Exploratory_Data_Analysis/Global_population_and_covid_analysis.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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    "colab": {
      "name": "pandas-practice-assignment.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHSvE1uMBvRy"
      },
      "source": [
        "# Jovian Commit Essentials\n",
        "# Please retain and execute this cell without modifying the contents for `jovian.commit` to work\n",
        "!pip install jovian --upgrade -q\n",
        "import jovian\n",
        "jovian.set_project('pandas-practice-assignment')\n",
        "jovian.set_colab_id('1EMzM1GAuekn6b3mjbgjC83UH-2XgQHAe')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vk7bn66zBvR3"
      },
      "source": [
        "# Assignment 3 - Pandas Data Analysis Practice\n",
        "\n",
        "*This assignment is a part of the course [\"Data Analysis with Python: Zero to Pandas\"](https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas)*\n",
        "\n",
        "In this assignment, you'll get to practice some of the concepts and skills covered this tutorial: https://jovian.ml/aakashns/python-pandas-data-analysis\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",
        "* Make sure to run all the code cells, otherwise you may get errors like `NameError` for undefined variables.\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",
        "* Since you'll be using a temporary online service for code execution, save your work by running `jovian.commit` at regular intervals.\n",
        "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n",
        "\n",
        "You can make submissions on this page: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-3-pandas-practice\n",
        "\n",
        "If you are stuck, you can ask for help on the community forum: https://jovian.ml/forum/t/assignment-3-pandas-practice/11225/3 . You can get help with errors or ask for hints, describe your approach in simple words, link to documentation, but **please don't ask for or share the full working answer code** on the forum.\n",
        "\n",
        "\n",
        "## How to run the code and save your work\n",
        "\n",
        "The recommended way to run this notebook is to click the \"Run\" button at the top of this page, and select \"Run on Binder\". This will run the notebook on [mybinder.org](https://mybinder.org), a free online service for running Jupyter notebooks. \n",
        "\n",
        "Before staring the assignment, let's save a snapshot of the assignment to your Jovian.ml profile, so that you can access it later, and continue your work."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPtDOpNPBvR4"
      },
      "source": [
        "import jovian"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
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        "id": "Gna3e906BvR4",
        "outputId": "1d16e13b-cd7f-4762-a89e-b8449fd7cdc8"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8od0L2BvR4",
        "outputId": "701b6cc1-7aff-4a1a-eacf-5fc4b7c2797e"
      },
      "source": [
        "# Run the next line to install Pandas\n",
        "!pip install pandas --upgrade"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: pandas in /usr/local/lib/python3.7/dist-packages (1.2.3)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntV2COQ7BvR5"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qMYSjxi3BvR5"
      },
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJsrb2AZBvR5",
        "outputId": "0f887c59-e9c8-481e-b3ca-44d6ae9a366a"
      },
      "source": [
        "from urllib.request import urlretrieve\n",
        "\n",
        "urlretrieve('https://hub.jovian.ml/wp-content/uploads/2020/09/countries.csv', \n",
        "            'countries.csv')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('countries.csv', <http.client.HTTPMessage at 0x7fc264b60850>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wOb8iNuBvR5"
      },
      "source": [
        "Let's load the data from the CSV file into a Pandas data frame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRfOgIcgBvR5"
      },
      "source": [
        "countries_df = pd.read_csv('countries.csv')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "-DMUkM5IBvR6",
        "outputId": "126c435c-f5ab-4e28-fc39-f1fbc019533b"
      },
      "source": [
        "countries_df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>38928341.0</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>2877800.0</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>43851043.0</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>77265.0</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>32866268.0</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>97338583.0</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>597330.0</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>29825968.0</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>18383956.0</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>14862927.0</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>"
            ],
            "text/plain": [
              "           location continent  ...  hospital_beds_per_thousand  gdp_per_capita\n",
              "0       Afghanistan      Asia  ...                        0.50        1803.987\n",
              "1           Albania    Europe  ...                        2.89       11803.431\n",
              "2           Algeria    Africa  ...                        1.90       13913.839\n",
              "3           Andorra    Europe  ...                         NaN             NaN\n",
              "4            Angola    Africa  ...                         NaN        5819.495\n",
              "..              ...       ...  ...                         ...             ...\n",
              "205         Vietnam      Asia  ...                        2.60        6171.884\n",
              "206  Western Sahara    Africa  ...                         NaN             NaN\n",
              "207           Yemen      Asia  ...                        0.70        1479.147\n",
              "208          Zambia    Africa  ...                        2.00        3689.251\n",
              "209        Zimbabwe    Africa  ...                        1.70        1899.775\n",
              "\n",
              "[210 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0RqcpL7BvR6"
      },
      "source": [
        "**Q: How many countries does the dataframe contain?**\n",
        "\n",
        "Hint: Use the `.shape` method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjqdPOO_BvR6"
      },
      "source": [
        "num_countries = countries_df.shape[0]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAbx-nYZBvR6",
        "outputId": "a8488ef8-40f7-42b8-e485-e9e120efeb37"
      },
      "source": [
        "print('There are {} countries in the dataset'.format(num_countries))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 210 countries in the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
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        "id": "P4AeGLJxBvR6",
        "outputId": "a006c507-0b24-453a-8fc7-99c3e01d129d"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwGcXUP5BvR7"
      },
      "source": [
        "**Q: Retrieve a list of continents from the dataframe?**\n",
        "\n",
        "*Hint: Use the `.unique` method of a series.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HK1sN9mNBvR7"
      },
      "source": [
        "continents = countries_df[\"continent\"].unique()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4gNDrhGBvR7",
        "outputId": "3d4597a3-9e40-4d5a-f12d-756ac34362b8"
      },
      "source": [
        "continents"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Asia', 'Europe', 'Africa', 'North America', 'South America',\n",
              "       'Oceania'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 88
        },
        "id": "LOAJcItxBvR7",
        "outputId": "694ef676-bb69-405b-df1e-bedf8de7fcf1"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
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              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
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          "metadata": {
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          "execution_count": 17
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVvhml6vBvR7"
      },
      "source": [
        "**Q: What is the total population of all the countries listed in this dataset?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLOlu0h4BvR7"
      },
      "source": [
        "total_population = countries_df[\"population\"].sum()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n98wZgKXBvR8",
        "outputId": "0bb4f0c5-4af2-463b-d1f2-16e4ebd45a50"
      },
      "source": [
        "print('The total population is {}.'.format(int(total_population)))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The total population is 7757980095.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "jovian.commit(project='pandas-practice-assignment', environment=None)"
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      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5nFlNudBvR8"
      },
      "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",
      "metadata": {
        "id": "YBNIwfnrBvR8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoRI7f3IBvR8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJEX8HRlBvR8"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2S70jlPBvR9"
      },
      "source": [
        "**Q: Create a dataframe containing 10 countries with the highest population.**\n",
        "\n",
        "*Hint: Chain the `sort_values` and `head` methods.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQH0yZhTBvR9"
      },
      "source": [
        "most_populous_df = countries_df.sort_values(\"population\", ascending=False)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "M7yHFiERBvR9",
        "outputId": "5f69eea6-d200-4eda-ed9e-02a2f4c744bc"
      },
      "source": [
        "most_populous_df"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>41</th>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.439324e+09</td>\n",
              "      <td>76.91</td>\n",
              "      <td>4.34</td>\n",
              "      <td>15308.712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.380004e+09</td>\n",
              "      <td>69.66</td>\n",
              "      <td>0.53</td>\n",
              "      <td>6426.674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>United States</td>\n",
              "      <td>North America</td>\n",
              "      <td>3.310026e+08</td>\n",
              "      <td>78.86</td>\n",
              "      <td>2.77</td>\n",
              "      <td>54225.446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.735236e+08</td>\n",
              "      <td>71.72</td>\n",
              "      <td>1.04</td>\n",
              "      <td>11188.744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.208923e+08</td>\n",
              "      <td>67.27</td>\n",
              "      <td>0.60</td>\n",
              "      <td>5034.708</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>24</th>\n",
              "      <td>Bonaire Sint Eustatius and Saba</td>\n",
              "      <td>North America</td>\n",
              "      <td>2.622100e+04</td>\n",
              "      <td>77.79</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Anguilla</td>\n",
              "      <td>North America</td>\n",
              "      <td>1.500200e+04</td>\n",
              "      <td>81.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Montserrat</td>\n",
              "      <td>North America</td>\n",
              "      <td>4.999000e+03</td>\n",
              "      <td>74.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Falkland Islands</td>\n",
              "      <td>South America</td>\n",
              "      <td>3.483000e+03</td>\n",
              "      <td>81.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>Vatican</td>\n",
              "      <td>Europe</td>\n",
              "      <td>8.090000e+02</td>\n",
              "      <td>75.12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>210 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            location  ... gdp_per_capita\n",
              "41                             China  ...      15308.712\n",
              "90                             India  ...       6426.674\n",
              "199                    United States  ...      54225.446\n",
              "91                         Indonesia  ...      11188.744\n",
              "145                         Pakistan  ...       5034.708\n",
              "..                               ...  ...            ...\n",
              "24   Bonaire Sint Eustatius and Saba  ...            NaN\n",
              "5                           Anguilla  ...            NaN\n",
              "130                       Montserrat  ...            NaN\n",
              "65                  Falkland Islands  ...            NaN\n",
              "203                          Vatican  ...            NaN\n",
              "\n",
              "[210 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "vn4WvdFIBvR9",
        "outputId": "289e2de7-164f-4e2e-90ba-710d830ed984"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKl_4j9jBvR9"
      },
      "source": [
        "**Q: 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",
      "metadata": {
        "id": "vDPb04daBvR9"
      },
      "source": [
        "countries_df['gdp'] = countries_df[\"population\"]*countries_df[\"gdp_per_capita\"]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "drzStT6IBvR9",
        "outputId": "a2251e18-cc90-4400-9908-2936780a684d"
      },
      "source": [
        "countries_df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>38928341.0</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>2877800.0</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>43851043.0</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>77265.0</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>32866268.0</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>97338583.0</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>597330.0</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>29825968.0</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>18383956.0</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>14862927.0</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>"
            ],
            "text/plain": [
              "           location continent  ...  gdp_per_capita           gdp\n",
              "0       Afghanistan      Asia  ...        1803.987  7.022622e+10\n",
              "1           Albania    Europe  ...       11803.431  3.396791e+10\n",
              "2           Algeria    Africa  ...       13913.839  6.101364e+11\n",
              "3           Andorra    Europe  ...             NaN           NaN\n",
              "4            Angola    Africa  ...        5819.495  1.912651e+11\n",
              "..              ...       ...  ...             ...           ...\n",
              "205         Vietnam      Asia  ...        6171.884  6.007624e+11\n",
              "206  Western Sahara    Africa  ...             NaN           NaN\n",
              "207           Yemen      Asia  ...        1479.147  4.411699e+10\n",
              "208          Zambia    Africa  ...        3689.251  6.782303e+10\n",
              "209        Zimbabwe    Africa  ...        1899.775  2.823622e+10\n",
              "\n",
              "[210 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "0EC13ibOBvR-",
        "outputId": "e3ca0f1d-a4f1-4949-a570-e76d8f45e92e"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tatHWc-hBvR-"
      },
      "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",
      "metadata": {
        "id": "V08nbWkXBvR-"
      },
      "source": [
        "lowest_gdp_df = countries_df[countries_df[\"population\"] > 100000000].sort_values(\"gdp_per_capita\").head(10).reset_index()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "1tI0HnTkBvR-",
        "outputId": "201653fe-decf-4bc9-d766-68a8bcd2b8cb"
      },
      "source": [
        "lowest_gdp_df"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>0</th>\n",
              "      <td>63</td>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.149636e+08</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>1</th>\n",
              "      <td>15</td>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.646894e+08</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>2</th>\n",
              "      <td>145</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.208923e+08</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>3</th>\n",
              "      <td>141</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2.061396e+08</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>4</th>\n",
              "      <td>90</td>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.380004e+09</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>5</th>\n",
              "      <td>151</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.095811e+08</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>6</th>\n",
              "      <td>58</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.023344e+08</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>7</th>\n",
              "      <td>91</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.735236e+08</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>8</th>\n",
              "      <td>27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>South America</td>\n",
              "      <td>2.125594e+08</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>9</th>\n",
              "      <td>41</td>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.439324e+09</td>\n",
              "      <td>76.91</td>\n",
              "      <td>4.34</td>\n",
              "      <td>15308.712</td>\n",
              "      <td>2.203419e+13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index     location  ... gdp_per_capita           gdp\n",
              "0     63     Ethiopia  ...       1729.927  1.988786e+11\n",
              "1     15   Bangladesh  ...       3523.984  5.803628e+11\n",
              "2    145     Pakistan  ...       5034.708  1.112128e+12\n",
              "3    141      Nigeria  ...       5338.454  1.100467e+12\n",
              "4     90        India  ...       6426.674  8.868838e+12\n",
              "5    151  Philippines  ...       7599.188  8.327273e+11\n",
              "6     58        Egypt  ...      10550.206  1.079649e+12\n",
              "7     91    Indonesia  ...      11188.744  3.060386e+12\n",
              "8     27       Brazil  ...      14103.452  2.997821e+12\n",
              "9     41        China  ...      15308.712  2.203419e+13\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "yEh0teREBvR-",
        "outputId": "394dbfce-34d9-498b-d608-5cd1214677b1"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvBCxewlBvR-"
      },
      "source": [
        "**Q: 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",
      "metadata": {
        "id": "e7ajY16XBvR-"
      },
      "source": [
        "country_counts_df = countries_df.groupby(\"continent\")[\"location\"].count()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cM0DZGwBvR_",
        "outputId": "d690440d-eaeb-4fdd-e223-bbf529d53c26"
      },
      "source": [
        "country_counts_df"
      ],
      "execution_count": 36,
      "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: location, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "pIQ_AFRvBvR_",
        "outputId": "b7c2db5b-8930-4abb-e162-5037be7e29a9"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1-n_KMgBvR_"
      },
      "source": [
        "**Q: 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",
      "metadata": {
        "id": "x-HIRODnBvR_"
      },
      "source": [
        "continent_populations_df = countries_df.groupby(\"continent\")[\"population\"].sum()"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8n3g8cuBvR_",
        "outputId": "0fd6cf15-3aff-40f9-9e4a-aafe3ce1985c"
      },
      "source": [
        "continent_populations_df"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "continent\n",
              "Africa           1.339424e+09\n",
              "Asia             4.607388e+09\n",
              "Europe           7.485062e+08\n",
              "North America    5.912425e+08\n",
              "Oceania          4.095832e+07\n",
              "South America    4.304611e+08\n",
              "Name: population, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "a1yfcN_qBvR_",
        "outputId": "72d2df14-b624-4462-f21f-fdf9f39b77e0"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJPUn6V8BvR_"
      },
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kxhSiEeBvSA",
        "outputId": "18c62700-6f69-4507-996b-a7bc761af6b9"
      },
      "source": [
        "urlretrieve('https://hub.jovian.ml/wp-content/uploads/2020/09/covid-countries-data.csv', \n",
        "            'covid-countries-data.csv')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('covid-countries-data.csv', <http.client.HTTPMessage at 0x7fc25eb216d0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OgRc0eOBvSA"
      },
      "source": [
        "covid_data_df = pd.read_csv('covid-countries-data.csv')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "lFhmqEXJBvSA",
        "outputId": "ae48e723-3a79-4183-bc75-61eda6452987"
      },
      "source": [
        "covid_data_df"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7_AYncdBvSA"
      },
      "source": [
        "**Q: Count the number of countries for which the `total_tests` data is missing.**\n",
        "\n",
        "*Hint: Use the `.isna` method.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vd3kv43RBvSA"
      },
      "source": [
        "total_tests_missing = covid_data_df[covid_data_df[\"total_tests\"].isna()][\"location\"].count()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLsHzUwsBvSA",
        "outputId": "25ad1f9b-11f9-4e7b-b68f-ab299035b685"
      },
      "source": [
        "print(\"The data for total tests is missing for {} countries.\".format(int(total_tests_missing)))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The data for total tests is missing for 122 countries.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "xrr7PxcBBvSA",
        "outputId": "65cec8c6-5a38-4ef7-f05c-9d95cab8df1b"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdXWS1xZBvSB"
      },
      "source": [
        "Let's merge the two data frames, and compute some more metrics.\n",
        "\n",
        "**Q: Merge `countries_df` with `covid_data_df` on the `location` column.**\n",
        "\n",
        "*Hint: Use the `.merge` method on `countries_df`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FLKPMPNBvSB"
      },
      "source": [
        "combined_df = countries_df.merge(covid_data_df, on=\"location\")"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "v7_iYSmeBvSB",
        "outputId": "8994e5a3-ed57-4289-82d8-043204ea889d"
      },
      "source": [
        "combined_df"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>38928341.0</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>2877800.0</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>43851043.0</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>77265.0</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>32866268.0</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>97338583.0</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>597330.0</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>29825968.0</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>18383956.0</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>14862927.0</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>"
            ],
            "text/plain": [
              "           location continent  ...  total_deaths  total_tests\n",
              "0       Afghanistan      Asia  ...        1409.0          NaN\n",
              "1           Albania    Europe  ...         296.0          NaN\n",
              "2           Algeria    Africa  ...        1525.0          NaN\n",
              "3           Andorra    Europe  ...          53.0          NaN\n",
              "4            Angola    Africa  ...         109.0          NaN\n",
              "..              ...       ...  ...           ...          ...\n",
              "205         Vietnam      Asia  ...          35.0     261004.0\n",
              "206  Western Sahara    Africa  ...           1.0          NaN\n",
              "207           Yemen      Asia  ...         571.0          NaN\n",
              "208          Zambia    Africa  ...         292.0          NaN\n",
              "209        Zimbabwe    Africa  ...         206.0      97272.0\n",
              "\n",
              "[210 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "S9n67gDwBvSB",
        "outputId": "34e3579a-7ef8-43f3-a3e2-5c4df0aee9e0"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Nsynno3BvSB"
      },
      "source": [
        "**Q: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlWMNXxIBvSB"
      },
      "source": [
        "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToFZzzuqBvSB"
      },
      "source": [
        "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqJWHDtiBvSC"
      },
      "source": [
        "combined_df['deaths_per_million'] = combined_df['total_deaths'] * 1e6 / combined_df['population']"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "UiHXcZg7BvSC",
        "outputId": "15760092-5786-457b-8b53-1f221fd3c6fb"
      },
      "source": [
        "combined_df"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>38928341.0</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>2877800.0</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>43851043.0</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>77265.0</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>32866268.0</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>97338583.0</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>597330.0</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>29825968.0</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>18383956.0</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>14862927.0</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>"
            ],
            "text/plain": [
              "           location continent  ...  cases_per_million  deaths_per_million\n",
              "0       Afghanistan      Asia  ...         982.394806           36.194710\n",
              "1           Albania    Europe  ...        3380.359997          102.856349\n",
              "2           Algeria    Africa  ...        1029.804468           34.776824\n",
              "3           Andorra    Europe  ...       15518.022390          685.950948\n",
              "4            Angola    Africa  ...          83.033462            3.316470\n",
              "..              ...       ...  ...                ...                 ...\n",
              "205         Vietnam      Asia  ...          10.745996            0.359570\n",
              "206  Western Sahara    Africa  ...        1282.373228            1.674116\n",
              "207           Yemen      Asia  ...          66.250993           19.144391\n",
              "208          Zambia    Africa  ...         675.317108           15.883415\n",
              "209        Zimbabwe    Africa  ...         446.614587           13.859989\n",
              "\n",
              "[210 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "zp3DIassBvSC",
        "outputId": "e018cad0-81c8-46af-e550-aea7c119a34c"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfrWox3cBvSC"
      },
      "source": [
        "**Q: Create a dataframe with 10 countires that have highest number of tests per million people.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KsF-91ABvSC"
      },
      "source": [
        "highest_tests_df = combined_df.sort_values(\"tests_per_million\",ascending=False).head(10).reset_index()"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "qJfIYaCUBvSC",
        "outputId": "e849f543-7e66-4033-ad17-ac06636b8d8f"
      },
      "source": [
        "highest_tests_df"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>197</td>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>Asia</td>\n",
              "      <td>9890400.0</td>\n",
              "      <td>77.97</td>\n",
              "      <td>1.200</td>\n",
              "      <td>67293.483</td>\n",
              "      <td>6.655595e+11</td>\n",
              "      <td>71540.0</td>\n",
              "      <td>387.0</td>\n",
              "      <td>7177430.0</td>\n",
              "      <td>725696.635121</td>\n",
              "      <td>7233.276713</td>\n",
              "      <td>39.128852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "      <td>Bahrain</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1701583.0</td>\n",
              "      <td>77.29</td>\n",
              "      <td>2.000</td>\n",
              "      <td>43290.705</td>\n",
              "      <td>7.366273e+10</td>\n",
              "      <td>52440.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>1118837.0</td>\n",
              "      <td>657527.137965</td>\n",
              "      <td>30818.361490</td>\n",
              "      <td>111.660730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>115</td>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>Europe</td>\n",
              "      <td>625976.0</td>\n",
              "      <td>82.25</td>\n",
              "      <td>4.510</td>\n",
              "      <td>94277.965</td>\n",
              "      <td>5.901574e+10</td>\n",
              "      <td>7928.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>385820.0</td>\n",
              "      <td>616349.508607</td>\n",
              "      <td>12665.022301</td>\n",
              "      <td>198.090662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>122</td>\n",
              "      <td>Malta</td>\n",
              "      <td>Europe</td>\n",
              "      <td>441539.0</td>\n",
              "      <td>82.53</td>\n",
              "      <td>4.485</td>\n",
              "      <td>36513.323</td>\n",
              "      <td>1.612206e+10</td>\n",
              "      <td>1931.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>188539.0</td>\n",
              "      <td>427004.183096</td>\n",
              "      <td>4373.339614</td>\n",
              "      <td>29.442473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>Europe</td>\n",
              "      <td>5792203.0</td>\n",
              "      <td>80.90</td>\n",
              "      <td>2.500</td>\n",
              "      <td>46682.515</td>\n",
              "      <td>2.703946e+11</td>\n",
              "      <td>17195.0</td>\n",
              "      <td>626.0</td>\n",
              "      <td>2447911.0</td>\n",
              "      <td>422621.755488</td>\n",
              "      <td>2968.645954</td>\n",
              "      <td>108.076323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>96</td>\n",
              "      <td>Israel</td>\n",
              "      <td>Asia</td>\n",
              "      <td>8655541.0</td>\n",
              "      <td>82.97</td>\n",
              "      <td>2.990</td>\n",
              "      <td>33132.320</td>\n",
              "      <td>2.867782e+11</td>\n",
              "      <td>122539.0</td>\n",
              "      <td>969.0</td>\n",
              "      <td>2353984.0</td>\n",
              "      <td>271962.665303</td>\n",
              "      <td>14157.289533</td>\n",
              "      <td>111.951408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>89</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>Europe</td>\n",
              "      <td>341250.0</td>\n",
              "      <td>82.99</td>\n",
              "      <td>2.910</td>\n",
              "      <td>46482.958</td>\n",
              "      <td>1.586231e+10</td>\n",
              "      <td>2121.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>88829.0</td>\n",
              "      <td>260304.761905</td>\n",
              "      <td>6215.384615</td>\n",
              "      <td>29.304029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>157</td>\n",
              "      <td>Russia</td>\n",
              "      <td>Europe</td>\n",
              "      <td>145934460.0</td>\n",
              "      <td>72.58</td>\n",
              "      <td>8.050</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>8</th>\n",
              "      <td>199</td>\n",
              "      <td>United States</td>\n",
              "      <td>North America</td>\n",
              "      <td>331002647.0</td>\n",
              "      <td>78.86</td>\n",
              "      <td>2.770</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>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Australia</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>25499881.0</td>\n",
              "      <td>83.44</td>\n",
              "      <td>3.840</td>\n",
              "      <td>44648.710</td>\n",
              "      <td>1.138537e+12</td>\n",
              "      <td>25923.0</td>\n",
              "      <td>663.0</td>\n",
              "      <td>6255797.0</td>\n",
              "      <td>245326.517406</td>\n",
              "      <td>1016.592979</td>\n",
              "      <td>26.000121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index              location  ... cases_per_million  deaths_per_million\n",
              "0    197  United Arab Emirates  ...       7233.276713           39.128852\n",
              "1     14               Bahrain  ...      30818.361490          111.660730\n",
              "2    115            Luxembourg  ...      12665.022301          198.090662\n",
              "3    122                 Malta  ...       4373.339614           29.442473\n",
              "4     53               Denmark  ...       2968.645954          108.076323\n",
              "5     96                Israel  ...      14157.289533          111.951408\n",
              "6     89               Iceland  ...       6215.384615           29.304029\n",
              "7    157                Russia  ...       6886.653091          119.327539\n",
              "8    199         United States  ...      18472.377957          561.155633\n",
              "9     10             Australia  ...       1016.592979           26.000121\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "uUNxcTFqBvSC",
        "outputId": "e45b0230-533d-4621-a59e-035c352d9c17"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnR8euX5BvSD"
      },
      "source": [
        "**Q: Create a dataframe with 10 countires that have highest number of positive cases per million people.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQ2asBcABvSD"
      },
      "source": [
        "highest_cases_df = combined_df.sort_values(\"cases_per_million\",ascending=False).head(10).reset_index()"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "sUlvNaAyBvSD",
        "outputId": "65626f6d-abee-44a2-a38d-f10e59564af9"
      },
      "source": [
        "highest_cases_df"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>155</td>\n",
              "      <td>Qatar</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2881060.0</td>\n",
              "      <td>80.23</td>\n",
              "      <td>1.20</td>\n",
              "      <td>116935.600</td>\n",
              "      <td>3.368985e+11</td>\n",
              "      <td>119206.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>634745.0</td>\n",
              "      <td>220316.480740</td>\n",
              "      <td>41375.743650</td>\n",
              "      <td>69.071800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "      <td>Bahrain</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1701583.0</td>\n",
              "      <td>77.29</td>\n",
              "      <td>2.00</td>\n",
              "      <td>43290.705</td>\n",
              "      <td>7.366273e+10</td>\n",
              "      <td>52440.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>1118837.0</td>\n",
              "      <td>657527.137965</td>\n",
              "      <td>30818.361490</td>\n",
              "      <td>111.660730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>147</td>\n",
              "      <td>Panama</td>\n",
              "      <td>North America</td>\n",
              "      <td>4314768.0</td>\n",
              "      <td>78.51</td>\n",
              "      <td>2.30</td>\n",
              "      <td>22267.037</td>\n",
              "      <td>9.607710e+10</td>\n",
              "      <td>94084.0</td>\n",
              "      <td>2030.0</td>\n",
              "      <td>336345.0</td>\n",
              "      <td>77952.047480</td>\n",
              "      <td>21805.112117</td>\n",
              "      <td>470.477208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>Chile</td>\n",
              "      <td>South America</td>\n",
              "      <td>19116209.0</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>4</th>\n",
              "      <td>162</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>Europe</td>\n",
              "      <td>33938.0</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>5</th>\n",
              "      <td>9</td>\n",
              "      <td>Aruba</td>\n",
              "      <td>North America</td>\n",
              "      <td>106766.0</td>\n",
              "      <td>76.29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35973.781</td>\n",
              "      <td>3.840777e+09</td>\n",
              "      <td>2211.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20708.839893</td>\n",
              "      <td>112.395332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>105</td>\n",
              "      <td>Kuwait</td>\n",
              "      <td>Asia</td>\n",
              "      <td>4270563.0</td>\n",
              "      <td>75.49</td>\n",
              "      <td>2.00</td>\n",
              "      <td>65530.537</td>\n",
              "      <td>2.798523e+11</td>\n",
              "      <td>86478.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>621616.0</td>\n",
              "      <td>145558.325682</td>\n",
              "      <td>20249.789079</td>\n",
              "      <td>125.276222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>150</td>\n",
              "      <td>Peru</td>\n",
              "      <td>South America</td>\n",
              "      <td>32971846.0</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>8</th>\n",
              "      <td>27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>South America</td>\n",
              "      <td>212559409.0</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>9</th>\n",
              "      <td>199</td>\n",
              "      <td>United States</td>\n",
              "      <td>North America</td>\n",
              "      <td>331002647.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index       location  ... cases_per_million  deaths_per_million\n",
              "0    155          Qatar  ...      41375.743650           69.071800\n",
              "1     14        Bahrain  ...      30818.361490          111.660730\n",
              "2    147         Panama  ...      21805.112117          470.477208\n",
              "3     40          Chile  ...      21695.671982          593.423100\n",
              "4    162     San Marino  ...      21657.139490         1237.550828\n",
              "5      9          Aruba  ...      20708.839893          112.395332\n",
              "6    105         Kuwait  ...      20249.789079          125.276222\n",
              "7    150           Peru  ...      20121.318048          887.393445\n",
              "8     27         Brazil  ...      18808.224105          582.331314\n",
              "9    199  United States  ...      18472.377957          561.155633\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "af_QZmthBvSD",
        "outputId": "5980a974-1d50-4e31-b067-70e28a123d8b"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sHmQKAYBvSD"
      },
      "source": [
        "**Q: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wlm_mT8qBvSD"
      },
      "source": [
        "highest_deaths_df = combined_df.sort_values(\"deaths_per_million\",ascending=False).head(10).reset_index()"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "HEVLWUy0BvSD",
        "outputId": "cf1d93a8-ad82-488f-ea9c-eac481c3428b"
      },
      "source": [
        "highest_deaths_df"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>162</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>Europe</td>\n",
              "      <td>33938.0</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>1</th>\n",
              "      <td>150</td>\n",
              "      <td>Peru</td>\n",
              "      <td>South America</td>\n",
              "      <td>32971846.0</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>2</th>\n",
              "      <td>18</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Europe</td>\n",
              "      <td>11589616.0</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>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>Europe</td>\n",
              "      <td>77265.0</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>177</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Europe</td>\n",
              "      <td>46754783.0</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>5</th>\n",
              "      <td>198</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Europe</td>\n",
              "      <td>67886004.0</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>6</th>\n",
              "      <td>40</td>\n",
              "      <td>Chile</td>\n",
              "      <td>South America</td>\n",
              "      <td>19116209.0</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>7</th>\n",
              "      <td>97</td>\n",
              "      <td>Italy</td>\n",
              "      <td>Europe</td>\n",
              "      <td>60461828.0</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>8</th>\n",
              "      <td>27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>South America</td>\n",
              "      <td>212559409.0</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>9</th>\n",
              "      <td>182</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Europe</td>\n",
              "      <td>10099270.0</td>\n",
              "      <td>82.80</td>\n",
              "      <td>2.22</td>\n",
              "      <td>46949.283</td>\n",
              "      <td>4.741535e+11</td>\n",
              "      <td>84532.0</td>\n",
              "      <td>5820.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8370.109919</td>\n",
              "      <td>576.279276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index        location  ... cases_per_million  deaths_per_million\n",
              "0    162      San Marino  ...      21657.139490         1237.550828\n",
              "1    150            Peru  ...      20121.318048          887.393445\n",
              "2     18         Belgium  ...       7404.645676          854.040375\n",
              "3      3         Andorra  ...      15518.022390          685.950948\n",
              "4    177           Spain  ...      10256.790198          624.406705\n",
              "5    198  United Kingdom  ...       4988.892850          611.525168\n",
              "6     40           Chile  ...      21695.671982          593.423100\n",
              "7     97           Italy  ...       4490.684602          587.097697\n",
              "8     27          Brazil  ...      18808.224105          582.331314\n",
              "9    182          Sweden  ...       8370.109919          576.279276\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "WIdAc4StBvSD",
        "outputId": "5e997d23-ad31-4da7-cf4a-ed9649ba9726"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAkfMh-zBvSE"
      },
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JB0TgsEfBvSE",
        "outputId": "f94f9f3b-7aa3-40a1-c1a8-872ed83eafe0"
      },
      "source": [
        "highest_test_and_cases = highest_cases_df[\"location\"].isin(highest_tests_df[\"location\"]).sum()\r\n",
        "print(f\"Total number of Contries having both highest number of cases and covid tests are: {highest_test_and_cases}\")"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of Contries having both highest number of cases and covid tests are: 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "y4rKxPfbBvSF",
        "outputId": "3de092c6-78e3-459e-d447-9e57a0d990ef"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wezSTYDJBvSF"
      },
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "fLmLbjE0BvSF",
        "outputId": "a1be7396-c6c5-4a79-9243-c1a750bbd0ca"
      },
      "source": [
        "lowest_gdp_df =  countries_df[countries_df[\"population\"] > 10000000].sort_values(\"gdp_per_capita\").head(20).reset_index()\r\n",
        "lowest_gdp_df"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>0</th>\n",
              "      <td>32</td>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>11890781.0</td>\n",
              "      <td>61.58</td>\n",
              "      <td>0.8</td>\n",
              "      <td>702.225</td>\n",
              "      <td>8.350004e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52</td>\n",
              "      <td>Democratic Republic of Congo</td>\n",
              "      <td>Africa</td>\n",
              "      <td>89561404.0</td>\n",
              "      <td>60.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808.133</td>\n",
              "      <td>7.237753e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>140</td>\n",
              "      <td>Niger</td>\n",
              "      <td>Africa</td>\n",
              "      <td>24206636.0</td>\n",
              "      <td>62.42</td>\n",
              "      <td>0.3</td>\n",
              "      <td>926.000</td>\n",
              "      <td>2.241534e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>118</td>\n",
              "      <td>Malawi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>19129955.0</td>\n",
              "      <td>64.26</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1095.042</td>\n",
              "      <td>2.094810e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>132</td>\n",
              "      <td>Mozambique</td>\n",
              "      <td>Africa</td>\n",
              "      <td>31255435.0</td>\n",
              "      <td>60.85</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1136.103</td>\n",
              "      <td>3.550939e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>117</td>\n",
              "      <td>Madagascar</td>\n",
              "      <td>Africa</td>\n",
              "      <td>27691019.0</td>\n",
              "      <td>67.04</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1416.440</td>\n",
              "      <td>3.922267e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>207</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>Asia</td>\n",
              "      <td>29825968.0</td>\n",
              "      <td>66.12</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1479.147</td>\n",
              "      <td>4.411699e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>176</td>\n",
              "      <td>South Sudan</td>\n",
              "      <td>Africa</td>\n",
              "      <td>11193729.0</td>\n",
              "      <td>57.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1569.888</td>\n",
              "      <td>1.757290e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>85</td>\n",
              "      <td>Haiti</td>\n",
              "      <td>North America</td>\n",
              "      <td>11402533.0</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1653.173</td>\n",
              "      <td>1.885036e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>195</td>\n",
              "      <td>Uganda</td>\n",
              "      <td>Africa</td>\n",
              "      <td>45741000.0</td>\n",
              "      <td>63.37</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1697.707</td>\n",
              "      <td>7.765482e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>31</td>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>Africa</td>\n",
              "      <td>20903278.0</td>\n",
              "      <td>61.58</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1703.102</td>\n",
              "      <td>3.560041e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>63</td>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>114963583.0</td>\n",
              "      <td>66.60</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1729.927</td>\n",
              "      <td>1.988786e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>39</td>\n",
              "      <td>Chad</td>\n",
              "      <td>Africa</td>\n",
              "      <td>16425859.0</td>\n",
              "      <td>54.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1768.153</td>\n",
              "      <td>2.904343e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>7.022622e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>158</td>\n",
              "      <td>Rwanda</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12952209.0</td>\n",
              "      <td>69.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1854.211</td>\n",
              "      <td>2.401613e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>209</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>14862927.0</td>\n",
              "      <td>61.49</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>2.823622e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>82</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>Africa</td>\n",
              "      <td>13132792.0</td>\n",
              "      <td>61.60</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1998.926</td>\n",
              "      <td>2.625148e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>121</td>\n",
              "      <td>Mali</td>\n",
              "      <td>Africa</td>\n",
              "      <td>20250834.0</td>\n",
              "      <td>59.31</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2014.306</td>\n",
              "      <td>4.079138e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>20</td>\n",
              "      <td>Benin</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12123198.0</td>\n",
              "      <td>61.77</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2064.236</td>\n",
              "      <td>2.502514e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>135</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>Asia</td>\n",
              "      <td>29136808.0</td>\n",
              "      <td>70.78</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2442.804</td>\n",
              "      <td>7.117551e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    index                      location  ... gdp_per_capita           gdp\n",
              "0      32                       Burundi  ...        702.225  8.350004e+09\n",
              "1      52  Democratic Republic of Congo  ...        808.133  7.237753e+10\n",
              "2     140                         Niger  ...        926.000  2.241534e+10\n",
              "3     118                        Malawi  ...       1095.042  2.094810e+10\n",
              "4     132                    Mozambique  ...       1136.103  3.550939e+10\n",
              "5     117                    Madagascar  ...       1416.440  3.922267e+10\n",
              "6     207                         Yemen  ...       1479.147  4.411699e+10\n",
              "7     176                   South Sudan  ...       1569.888  1.757290e+10\n",
              "8      85                         Haiti  ...       1653.173  1.885036e+10\n",
              "9     195                        Uganda  ...       1697.707  7.765482e+10\n",
              "10     31                  Burkina Faso  ...       1703.102  3.560041e+10\n",
              "11     63                      Ethiopia  ...       1729.927  1.988786e+11\n",
              "12     39                          Chad  ...       1768.153  2.904343e+10\n",
              "13      0                   Afghanistan  ...       1803.987  7.022622e+10\n",
              "14    158                        Rwanda  ...       1854.211  2.401613e+10\n",
              "15    209                      Zimbabwe  ...       1899.775  2.823622e+10\n",
              "16     82                        Guinea  ...       1998.926  2.625148e+10\n",
              "17    121                          Mali  ...       2014.306  4.079138e+10\n",
              "18     20                         Benin  ...       2064.236  2.502514e+10\n",
              "19    135                         Nepal  ...       2442.804  7.117551e+10\n",
              "\n",
              "[20 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "VzXVJx3NBvSF",
        "outputId": "81677107-a740-4c4c-9b98-4bdbe95b506c"
      },
      "source": [
        "lowest_hospital_df =  countries_df[countries_df[\"population\"] > 10000000].sort_values(\"hospital_beds_per_thousand\").head(20).reset_index()\r\n",
        "lowest_hospital_df"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</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>0</th>\n",
              "      <td>121</td>\n",
              "      <td>Mali</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2.025083e+07</td>\n",
              "      <td>59.31</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2014.306</td>\n",
              "      <td>4.079138e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>117</td>\n",
              "      <td>Madagascar</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2.769102e+07</td>\n",
              "      <td>67.04</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1416.440</td>\n",
              "      <td>3.922267e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>140</td>\n",
              "      <td>Niger</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2.420664e+07</td>\n",
              "      <td>62.42</td>\n",
              "      <td>0.30</td>\n",
              "      <td>926.000</td>\n",
              "      <td>2.241534e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>135</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.913681e+07</td>\n",
              "      <td>70.78</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2442.804</td>\n",
              "      <td>7.117551e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82</td>\n",
              "      <td>Guinea</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.313279e+07</td>\n",
              "      <td>61.60</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1998.926</td>\n",
              "      <td>2.625148e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>63</td>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.149636e+08</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>6</th>\n",
              "      <td>31</td>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>Africa</td>\n",
              "      <td>2.090328e+07</td>\n",
              "      <td>61.58</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1703.102</td>\n",
              "      <td>3.560041e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>195</td>\n",
              "      <td>Uganda</td>\n",
              "      <td>Africa</td>\n",
              "      <td>4.574100e+07</td>\n",
              "      <td>63.37</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1697.707</td>\n",
              "      <td>7.765482e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>3.892834e+07</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>9</th>\n",
              "      <td>20</td>\n",
              "      <td>Benin</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.212320e+07</td>\n",
              "      <td>61.77</td>\n",
              "      <td>0.50</td>\n",
              "      <td>2064.236</td>\n",
              "      <td>2.502514e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>90</td>\n",
              "      <td>India</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.380004e+09</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>11</th>\n",
              "      <td>80</td>\n",
              "      <td>Guatemala</td>\n",
              "      <td>North America</td>\n",
              "      <td>1.791557e+07</td>\n",
              "      <td>74.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>7423.808</td>\n",
              "      <td>1.330017e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>145</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.208923e+08</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>13</th>\n",
              "      <td>85</td>\n",
              "      <td>Haiti</td>\n",
              "      <td>North America</td>\n",
              "      <td>1.140253e+07</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1653.173</td>\n",
              "      <td>1.885036e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>207</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>Asia</td>\n",
              "      <td>2.982597e+07</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>15</th>\n",
              "      <td>187</td>\n",
              "      <td>Tanzania</td>\n",
              "      <td>Africa</td>\n",
              "      <td>5.973421e+07</td>\n",
              "      <td>65.46</td>\n",
              "      <td>0.70</td>\n",
              "      <td>2683.304</td>\n",
              "      <td>1.602851e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>132</td>\n",
              "      <td>Mozambique</td>\n",
              "      <td>Africa</td>\n",
              "      <td>3.125544e+07</td>\n",
              "      <td>60.85</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1136.103</td>\n",
              "      <td>3.550939e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>32</td>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.189078e+07</td>\n",
              "      <td>61.58</td>\n",
              "      <td>0.80</td>\n",
              "      <td>702.225</td>\n",
              "      <td>8.350004e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>33</td>\n",
              "      <td>Cambodia</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1.671897e+07</td>\n",
              "      <td>69.82</td>\n",
              "      <td>0.80</td>\n",
              "      <td>3645.070</td>\n",
              "      <td>6.094182e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>204</td>\n",
              "      <td>Venezuela</td>\n",
              "      <td>South America</td>\n",
              "      <td>2.843594e+07</td>\n",
              "      <td>72.06</td>\n",
              "      <td>0.80</td>\n",
              "      <td>16745.022</td>\n",
              "      <td>4.761605e+11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    index      location  ... gdp_per_capita           gdp\n",
              "0     121          Mali  ...       2014.306  4.079138e+10\n",
              "1     117    Madagascar  ...       1416.440  3.922267e+10\n",
              "2     140         Niger  ...        926.000  2.241534e+10\n",
              "3     135         Nepal  ...       2442.804  7.117551e+10\n",
              "4      82        Guinea  ...       1998.926  2.625148e+10\n",
              "5      63      Ethiopia  ...       1729.927  1.988786e+11\n",
              "6      31  Burkina Faso  ...       1703.102  3.560041e+10\n",
              "7     195        Uganda  ...       1697.707  7.765482e+10\n",
              "8       0   Afghanistan  ...       1803.987  7.022622e+10\n",
              "9      20         Benin  ...       2064.236  2.502514e+10\n",
              "10     90         India  ...       6426.674  8.868838e+12\n",
              "11     80     Guatemala  ...       7423.808  1.330017e+11\n",
              "12    145      Pakistan  ...       5034.708  1.112128e+12\n",
              "13     85         Haiti  ...       1653.173  1.885036e+10\n",
              "14    207         Yemen  ...       1479.147  4.411699e+10\n",
              "15    187      Tanzania  ...       2683.304  1.602851e+11\n",
              "16    132    Mozambique  ...       1136.103  3.550939e+10\n",
              "17     32       Burundi  ...        702.225  8.350004e+09\n",
              "18     33      Cambodia  ...       3645.070  6.094182e+10\n",
              "19    204     Venezuela  ...      16745.022  4.761605e+11\n",
              "\n",
              "[20 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvB2xbQiBvSF",
        "outputId": "456ad4af-1361-4908-a59e-7a339a1dacb5"
      },
      "source": [
        "# set(lowest_gdp_df['location']).intersection(set(lowest_hospital_df['location']))\r\n",
        "total_countries_having_low_gdp_and_bed = lowest_gdp_df['location'].isin(lowest_hospital_df['location']).sum()\r\n",
        "print(f\"Countries having lowest gdp per capita and lowest hosiptal bed are: {total_countries_having_low_gdp_and_bed}\")"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Countries having lowest gdp per capita and lowest hosiptal bed are: 14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ia3mN9rKBvSF"
      },
      "source": [
        "import jovian"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "qJkRQNL3BvSF",
        "outputId": "dfa284e0-398a-4ad2-caf8-a6daa063fb8c"
      },
      "source": [
        "jovian.commit(project='pandas-practice-assignment', environment=None)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://jovian.ai/elysian01/pandas-practice-assignment'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3icNX-ECBvSH"
      },
      "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. It's time to record one final version of your notebook for submission.\n",
        "\n",
        "Make a submission here by filling the submission form: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-3-pandas-practice\n",
        "\n",
        "Also make sure to help others on the forum: https://jovian.ml/forum/t/assignment-3-pandas-practice/11225/2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fuCARa-BvSH",
        "outputId": "6594c54c-ff21-486e-e6ce-c67ff7010e39"
      },
      "source": [
        "jovian.submit(assignment=\"zero-to-pandas-a3\")"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] Uploading colab notebook to Jovian...\u001b[0m\n",
            "[jovian] Capturing environment..\u001b[0m\n",
            "[jovian] Committed successfully! https://jovian.ai/elysian01/pandas-practice-assignment\u001b[0m\n",
            "[jovian] Submitting assignment..\u001b[0m\n",
            "[jovian] Verify your submission at https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-3-pandas-practice\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alvTgCPDMw2v"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}