\": 9,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 67
+        }
+      ],
+      "source": [
+        "highest_tests_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ug-VTfalNagT"
+      },
+      "source": [
+        "**Q12: Create a dataframe with 10 countires that have highest number of positive cases per million people.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HxjUJq8oNagU"
+      },
+      "outputs": [],
+      "source": [
+        "highest_cases_df = combined_df.sort_values(by=\"total_cases\").tail(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7EuO7nPENagY",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 504
+        },
+        "outputId": "09665709-b020-42b9-d77a-125a4490ce62"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          location      continent  population  life_expectancy  \\\n",
+              "177          Spain         Europe    46754783            83.56   \n",
+              "125         Mexico  North America   128932753            75.05   \n",
+              "174   South Africa         Africa    59308690            64.13   \n",
+              "42        Colombia  South America    50882884            77.29   \n",
+              "150           Peru  South America    32971846            76.74   \n",
+              "157         Russia         Europe   145934460            72.58   \n",
+              "90           India           Asia  1380004385            69.66   \n",
+              "27          Brazil  South America   212559409            75.88   \n",
+              "199  United States  North America   331002647            78.86   \n",
+              "87       Hong Kong           Asia     7496988            84.86   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
+              "177                        2.97       34272.360  1.602397e+12     479554.0   \n",
+              "125                        1.38       17336.469  2.235239e+12     610957.0   \n",
+              "174                        2.32       12294.876  7.291930e+11     630595.0   \n",
+              "42                         1.71       13254.949  6.744500e+11     633339.0   \n",
+              "150                        1.60       12236.706  4.034668e+11     663437.0   \n",
+              "157                        8.05       24765.954  3.614206e+12    1005000.0   \n",
+              "90                         0.53        6426.674  8.868838e+12    3853406.0   \n",
+              "27                         2.20       14103.452  2.997821e+12    3997865.0   \n",
+              "199                        2.77       54225.446  1.794877e+13    6114406.0   \n",
+              "87                          NaN       56054.920  4.202431e+11          NaN   \n",
+              "\n",
+              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
+              "177       29194.0    6416533.0      137238.001939       10256.790198   \n",
+              "125       65816.0    1271295.0        9860.140037        4738.570967   \n",
+              "174       14389.0    3705408.0       62476.645497       10632.421657   \n",
+              "42        20348.0    2647702.0       52035.218758       12446.994946   \n",
+              "150       29259.0     584232.0       17719.117092       20121.318048   \n",
+              "157       17414.0   37176827.0      254750.159763        6886.653091   \n",
+              "90        67376.0   44337201.0       32128.304433        2792.314316   \n",
+              "27       123780.0    4797948.0       22572.268255       18808.224105   \n",
+              "199      185744.0   83898416.0      253467.507769       18472.377957   \n",
+              "87            NaN     780410.0      104096.471810                NaN   \n",
+              "\n",
+              "     deaths_per_million  \n",
+              "177          624.406705  \n",
+              "125          510.467654  \n",
+              "174          242.612002  \n",
+              "42           399.898716  \n",
+              "150          887.393445  \n",
+              "157          119.327539  \n",
+              "90            48.823033  \n",
+              "27           582.331314  \n",
+              "199          561.155633  \n",
+              "87                  NaN  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-74ee4a5c-b2d4-4e00-9094-c7c1b828c08a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "      <th>tests_per_million</th>\n",
+              "      <th>cases_per_million</th>\n",
+              "      <th>deaths_per_million</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>177</th>\n",
+              "      <td>Spain</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>46754783</td>\n",
+              "      <td>83.56</td>\n",
+              "      <td>2.97</td>\n",
+              "      <td>34272.360</td>\n",
+              "      <td>1.602397e+12</td>\n",
+              "      <td>479554.0</td>\n",
+              "      <td>29194.0</td>\n",
+              "      <td>6416533.0</td>\n",
+              "      <td>137238.001939</td>\n",
+              "      <td>10256.790198</td>\n",
+              "      <td>624.406705</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>125</th>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>North America</td>\n",
+              "      <td>128932753</td>\n",
+              "      <td>75.05</td>\n",
+              "      <td>1.38</td>\n",
+              "      <td>17336.469</td>\n",
+              "      <td>2.235239e+12</td>\n",
+              "      <td>610957.0</td>\n",
+              "      <td>65816.0</td>\n",
+              "      <td>1271295.0</td>\n",
+              "      <td>9860.140037</td>\n",
+              "      <td>4738.570967</td>\n",
+              "      <td>510.467654</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>174</th>\n",
+              "      <td>South Africa</td>\n",
+              "      <td>Africa</td>\n",
+              "      <td>59308690</td>\n",
+              "      <td>64.13</td>\n",
+              "      <td>2.32</td>\n",
+              "      <td>12294.876</td>\n",
+              "      <td>7.291930e+11</td>\n",
+              "      <td>630595.0</td>\n",
+              "      <td>14389.0</td>\n",
+              "      <td>3705408.0</td>\n",
+              "      <td>62476.645497</td>\n",
+              "      <td>10632.421657</td>\n",
+              "      <td>242.612002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>Colombia</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>50882884</td>\n",
+              "      <td>77.29</td>\n",
+              "      <td>1.71</td>\n",
+              "      <td>13254.949</td>\n",
+              "      <td>6.744500e+11</td>\n",
+              "      <td>633339.0</td>\n",
+              "      <td>20348.0</td>\n",
+              "      <td>2647702.0</td>\n",
+              "      <td>52035.218758</td>\n",
+              "      <td>12446.994946</td>\n",
+              "      <td>399.898716</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>150</th>\n",
+              "      <td>Peru</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>32971846</td>\n",
+              "      <td>76.74</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>12236.706</td>\n",
+              "      <td>4.034668e+11</td>\n",
+              "      <td>663437.0</td>\n",
+              "      <td>29259.0</td>\n",
+              "      <td>584232.0</td>\n",
+              "      <td>17719.117092</td>\n",
+              "      <td>20121.318048</td>\n",
+              "      <td>887.393445</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>157</th>\n",
+              "      <td>Russia</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>145934460</td>\n",
+              "      <td>72.58</td>\n",
+              "      <td>8.05</td>\n",
+              "      <td>24765.954</td>\n",
+              "      <td>3.614206e+12</td>\n",
+              "      <td>1005000.0</td>\n",
+              "      <td>17414.0</td>\n",
+              "      <td>37176827.0</td>\n",
+              "      <td>254750.159763</td>\n",
+              "      <td>6886.653091</td>\n",
+              "      <td>119.327539</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>90</th>\n",
+              "      <td>India</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>1380004385</td>\n",
+              "      <td>69.66</td>\n",
+              "      <td>0.53</td>\n",
+              "      <td>6426.674</td>\n",
+              "      <td>8.868838e+12</td>\n",
+              "      <td>3853406.0</td>\n",
+              "      <td>67376.0</td>\n",
+              "      <td>44337201.0</td>\n",
+              "      <td>32128.304433</td>\n",
+              "      <td>2792.314316</td>\n",
+              "      <td>48.823033</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Brazil</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>212559409</td>\n",
+              "      <td>75.88</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>14103.452</td>\n",
+              "      <td>2.997821e+12</td>\n",
+              "      <td>3997865.0</td>\n",
+              "      <td>123780.0</td>\n",
+              "      <td>4797948.0</td>\n",
+              "      <td>22572.268255</td>\n",
+              "      <td>18808.224105</td>\n",
+              "      <td>582.331314</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>199</th>\n",
+              "      <td>United States</td>\n",
+              "      <td>North America</td>\n",
+              "      <td>331002647</td>\n",
+              "      <td>78.86</td>\n",
+              "      <td>2.77</td>\n",
+              "      <td>54225.446</td>\n",
+              "      <td>1.794877e+13</td>\n",
+              "      <td>6114406.0</td>\n",
+              "      <td>185744.0</td>\n",
+              "      <td>83898416.0</td>\n",
+              "      <td>253467.507769</td>\n",
+              "      <td>18472.377957</td>\n",
+              "      <td>561.155633</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>Hong Kong</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>7496988</td>\n",
+              "      <td>84.86</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>56054.920</td>\n",
+              "      <td>4.202431e+11</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>780410.0</td>\n",
+              "      <td>104096.471810</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "  .colab-df-quickchart {\n",
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+              "\n",
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+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
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+              "\n",
+              "      .colab-df-generate:hover {\n",
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+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('highest_cases_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
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+              "        document.querySelector('#id_f35dfac0-f3c5-4a82-bdc4-5aaeef75883f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('highest_cases_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "highest_cases_df",
+              "summary": "{\n  \"name\": \"highest_cases_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"United States\",\n          \"Mexico\",\n          \"Russia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"North America\",\n          \"Asia\",\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 412647772,\n        \"min\": 7496988,\n        \"max\": 1380004385,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          331002647,\n          128932753,\n          145934460\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.14235830569624,\n        \"min\": 64.13,\n        \"max\": 84.86,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          78.86,\n          75.05,\n          72.58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1699718380149036,\n        \"min\": 0.53,\n        \"max\": 8.05,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2.2,\n          1.38,\n          8.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17909.46022854285,\n        \"min\": 6426.674,\n        \"max\": 56054.92,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          54225.446,\n          17336.469,\n          24765.954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5535455191534.749,\n        \"min\": 403466785779.276,\n        \"max\": 17948766160755.562,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          17948766160755.562,\n          2235238675469.157,\n          3614206123374.8403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2095100.088257228,\n        \"min\": 479554.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3997865.0,\n          610957.0,\n          1005000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58425.36917512803,\n        \"min\": 14389.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          123780.0,\n          65816.0,\n          17414.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27946413.588854726,\n        \"min\": 584232.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          83898416.0,\n          1271295.0,\n          37176827.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92988.064035842,\n        \"min\": 9860.14003749691,\n        \"max\": 254750.1597634993,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          253467.50776890313,\n          9860.14003749691,\n          254750.1597634993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6344.933830014123,\n        \"min\": 2792.3143157258883,\n        \"max\": 20121.318048131125,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18808.224104537287,\n          4738.570966525473,\n          6886.65309070935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 267.034582574439,\n        \"min\": 48.823033268839936,\n        \"max\": 887.3934446982435,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          582.3313142538893,\n          510.46765440585915,\n          119.3275392254852\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ],
+      "source": [
+        "highest_cases_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GX-WdUFHNagb"
+      },
+      "source": [
+        "**Q13: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hH19qct0Nagc"
+      },
+      "outputs": [],
+      "source": [
+        "highest_deaths_df = combined_df.sort_values(by=\"deaths_per_million\").tail(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lWhydo7ENagd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "outputId": "fd8ac747-80fa-499b-adb0-347dfa8ee55d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           location      continent  population  life_expectancy  \\\n",
+              "27           Brazil  South America   212559409            75.88   \n",
+              "97            Italy         Europe    60461828            83.51   \n",
+              "40            Chile  South America    19116209            80.18   \n",
+              "198  United Kingdom         Europe    67886004            81.32   \n",
+              "177           Spain         Europe    46754783            83.56   \n",
+              "3           Andorra         Europe       77265            83.73   \n",
+              "18          Belgium         Europe    11589616            81.63   \n",
+              "150            Peru  South America    32971846            76.74   \n",
+              "162      San Marino         Europe       33938            84.97   \n",
+              "87        Hong Kong           Asia     7496988            84.86   \n",
+              "\n",
+              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
+              "27                         2.20       14103.452  2.997821e+12    3997865.0   \n",
+              "97                         3.18       35220.084  2.129471e+12     271515.0   \n",
+              "40                         2.11       22767.037  4.352194e+11     414739.0   \n",
+              "198                        2.54       39753.244  2.698689e+12     338676.0   \n",
+              "177                        2.97       34272.360  1.602397e+12     479554.0   \n",
+              "3                           NaN             NaN           NaN       1199.0   \n",
+              "18                         5.64       42658.576  4.943965e+11      85817.0   \n",
+              "150                        1.60       12236.706  4.034668e+11     663437.0   \n",
+              "162                        3.80       56861.470  1.929765e+09        735.0   \n",
+              "87                          NaN       56054.920  4.202431e+11          NaN   \n",
+              "\n",
+              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
+              "27       123780.0    4797948.0       22572.268255       18808.224105   \n",
+              "97        35497.0    5214766.0       86248.897403        4490.684602   \n",
+              "40        11344.0    2458762.0      128621.841287       21695.671982   \n",
+              "198       41514.0   13447568.0      198090.434075        4988.892850   \n",
+              "177       29194.0    6416533.0      137238.001939       10256.790198   \n",
+              "3            53.0          NaN                NaN       15518.022390   \n",
+              "18         9898.0    2281853.0      196887.713967        7404.645676   \n",
+              "150       29259.0     584232.0       17719.117092       20121.318048   \n",
+              "162          42.0          NaN                NaN       21657.139490   \n",
+              "87            NaN     780410.0      104096.471810                NaN   \n",
+              "\n",
+              "     deaths_per_million  \n",
+              "27           582.331314  \n",
+              "97           587.097697  \n",
+              "40           593.423100  \n",
+              "198          611.525168  \n",
+              "177          624.406705  \n",
+              "3            685.950948  \n",
+              "18           854.040375  \n",
+              "150          887.393445  \n",
+              "162         1237.550828  \n",
+              "87                  NaN  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f45140e0-3905-49a5-8a41-36d318f8c3b6\" class=\"colab-df-container\">\n",
+              "    <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>location</th>\n",
+              "      <th>continent</th>\n",
+              "      <th>population</th>\n",
+              "      <th>life_expectancy</th>\n",
+              "      <th>hospital_beds_per_thousand</th>\n",
+              "      <th>gdp_per_capita</th>\n",
+              "      <th>gdp</th>\n",
+              "      <th>total_cases</th>\n",
+              "      <th>total_deaths</th>\n",
+              "      <th>total_tests</th>\n",
+              "      <th>tests_per_million</th>\n",
+              "      <th>cases_per_million</th>\n",
+              "      <th>deaths_per_million</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Brazil</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>212559409</td>\n",
+              "      <td>75.88</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>14103.452</td>\n",
+              "      <td>2.997821e+12</td>\n",
+              "      <td>3997865.0</td>\n",
+              "      <td>123780.0</td>\n",
+              "      <td>4797948.0</td>\n",
+              "      <td>22572.268255</td>\n",
+              "      <td>18808.224105</td>\n",
+              "      <td>582.331314</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>Italy</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>60461828</td>\n",
+              "      <td>83.51</td>\n",
+              "      <td>3.18</td>\n",
+              "      <td>35220.084</td>\n",
+              "      <td>2.129471e+12</td>\n",
+              "      <td>271515.0</td>\n",
+              "      <td>35497.0</td>\n",
+              "      <td>5214766.0</td>\n",
+              "      <td>86248.897403</td>\n",
+              "      <td>4490.684602</td>\n",
+              "      <td>587.097697</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>Chile</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>19116209</td>\n",
+              "      <td>80.18</td>\n",
+              "      <td>2.11</td>\n",
+              "      <td>22767.037</td>\n",
+              "      <td>4.352194e+11</td>\n",
+              "      <td>414739.0</td>\n",
+              "      <td>11344.0</td>\n",
+              "      <td>2458762.0</td>\n",
+              "      <td>128621.841287</td>\n",
+              "      <td>21695.671982</td>\n",
+              "      <td>593.423100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>198</th>\n",
+              "      <td>United Kingdom</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>67886004</td>\n",
+              "      <td>81.32</td>\n",
+              "      <td>2.54</td>\n",
+              "      <td>39753.244</td>\n",
+              "      <td>2.698689e+12</td>\n",
+              "      <td>338676.0</td>\n",
+              "      <td>41514.0</td>\n",
+              "      <td>13447568.0</td>\n",
+              "      <td>198090.434075</td>\n",
+              "      <td>4988.892850</td>\n",
+              "      <td>611.525168</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>177</th>\n",
+              "      <td>Spain</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>46754783</td>\n",
+              "      <td>83.56</td>\n",
+              "      <td>2.97</td>\n",
+              "      <td>34272.360</td>\n",
+              "      <td>1.602397e+12</td>\n",
+              "      <td>479554.0</td>\n",
+              "      <td>29194.0</td>\n",
+              "      <td>6416533.0</td>\n",
+              "      <td>137238.001939</td>\n",
+              "      <td>10256.790198</td>\n",
+              "      <td>624.406705</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Andorra</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>77265</td>\n",
+              "      <td>83.73</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1199.0</td>\n",
+              "      <td>53.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>15518.022390</td>\n",
+              "      <td>685.950948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Belgium</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>11589616</td>\n",
+              "      <td>81.63</td>\n",
+              "      <td>5.64</td>\n",
+              "      <td>42658.576</td>\n",
+              "      <td>4.943965e+11</td>\n",
+              "      <td>85817.0</td>\n",
+              "      <td>9898.0</td>\n",
+              "      <td>2281853.0</td>\n",
+              "      <td>196887.713967</td>\n",
+              "      <td>7404.645676</td>\n",
+              "      <td>854.040375</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>150</th>\n",
+              "      <td>Peru</td>\n",
+              "      <td>South America</td>\n",
+              "      <td>32971846</td>\n",
+              "      <td>76.74</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>12236.706</td>\n",
+              "      <td>4.034668e+11</td>\n",
+              "      <td>663437.0</td>\n",
+              "      <td>29259.0</td>\n",
+              "      <td>584232.0</td>\n",
+              "      <td>17719.117092</td>\n",
+              "      <td>20121.318048</td>\n",
+              "      <td>887.393445</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>162</th>\n",
+              "      <td>San Marino</td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>33938</td>\n",
+              "      <td>84.97</td>\n",
+              "      <td>3.80</td>\n",
+              "      <td>56861.470</td>\n",
+              "      <td>1.929765e+09</td>\n",
+              "      <td>735.0</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>21657.139490</td>\n",
+              "      <td>1237.550828</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>Hong Kong</td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>7496988</td>\n",
+              "      <td>84.86</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>56054.920</td>\n",
+              "      <td>4.202431e+11</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>780410.0</td>\n",
+              "      <td>104096.471810</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f45140e0-3905-49a5-8a41-36d318f8c3b6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f45140e0-3905-49a5-8a41-36d318f8c3b6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f45140e0-3905-49a5-8a41-36d318f8c3b6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-11b1d70b-b997-454d-8cca-9457b5432ec6\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11b1d70b-b997-454d-8cca-9457b5432ec6')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-11b1d70b-b997-454d-8cca-9457b5432ec6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_d12daa06-1f8c-498f-86c9-1d9f1353df5d\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('highest_deaths_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d12daa06-1f8c-498f-86c9-1d9f1353df5d button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('highest_deaths_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "highest_deaths_df",
+              "summary": "{\n  \"name\": \"highest_deaths_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"San Marino\",\n          \"Italy\",\n          \"Andorra\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"South America\",\n          \"Europe\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63431013,\n        \"min\": 33938,\n        \"max\": 212559409,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          33938,\n          60461828,\n          77265\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.204690312651133,\n        \"min\": 75.88,\n        \"max\": 84.97,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.97,\n          83.51,\n          83.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2675285288420814,\n        \"min\": 1.6,\n        \"max\": 5.64,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.18,\n          5.64,\n          2.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16230.409234274475,\n        \"min\": 12236.706,\n        \"max\": 56861.47,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          56861.47,\n          35220.084,\n          42658.576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1131831495984.7166,\n        \"min\": 1929764568.8600001,\n        \"max\": 2997821421979.8677,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1929764568.8600001,\n          2129470660953.5522,\n          494396514946.81604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1258751.8810278014,\n        \"min\": 735.0,\n        \"max\": 3997865.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          663437.0,\n          271515.0,\n          1199.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37902.305183853925,\n        \"min\": 42.0,\n        \"max\": 123780.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          29259.0,\n          35497.0,\n          53.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4184787.289485947,\n        \"min\": 584232.0,\n        \"max\": 13447568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5214766.0,\n          2281853.0,\n          4797948.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68806.1028862353,\n        \"min\": 17719.117091593842,\n        \"max\": 198090.43407533606,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          86248.89740349895,\n          196887.7139673998,\n          22572.26825466004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7156.832665095871,\n        \"min\": 4490.684601861525,\n        \"max\": 21695.67198182443,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          20121.318048131125,\n          4490.684601861525,\n          15518.022390474342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 219.31641882952675,\n        \"min\": 582.3313142538893,\n        \"max\": 1237.5508279804349,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          887.3934446982435,\n          587.0976974100089,\n          685.9509480359801\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ],
+      "source": [
+        "highest_deaths_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nUTDc8q4Nage"
+      },
+      "source": [
+        "**(Optional) Q: Count number of countries that feature in both the lists of \"highest number of tests per million\" and \"highest number of cases per million\".**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nXat9wCrNagf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dfe4275b-5628-43b5-8bd3-422fb75e27d1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "4"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 76
+        }
+      ],
+      "source": [
+        "pd.merge(highest_cases_df,highest_deaths_df,on=\"location\").shape[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uHWghQHUNagh"
+      },
+      "source": [
+        "**(Optional) Q: Count number of countries that feature in both the lists \"20 countries with lowest GDP per capita\" and \"20 countries with the lowest number of hospital beds per thousand population\". Only consider countries with a population higher than 10 million while creating the list.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5C7khtW3Nagi"
+      },
+      "outputs": [],
+      "source": [
+        "cc= combined_df[combined_df['population']>100000000]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RKk0r3umNagj"
+      },
+      "outputs": [],
+      "source": [
+        "cc1=combined_df.sort_values(by=\"gdp_per_capita\").head(20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XH7POtliNagj"
+      },
+      "outputs": [],
+      "source": [
+        "cc2=combined_df.sort_values(by=\"hospital_beds_per_thousand\").head(20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.merge(cc1,cc2,on=\"location\").shape[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pReXGGslS9is",
+        "outputId": "1e9d56e3-d09e-437c-caf6-371ebe71d64d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wvjafOzENagk"
+      },
+      "source": [
+        "## Submission\n",
+        "\n",
+        "Congratulations on making it this far! You've reached the end of this assignment, and you just completed your first real-world data analysis problem.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LBOb5DhHNagl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cbac7b96-0022-4e4c-b63d-bcf945c5dd5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Thank you so much\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Thank you so much\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "NX9I-esQTSao"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.6"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From 6ef7225b10bb032329fa412772c6e4b67125e069 Mon Sep 17 00:00:00 2001
From: cgoyal23 <167020791+cgoyal23@users.noreply.github.com>
Date: Sat, 25 May 2024 06:52:27 +0530
Subject: [PATCH 3/3] Update 230335.ipynb

dscdc
---
 230335.ipynb | 762 +--------------------------------------------------
 1 file changed, 1 insertion(+), 761 deletions(-)

diff --git a/230335.ipynb b/230335.ipynb
index d92281b..8b13789 100644
--- a/230335.ipynb
+++ b/230335.ipynb
@@ -1,761 +1 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/cgoyal23/Customer_Churn_Analysis/blob/main/230335.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3eJmduQENaek"
-      },
-      "source": [
-        "# Assignment 1.1 - Pandas Data Analysis Practice\n",
-        "\n",
-        "*This assignment is a part of the project [\"Customer Churn Analysis: From Data To Strategy\"]*\n",
-        "\n",
-        "In this assignment, you'll get to practice some of the concepts and skills covered in pandas.\n",
-        "\n",
-        "As you go through this notebook, you will find a **???** in certain places. To complete this assignment, you must replace all the **???** with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.\n",
-        "\n",
-        "Some things to keep in mind:\n",
-        "\n",
-        "* Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.\n",
-        "* In some cases, you may need to add some code cells or new statements before or after the line of code containing the **???**.\n",
-        "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n",
-        "\n",
-        "\n",
-        "If you are stuck, you can ask for help on the Whatsapp group. Please do not use **ChatGPT or any other LLM to get solutions to the questions.**\n",
-        "\n",
-        "\n",
-        "Deadline : **22 May, 11:59 pm**\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "nzCoPd_zNae4"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yKwMduIXNae5"
-      },
-      "source": [
-        "In this assignment, we're going to analyze an operate on data from a CSV file. Let's begin by downloading the CSV file."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Xa18DHWvNae8"
-      },
-      "source": [
-        "Let's load the data from the CSV file into a Pandas data frame."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "2QPUhz19Nae-",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 287
-        },
-        "outputId": "9aa4e1b9-a78a-4ab6-f09c-3ad9fd4bb91d"
-      },
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "FileNotFoundError",
-          "evalue": "[Errno 2] No such file or directory: 'countries.csv'",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-2-4107ec6dae3f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountries_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'countries.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'countries.csv'"
-          ]
-        }
-      ],
-      "source": [
-        "countries_df = pd.read_csv('countries.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7_kR3k1wNae_"
-      },
-      "outputs": [],
-      "source": [
-        "countries_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KE8FlOjINafA"
-      },
-      "source": [
-        "**Q1: How many countries does the dataframe contain?**\n",
-        "\n",
-        "Hint: Use the `.shape` method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ZD4-AM-CNafB"
-      },
-      "outputs": [],
-      "source": [
-        "num_countries = countries_df.shape[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Iy8SMfkwNafC"
-      },
-      "outputs": [],
-      "source": [
-        "print('There are {} countries in the dataset'.format(num_countries))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "m9eCXKMCNafE"
-      },
-      "source": [
-        "**Q2: Retrieve a list of continents from the dataframe?**\n",
-        "\n",
-        "*Hint: Use the `.unique` method of a series.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "xL8zFetJNafF"
-      },
-      "outputs": [],
-      "source": [
-        "continents = countries_df['continent'].unique()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "40j7HkWZNafG"
-      },
-      "outputs": [],
-      "source": [
-        "continents"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yr9rdD5ZNafK"
-      },
-      "source": [
-        "**Q3: What is the total population of all the countries listed in this dataset?**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IrGeG0vwNafL"
-      },
-      "outputs": [],
-      "source": [
-        "total_population = countries_df['population'].sum()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "FUJRPPMcNafM"
-      },
-      "outputs": [],
-      "source": [
-        "print('The total population is {}.'.format(int(total_population)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nvs9htXuNafO"
-      },
-      "source": [
-        "**Q: (Optional) What is the overall life expectancy across in the world?**\n",
-        "\n",
-        "*Hint: You'll need to take a weighted average of life expectancy using populations as weights.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8TXTdZ0ENafP"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "97C4RlcPNafP"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "H1H_0zo8NafR"
-      },
-      "source": [
-        "**Q4: Create a dataframe containing 10 countries with the highest population.**\n",
-        "\n",
-        "*Hint: Chain the `sort_values` and `head` methods.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3BBebkbyNafR"
-      },
-      "outputs": [],
-      "source": [
-        "most_populous_df = countries_df.sort_values('population', ascending=False).head(10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "L83PYtCaNafS"
-      },
-      "outputs": [],
-      "source": [
-        "most_populous_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RldAR4WJNafT"
-      },
-      "source": [
-        "**Q5: Add a new column in `countries_df` to record the overall GDP per country (product of population & per capita GDP).**\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "R_mdT6GMNafU"
-      },
-      "outputs": [],
-      "source": [
-        "countries_df['gdp'] = countries_df['population'] * countries_df['gdp_per_capita']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "FLydP8pHNafV"
-      },
-      "outputs": [],
-      "source": [
-        "countries_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RXtXfNfBNafX"
-      },
-      "source": [
-        "**Q: (Optional) Create a dataframe containing 10 countries with the lowest GDP per capita, among the counties with population greater than 100 million.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EkCt9Wl7NafX"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nM5flE-QNafY"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_NA6oQnVNafZ"
-      },
-      "source": [
-        "**Q6: Create a data frame that counts the number countries in each continent?**\n",
-        "\n",
-        "*Hint: Use `groupby`, select the `location` column and aggregate using `count`.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Co-tfE7BNafv"
-      },
-      "outputs": [],
-      "source": [
-        "country_counts_df = countries_df.groupby('continent')['location'].count()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6fVwjKFFNafw"
-      },
-      "outputs": [],
-      "source": [
-        "country_counts_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Ut6cNp0yNafy"
-      },
-      "source": [
-        "**Q7: Create a data frame showing the total population of each continent.**\n",
-        "\n",
-        "*Hint: Use `groupby`, select the population column and aggregate using `sum`.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Hqnwb5V0Nafy"
-      },
-      "outputs": [],
-      "source": [
-        "continent_populations_df = countries_df.groupby('continent')['population'].sum()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D5gAu5IANafz"
-      },
-      "outputs": [],
-      "source": [
-        "continent_populations_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Am9h8-R4Naf1"
-      },
-      "source": [
-        "Let's download another CSV file containing overall Covid-19 stats for various countires, and read the data into another Pandas data frame."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sOybiAeVNaf6"
-      },
-      "outputs": [],
-      "source": [
-        "covid_data_df = pd.read_csv('covid-countries-data.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "scrolled": true,
-        "id": "k9PuPoYUNaf7"
-      },
-      "outputs": [],
-      "source": [
-        "covid_data_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "weKZowz0Naf8"
-      },
-      "source": [
-        "**Q8: Count the number of countries for which the `total_tests` data is missing.**\n",
-        "\n",
-        "*Hint: Use the `.isna` method.*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "UybZmvdFNaf9"
-      },
-      "outputs": [],
-      "source": [
-        "total_tests_missing = covid_data_df['total_tests'].isna().sum()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0_jz32NxNaf-"
-      },
-      "outputs": [],
-      "source": [
-        "print(\"The data for total tests is missing for {} countries.\".format(int(total_tests_missing)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bK9yyH3GNagA"
-      },
-      "source": [
-        "Let's merge the two data frames, and compute some more metrics.\n",
-        "\n",
-        "**Q9: Merge `countries_df` with `covid_data_df` on the `location` column.**\n",
-        "\n",
-        "*Hint: Use the `.merge` method on `countries_df`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6qu0tGtJNagB"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df = countries_df.merge(covid_data_df, on='location')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "yNR9WKaENagC"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DMFIKAXvNagG"
-      },
-      "source": [
-        "**Q10: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "_KdWa_juNagH"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IDdn-5CONagI"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "93n1py5BNagK"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df['deaths_per_million'] = combined_df['total_deaths'] * 1e6 / combined_df['population']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IRMupBBUNagL"
-      },
-      "outputs": [],
-      "source": [
-        "combined_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VNjlGq-uNagO"
-      },
-      "source": [
-        "**Q11: Create a dataframe with 10 countires that have highest number of tests per million people.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "HBcHEMJZNagO"
-      },
-      "outputs": [],
-      "source": [
-        "highest_tests_df = combined_df.sort_values('tests_per_million', ascending=False).head(10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "R57UrLhxNagR"
-      },
-      "outputs": [],
-      "source": [
-        "highest_tests_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ug-VTfalNagT"
-      },
-      "source": [
-        "**Q12: Create a dataframe with 10 countires that have highest number of positive cases per million people.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "HxjUJq8oNagU"
-      },
-      "outputs": [],
-      "source": [
-        "highest_cases_df = ???"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7EuO7nPENagY"
-      },
-      "outputs": [],
-      "source": [
-        "highest_cases_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GX-WdUFHNagb"
-      },
-      "source": [
-        "**Q13: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "hH19qct0Nagc"
-      },
-      "outputs": [],
-      "source": [
-        "highest_deaths_df = ???"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lWhydo7ENagd"
-      },
-      "outputs": [],
-      "source": [
-        "highest_deaths_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nUTDc8q4Nage"
-      },
-      "source": [
-        "**(Optional) Q: Count number of countries that feature in both the lists of \"highest number of tests per million\" and \"highest number of cases per million\".**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "nXat9wCrNagf"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Z7y8D9OtNagg"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "aJXjCbJnNagg"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uHWghQHUNagh"
-      },
-      "source": [
-        "**(Optional) Q: Count number of countries that feature in both the lists \"20 countries with lowest GDP per capita\" and \"20 countries with the lowest number of hospital beds per thousand population\". Only consider countries with a population higher than 10 million while creating the list.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "5C7khtW3Nagi"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "RKk0r3umNagj"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "XH7POtliNagj"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wvjafOzENagk"
-      },
-      "source": [
-        "## Submission\n",
-        "\n",
-        "Congratulations on making it this far! You've reached the end of this assignment, and you just completed your first real-world data analysis problem.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "LBOb5DhHNagl"
-      },
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.6"
-    },
-    "colab": {
-      "provenance": [],
-      "include_colab_link": true
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
+