230240.ipynb

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        "# Assignment 1.1 - Pandas Data Analysis Practice\n",
        "\n",
        "*This assignment is a part of the project [\"Customer Churn Analysis: From Data To Strategy\"]*\n",
        "\n",
        "In this assignment, you'll get to practice some of the concepts and skills covered in pandas.\n",
        "\n",
        "As you go through this notebook, you will find a **???** in certain places. To complete this assignment, you must replace all the **???** with appropriate values, expressions or statements to ensure that the notebook runs properly end-to-end.\n",
        "\n",
        "Some things to keep in mind:\n",
        "\n",
        "* Do not change variable names, delete cells or disturb other existing code. It may cause problems during evaluation.\n",
        "* In some cases, you may need to add some code cells or new statements before or after the line of code containing the **???**.\n",
        "* Questions marked **(Optional)** will not be considered for evaluation, and can be skipped. They are for your learning.\n",
        "\n",
        "\n",
        "If you are stuck, you can ask for help on the Whatsapp group. Please do not use **ChatGPT or any other LLM to get solutions to the questions.**\n",
        "\n",
        "\n",
        "Deadline : **22 May, 11:59 pm**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nzCoPd_zNae4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKwMduIXNae5"
      },
      "source": [
        "In this assignment, we're going to analyze an operate on data from a CSV file. Let's begin by downloading the CSV file."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xa18DHWvNae8"
      },
      "source": [
        "Let's load the data from the CSV file into a Pandas data frame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2QPUhz19Nae-"
      },
      "outputs": [],
      "source": [
        "countries_df = pd.read_csv('countries.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New section"
      ],
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        "id": "31H-aTmeKE3m"
      }
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          "height": 424
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          "output_type": "execute_result",
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            "text/plain": [
              "           location continent  population  life_expectancy  \\\n",
              "0       Afghanistan      Asia  38928341.0            64.83   \n",
              "1           Albania    Europe   2877800.0            78.57   \n",
              "2           Algeria    Africa  43851043.0            76.88   \n",
              "3           Andorra    Europe     77265.0            83.73   \n",
              "4            Angola    Africa  32866268.0            61.15   \n",
              "..              ...       ...         ...              ...   \n",
              "205         Vietnam      Asia  97338583.0            75.40   \n",
              "206  Western Sahara    Africa    597330.0            70.26   \n",
              "207           Yemen      Asia  29825968.0            66.12   \n",
              "208          Zambia    Africa  18383956.0            63.89   \n",
              "209        Zimbabwe    Africa  14862927.0            61.49   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita  \n",
              "0                          0.50        1803.987  \n",
              "1                          2.89       11803.431  \n",
              "2                          1.90       13913.839  \n",
              "3                           NaN             NaN  \n",
              "4                           NaN        5819.495  \n",
              "..                          ...             ...  \n",
              "205                        2.60        6171.884  \n",
              "206                         NaN             NaN  \n",
              "207                        0.70        1479.147  \n",
              "208                        2.00        3689.251  \n",
              "209                        1.70        1899.775  \n",
              "\n",
              "[210 rows x 6 columns]"
            ],
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              "    <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",
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              "      <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",
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              "    <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",
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              "      <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",
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              "      <th>3</th>\n",
              "      <td>Andorra</td>\n",
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              "      <td>83.73</td>\n",
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              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
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              "      <td>32866268.0</td>\n",
              "      <td>61.15</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <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",
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              "      <th>206</th>\n",
              "      <td>Western Sahara</td>\n",
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              "      <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",
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              "summary": "{\n  \"name\": \"countries_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205.13450256,\n        \"min\": 809.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445.0,\n          2078932.0,\n          786559.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "countries_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE8FlOjINafA"
      },
      "source": [
        "**Q1: How many countries does the dataframe contain?**\n",
        "\n",
        "Hint: Use the `.shape` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZD4-AM-CNafB"
      },
      "outputs": [],
      "source": [
        "num_countries = len(countries_df.location.tolist()) #this is another way but we can use .shape method also thats the same but that was given in hint so :)\n",
        "#num_countries = countries_df.shape[0] will also give the same result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iy8SMfkwNafC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5630a82e-fb94-46aa-dcb4-013052187f37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 210 countries in the dataset\n"
          ]
        }
      ],
      "source": [
        "print('There are {} countries in the dataset'.format(num_countries))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9eCXKMCNafE"
      },
      "source": [
        "**Q2: Retrieve a list of continents from the dataframe?**\n",
        "\n",
        "*Hint: Use the `.unique` method of a series.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xL8zFetJNafF"
      },
      "outputs": [],
      "source": [
        "continents = countries_df.continent.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "40j7HkWZNafG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bd33bb9-692e-484a-d30d-2a1aa7a98604"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Asia', 'Europe', 'Africa', 'North America', 'South America',\n",
              "       'Oceania'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "continents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yr9rdD5ZNafK"
      },
      "source": [
        "**Q3: What is the total population of all the countries listed in this dataset?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IrGeG0vwNafL"
      },
      "outputs": [],
      "source": [
        "total_population = countries_df.population.sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FUJRPPMcNafM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "749cca66-c83b-4321-adb2-834099b3fb24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total population is 7757980095.\n"
          ]
        }
      ],
      "source": [
        "print('The total population is {}.'.format(int(total_population)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvs9htXuNafO"
      },
      "source": [
        "**Q: (Optional) What is the overall life expectancy across in the world?**\n",
        "\n",
        "*Hint: You'll need to take a weighted average of life expectancy using populations as weights.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8TXTdZ0ENafP"
      },
      "outputs": [],
      "source": [
        "weighted_sum = (countries_df.population * countries_df.life_expectancy).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "97C4RlcPNafP"
      },
      "outputs": [],
      "source": [
        "overall_life_expectancy = weighted_sum/total_population"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1H_0zo8NafR"
      },
      "source": [
        "**Q4: Create a dataframe containing 10 countries with the highest population.**\n",
        "\n",
        "*Hint: Chain the `sort_values` and `head` methods.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3BBebkbyNafR"
      },
      "outputs": [],
      "source": [
        "most_populous_df = countries_df.sort_values(['population'], ascending = False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L83PYtCaNafS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "c5d387c7-64bd-4bc9-ce0f-768966428509"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          location      continent    population  life_expectancy  \\\n",
              "41           China           Asia  1.439324e+09            76.91   \n",
              "90           India           Asia  1.380004e+09            69.66   \n",
              "199  United States  North America  3.310026e+08            78.86   \n",
              "91       Indonesia           Asia  2.735236e+08            71.72   \n",
              "145       Pakistan           Asia  2.208923e+08            67.27   \n",
              "27          Brazil  South America  2.125594e+08            75.88   \n",
              "141        Nigeria         Africa  2.061396e+08            54.69   \n",
              "15      Bangladesh           Asia  1.646894e+08            72.59   \n",
              "157         Russia         Europe  1.459345e+08            72.58   \n",
              "125         Mexico  North America  1.289328e+08            75.05   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita  \n",
              "41                         4.34       15308.712  \n",
              "90                         0.53        6426.674  \n",
              "199                        2.77       54225.446  \n",
              "91                         1.04       11188.744  \n",
              "145                        0.60        5034.708  \n",
              "27                         2.20       14103.452  \n",
              "141                         NaN        5338.454  \n",
              "15                         0.80        3523.984  \n",
              "157                        8.05       24765.954  \n",
              "125                        1.38       17336.469  "
            ],
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              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>continent</th>\n",
              "      <th>population</th>\n",
              "      <th>life_expectancy</th>\n",
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              "      <th>41</th>\n",
              "      <td>China</td>\n",
              "      <td>Asia</td>\n",
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              "      <td>76.91</td>\n",
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              "      <th>90</th>\n",
              "      <td>India</td>\n",
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              "      <td>5034.708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Russia</td>\n",
              "      <td>Europe</td>\n",
              "      <td>1.459345e+08</td>\n",
              "      <td>72.58</td>\n",
              "      <td>8.05</td>\n",
              "      <td>24765.954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>North America</td>\n",
              "      <td>1.289328e+08</td>\n",
              "      <td>75.05</td>\n",
              "      <td>1.38</td>\n",
              "      <td>17336.469</td>\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e5aa7dfd-6180-4c0b-a325-d9822ea8479d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('most_populous_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_0515ec7b-19ef-4ef3-b52d-dd48db11c0f3 button.colab-df-generate');\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('most_populous_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "most_populous_df",
              "summary": "{\n  \"name\": \"most_populous_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Russia\",\n          \"India\",\n          \"Brazil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"North America\",\n          \"Europe\",\n          \"South America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 509275248.0010067,\n        \"min\": 128932753.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          145934460.0,\n          1380004385.0,\n          212559409.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.8433072738584855,\n        \"min\": 54.69,\n        \"max\": 78.86,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          72.58,\n          69.66,\n          75.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4507283497859254,\n        \"min\": 0.53,\n        \"max\": 8.05,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8.05,\n          0.53,\n          2.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15083.054118576796,\n        \"min\": 3523.9840000000004,\n        \"max\": 54225.446,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          24765.954,\n          6426.674,\n          14103.452\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ],
      "source": [
        "most_populous_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RldAR4WJNafT"
      },
      "source": [
        "**Q5: Add a new column in `countries_df` to record the overall GDP per country (product of population & per capita GDP).**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R_mdT6GMNafU"
      },
      "outputs": [],
      "source": [
        "countries_df['gdp'] = countries_df.population * countries_df.gdp_per_capita"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLydP8pHNafV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "63b1d9e4-afa5-40a3-cf35-1d175ced2b0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location continent  population  life_expectancy  \\\n",
              "0       Afghanistan      Asia  38928341.0            64.83   \n",
              "1           Albania    Europe   2877800.0            78.57   \n",
              "2           Algeria    Africa  43851043.0            76.88   \n",
              "3           Andorra    Europe     77265.0            83.73   \n",
              "4            Angola    Africa  32866268.0            61.15   \n",
              "..              ...       ...         ...              ...   \n",
              "205         Vietnam      Asia  97338583.0            75.40   \n",
              "206  Western Sahara    Africa    597330.0            70.26   \n",
              "207           Yemen      Asia  29825968.0            66.12   \n",
              "208          Zambia    Africa  18383956.0            63.89   \n",
              "209        Zimbabwe    Africa  14862927.0            61.49   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  \n",
              "0                          0.50        1803.987  7.022622e+10  \n",
              "1                          2.89       11803.431  3.396791e+10  \n",
              "2                          1.90       13913.839  6.101364e+11  \n",
              "3                           NaN             NaN           NaN  \n",
              "4                           NaN        5819.495  1.912651e+11  \n",
              "..                          ...             ...           ...  \n",
              "205                        2.60        6171.884  6.007624e+11  \n",
              "206                         NaN             NaN           NaN  \n",
              "207                        0.70        1479.147  4.411699e+10  \n",
              "208                        2.00        3689.251  6.782303e+10  \n",
              "209                        1.70        1899.775  2.823622e+10  \n",
              "\n",
              "[210 rows x 7 columns]"
            ],
            "text/html": [
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              "<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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "countries_df",
              "summary": "{\n  \"name\": \"countries_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205.13450256,\n        \"min\": 809.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445.0,\n          2078932.0,\n          786559.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.9540001,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "countries_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXtXfNfBNafX"
      },
      "source": [
        "**Q: (Optional) Create a dataframe containing 10 countries with the lowest GDP per capita, among the counties with population greater than 100 million.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EkCt9Wl7NafX"
      },
      "outputs": [],
      "source": [
        "count_pop_gre_100 = countries_df.loc[countries_df['population'] > 100000000]\n",
        "sub_df = count_pop_gre_100.sort_values(['gdp_per_capita']).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "_62q5yL5v3Qb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nM5flE-QNafY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "2f9c8214-e4f3-4928-9258-2e6fe186e7ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        location      continent    population  life_expectancy  \\\n",
              "63      Ethiopia         Africa  1.149636e+08            66.60   \n",
              "15    Bangladesh           Asia  1.646894e+08            72.59   \n",
              "145     Pakistan           Asia  2.208923e+08            67.27   \n",
              "141      Nigeria         Africa  2.061396e+08            54.69   \n",
              "90         India           Asia  1.380004e+09            69.66   \n",
              "151  Philippines           Asia  1.095811e+08            71.23   \n",
              "58         Egypt         Africa  1.023344e+08            71.99   \n",
              "91     Indonesia           Asia  2.735236e+08            71.72   \n",
              "27        Brazil  South America  2.125594e+08            75.88   \n",
              "41         China           Asia  1.439324e+09            76.91   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  \n",
              "63                         0.30        1729.927  1.988786e+11  \n",
              "15                         0.80        3523.984  5.803628e+11  \n",
              "145                        0.60        5034.708  1.112128e+12  \n",
              "141                         NaN        5338.454  1.100467e+12  \n",
              "90                         0.53        6426.674  8.868838e+12  \n",
              "151                        1.00        7599.188  8.327273e+11  \n",
              "58                         1.60       10550.206  1.079649e+12  \n",
              "91                         1.04       11188.744  3.060386e+12  \n",
              "27                         2.20       14103.452  2.997821e+12  \n",
              "41                         4.34       15308.712  2.203419e+13  "
            ],
            "text/html": [
              "\n",
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              "    <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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1.149636e+08</td>\n",
              "      <td>66.60</td>\n",
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              "      <th>15</th>\n",
              "      <td>Bangladesh</td>\n",
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              "      <td>Pakistan</td>\n",
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              "      <th>141</th>\n",
              "      <td>Nigeria</td>\n",
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              "      <td>1.100467e+12</td>\n",
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              "    <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",
              "      <td>8.868838e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\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>58</th>\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>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",
              "      <td>3.060386e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\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>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",
              "      <td>2.203419e+13</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sub_df",
              "summary": "{\n  \"name\": \"sub_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Brazil\",\n          \"Bangladesh\",\n          \"Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Africa\",\n          \"Asia\",\n          \"South America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 523443815.2608435,\n        \"min\": 102334403.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          212559409.0,\n          164689383.0,\n          109581085.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.2429821933645355,\n        \"min\": 54.69,\n        \"max\": 76.91,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          75.88,\n          72.59,\n          71.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2537986724794021,\n        \"min\": 0.3,\n        \"max\": 4.34,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2.2,\n          0.8,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4535.510632165357,\n        \"min\": 1729.927,\n        \"max\": 15308.712,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          14103.452,\n          3523.9840000000004,\n          7599.188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6765324654366.156,\n        \"min\": 198878606248.44098,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2997821421979.8677,\n          580362750661.8721,\n          832727266158.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "sub_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NA6oQnVNafZ"
      },
      "source": [
        "**Q6: Create a data frame that counts the number countries in each continent?**\n",
        "\n",
        "*Hint: Use `groupby`, select the `location` column and aggregate using `count`.*"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_counts_df = countries_df.groupby([\"continent\"]).count()\n",
        "country_counts_df = country_counts_df.location"
      ],
      "metadata": {
        "id": "9iB1hHSk2HG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6fVwjKFFNafw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "864d5734-5249-4e7f-8a87-7c4ea4a42817"
      },
      "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": {},
          "execution_count": 81
        }
      ],
      "source": [
        "country_counts_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ut6cNp0yNafy"
      },
      "source": [
        "**Q7: Create a data frame showing the total population of each continent.**\n",
        "\n",
        "*Hint: Use `groupby`, select the population column and aggregate using `sum`.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hqnwb5V0Nafy"
      },
      "outputs": [],
      "source": [
        "continent_populations_df = countries_df.groupby([\"continent\"]).sum()\n",
        "continent_populations_df = continent_populations_df.population"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5gAu5IANafz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab07b8b2-af0a-4c3a-f934-aa176c81db42"
      },
      "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": {},
          "execution_count": 83
        }
      ],
      "source": [
        "continent_populations_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Am9h8-R4Naf1"
      },
      "source": [
        "Let's download another CSV file containing overall Covid-19 stats for various countires, and read the data into another Pandas data frame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sOybiAeVNaf6"
      },
      "outputs": [],
      "source": [
        "covid_data_df = pd.read_csv('covid-countries-data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "k9PuPoYUNaf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ae0030f7-ff33-465c-f777-b369198786b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location  total_cases  total_deaths  total_tests\n",
              "0       Afghanistan      38243.0        1409.0          NaN\n",
              "1           Albania       9728.0         296.0          NaN\n",
              "2           Algeria      45158.0        1525.0          NaN\n",
              "3           Andorra       1199.0          53.0          NaN\n",
              "4            Angola       2729.0         109.0          NaN\n",
              "..              ...          ...           ...          ...\n",
              "207  Western Sahara        766.0           1.0          NaN\n",
              "208           World   26059065.0      863535.0          NaN\n",
              "209           Yemen       1976.0         571.0          NaN\n",
              "210          Zambia      12415.0         292.0          NaN\n",
              "211        Zimbabwe       6638.0         206.0      97272.0\n",
              "\n",
              "[212 rows x 4 columns]"
            ],
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              "\n",
              "  <div id=\"df-dbed6317-cc94-4e1a-b216-da9d0c976c00\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>45158.0</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
              "      <td>2729.0</td>\n",
              "      <td>109.0</td>\n",
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              "      <th>207</th>\n",
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              "      <td>766.0</td>\n",
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              "    <tr>\n",
              "      <th>208</th>\n",
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              "      <td>863535.0</td>\n",
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              "    <tr>\n",
              "      <th>209</th>\n",
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              "    <tr>\n",
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              "      <td>206.0</td>\n",
              "      <td>97272.0</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>212 rows × 4 columns</p>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "\n",
              "  <div id=\"id_2f9c0dac-a0f3-4a15-8152-69fc99efad5f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid_data_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('covid_data_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid_data_df",
              "summary": "{\n  \"name\": \"covid_data_df\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 212,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Nicaragua\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1875762.2640246067,\n        \"min\": 3.0,\n        \"max\": 26059065.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          16454.0,\n          3310.0,\n          1382.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61654.54433404689,\n        \"min\": 0.0,\n        \"max\": 863535.0,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          42.0,\n          626.0,\n          329.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "covid_data_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "weKZowz0Naf8"
      },
      "source": [
        "**Q8: Count the number of countries for which the `total_tests` data is missing.**\n",
        "\n",
        "*Hint: Use the `.isna` method.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UybZmvdFNaf9"
      },
      "outputs": [],
      "source": [
        "total_tests_missing = covid_data_df.isna().shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_jz32NxNaf-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8659fb3-91e9-4ef1-9350-76dd39b05730"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The data for total tests is missing for 212 countries.\n"
          ]
        }
      ],
      "source": [
        "print(\"The data for total tests is missing for {} countries.\".format(int(total_tests_missing)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bK9yyH3GNagA"
      },
      "source": [
        "Let's merge the two data frames, and compute some more metrics.\n",
        "\n",
        "**Q9: Merge `countries_df` with `covid_data_df` on the `location` column.**\n",
        "\n",
        "*Hint: Use the `.merge` method on `countries_df`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6qu0tGtJNagB"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "combined_df = countries_df.merge(covid_data_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNR9WKaENagC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "3e568e82-ac5c-45d9-e796-9b29d473528e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location continent  population  life_expectancy  \\\n",
              "0       Afghanistan      Asia  38928341.0            64.83   \n",
              "1           Albania    Europe   2877800.0            78.57   \n",
              "2           Algeria    Africa  43851043.0            76.88   \n",
              "3           Andorra    Europe     77265.0            83.73   \n",
              "4            Angola    Africa  32866268.0            61.15   \n",
              "..              ...       ...         ...              ...   \n",
              "205         Vietnam      Asia  97338583.0            75.40   \n",
              "206  Western Sahara    Africa    597330.0            70.26   \n",
              "207           Yemen      Asia  29825968.0            66.12   \n",
              "208          Zambia    Africa  18383956.0            63.89   \n",
              "209        Zimbabwe    Africa  14862927.0            61.49   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
              "1                          2.89       11803.431  3.396791e+10       9728.0   \n",
              "2                          1.90       13913.839  6.101364e+11      45158.0   \n",
              "3                           NaN             NaN           NaN       1199.0   \n",
              "4                           NaN        5819.495  1.912651e+11       2729.0   \n",
              "..                          ...             ...           ...          ...   \n",
              "205                        2.60        6171.884  6.007624e+11       1046.0   \n",
              "206                         NaN             NaN           NaN        766.0   \n",
              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
              "208                        2.00        3689.251  6.782303e+10      12415.0   \n",
              "209                        1.70        1899.775  2.823622e+10       6638.0   \n",
              "\n",
              "     total_deaths  total_tests  \n",
              "0          1409.0          NaN  \n",
              "1           296.0          NaN  \n",
              "2          1525.0          NaN  \n",
              "3            53.0          NaN  \n",
              "4           109.0          NaN  \n",
              "..            ...          ...  \n",
              "205          35.0     261004.0  \n",
              "206           1.0          NaN  \n",
              "207         571.0          NaN  \n",
              "208         292.0          NaN  \n",
              "209         206.0      97272.0  \n",
              "\n",
              "[210 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02cc8c32-7853-4be8-82dd-ff5c70767d44\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02cc8c32-7853-4be8-82dd-ff5c70767d44')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "type": "dataframe",
              "variable_name": "combined_df",
              "summary": "{\n  \"name\": \"combined_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205.13450256,\n        \"min\": 809.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445.0,\n          2078932.0,\n          786559.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.9540001,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577759.0509830201,\n        \"min\": 3.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          896.0,\n          317528.0,\n          246116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17424.11835293718,\n        \"min\": 0.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          29.0,\n          67376.0,\n          577.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "combined_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMFIKAXvNagG"
      },
      "source": [
        "\n",
        "**Q10: Add columns `tests_per_million`, `cases_per_million` and `deaths_per_million` into `combined_df`.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_KdWa_juNagH"
      },
      "outputs": [],
      "source": [
        "combined_df['tests_per_million'] = combined_df['total_tests'] * 1e6 / combined_df['population']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IDdn-5CONagI"
      },
      "outputs": [],
      "source": [
        "combined_df['cases_per_million'] = combined_df['total_cases'] * 1e6 / combined_df['population']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93n1py5BNagK"
      },
      "outputs": [],
      "source": [
        "combined_df['deaths_per_million'] = combined_df['total_deaths'] * 1e6 / combined_df['population']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IRMupBBUNagL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "0e7bb311-49be-4f29-bbb9-5c4be512b428"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location continent  population  life_expectancy  \\\n",
              "0       Afghanistan      Asia  38928341.0            64.83   \n",
              "1           Albania    Europe   2877800.0            78.57   \n",
              "2           Algeria    Africa  43851043.0            76.88   \n",
              "3           Andorra    Europe     77265.0            83.73   \n",
              "4            Angola    Africa  32866268.0            61.15   \n",
              "..              ...       ...         ...              ...   \n",
              "205         Vietnam      Asia  97338583.0            75.40   \n",
              "206  Western Sahara    Africa    597330.0            70.26   \n",
              "207           Yemen      Asia  29825968.0            66.12   \n",
              "208          Zambia    Africa  18383956.0            63.89   \n",
              "209        Zimbabwe    Africa  14862927.0            61.49   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
              "1                          2.89       11803.431  3.396791e+10       9728.0   \n",
              "2                          1.90       13913.839  6.101364e+11      45158.0   \n",
              "3                           NaN             NaN           NaN       1199.0   \n",
              "4                           NaN        5819.495  1.912651e+11       2729.0   \n",
              "..                          ...             ...           ...          ...   \n",
              "205                        2.60        6171.884  6.007624e+11       1046.0   \n",
              "206                         NaN             NaN           NaN        766.0   \n",
              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
              "208                        2.00        3689.251  6.782303e+10      12415.0   \n",
              "209                        1.70        1899.775  2.823622e+10       6638.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "0          1409.0          NaN                NaN         982.394806   \n",
              "1           296.0          NaN                NaN        3380.359997   \n",
              "2          1525.0          NaN                NaN        1029.804468   \n",
              "3            53.0          NaN                NaN       15518.022390   \n",
              "4           109.0          NaN                NaN          83.033462   \n",
              "..            ...          ...                ...                ...   \n",
              "205          35.0     261004.0        2681.403324          10.745996   \n",
              "206           1.0          NaN                NaN        1282.373228   \n",
              "207         571.0          NaN                NaN          66.250993   \n",
              "208         292.0          NaN                NaN         675.317108   \n",
              "209         206.0      97272.0        6544.605918         446.614587   \n",
              "\n",
              "     deaths_per_million  \n",
              "0             36.194710  \n",
              "1            102.856349  \n",
              "2             34.776824  \n",
              "3            685.950948  \n",
              "4              3.316470  \n",
              "..                  ...  \n",
              "205            0.359570  \n",
              "206            1.674116  \n",
              "207           19.144391  \n",
              "208           15.883415  \n",
              "209           13.859989  \n",
              "\n",
              "[210 rows x 13 columns]"
            ],
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              "      <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",
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              "    <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>\n",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('combined_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      buttonEl.onclick = () => {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "combined_df",
              "summary": "{\n  \"name\": \"combined_df\",\n  \"rows\": 210,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"Bulgaria\",\n          \"Slovenia\",\n          \"Guyana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Europe\",\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142509205.13450256,\n        \"min\": 809.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 210,\n        \"samples\": [\n          6948445.0,\n          2078932.0,\n          786559.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.560056151115564,\n        \"min\": 53.28,\n        \"max\": 86.75,\n        \"num_unique_values\": 197,\n        \"samples\": [\n          74.25,\n          76.16,\n          79.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649168855927868,\n        \"min\": 0.1,\n        \"max\": 13.8,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          2.397,\n          0.3,\n          2.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19820.802488409416,\n        \"min\": 661.24,\n        \"max\": 116935.6,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          50669.315,\n          3601.006,\n          1569.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272769090054.96,\n        \"min\": 669035852.9540001,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          3155330252.9950004,\n          94988326544.65,\n          17572900832.351997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 577759.0509830201,\n        \"min\": 3.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          896.0,\n          317528.0,\n          246116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17424.11835293718,\n        \"min\": 0.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          29.0,\n          67376.0,\n          577.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10616500.238255894,\n        \"min\": 8408.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          445722.0,\n          869430.0,\n          711225.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 137501.5426053333,\n        \"min\": 1969.1317223799424,\n        \"max\": 725696.6351209254,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          8289.217482188455,\n          7562.655732467907,\n          131192.26184700849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5920.784473156789,\n        \"min\": 3.0238238837004348,\n        \"max\": 41375.74364990663,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          2368.011835741666,\n          1437.2764477144995,\n          1757.0201345353623\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181.9372798469054,\n        \"min\": 0.0,\n        \"max\": 1237.5508279804349,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          0.3595696477315681,\n          576.2792756308129,\n          854.0403754533369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "combined_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VNjlGq-uNagO"
      },
      "source": [
        "**Q11: Create a dataframe with 10 countires that have highest number of tests per million people.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HBcHEMJZNagO"
      },
      "outputs": [],
      "source": [
        "highest_tests_df = combined_df.sort_values(['tests_per_million'], ascending = False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R57UrLhxNagR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "outputId": "e6e52554-8408-47e7-a652-a53d553688fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 location      continent   population  life_expectancy  \\\n",
              "197  United Arab Emirates           Asia    9890400.0            77.97   \n",
              "14                Bahrain           Asia    1701583.0            77.29   \n",
              "115            Luxembourg         Europe     625976.0            82.25   \n",
              "122                 Malta         Europe     441539.0            82.53   \n",
              "53                Denmark         Europe    5792203.0            80.90   \n",
              "96                 Israel           Asia    8655541.0            82.97   \n",
              "89                Iceland         Europe     341250.0            82.99   \n",
              "157                Russia         Europe  145934460.0            72.58   \n",
              "199         United States  North America  331002647.0            78.86   \n",
              "10              Australia        Oceania   25499881.0            83.44   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "197                       1.200       67293.483  6.655595e+11      71540.0   \n",
              "14                        2.000       43290.705  7.366273e+10      52440.0   \n",
              "115                       4.510       94277.965  5.901574e+10       7928.0   \n",
              "122                       4.485       36513.323  1.612206e+10       1931.0   \n",
              "53                        2.500       46682.515  2.703946e+11      17195.0   \n",
              "96                        2.990       33132.320  2.867782e+11     122539.0   \n",
              "89                        2.910       46482.958  1.586231e+10       2121.0   \n",
              "157                       8.050       24765.954  3.614206e+12    1005000.0   \n",
              "199                       2.770       54225.446  1.794877e+13    6114406.0   \n",
              "10                        3.840       44648.710  1.138537e+12      25923.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "197         387.0    7177430.0      725696.635121        7233.276713   \n",
              "14          190.0    1118837.0      657527.137965       30818.361490   \n",
              "115         124.0     385820.0      616349.508607       12665.022301   \n",
              "122          13.0     188539.0      427004.183096        4373.339614   \n",
              "53          626.0    2447911.0      422621.755488        2968.645954   \n",
              "96          969.0    2353984.0      271962.665303       14157.289533   \n",
              "89           10.0      88829.0      260304.761905        6215.384615   \n",
              "157       17414.0   37176827.0      254750.159763        6886.653091   \n",
              "199      185744.0   83898416.0      253467.507769       18472.377957   \n",
              "10          663.0    6255797.0      245326.517406        1016.592979   \n",
              "\n",
              "     deaths_per_million  \n",
              "197           39.128852  \n",
              "14           111.660730  \n",
              "115          198.090662  \n",
              "122           29.442473  \n",
              "53           108.076323  \n",
              "96           111.951408  \n",
              "89            29.304029  \n",
              "157          119.327539  \n",
              "199          561.155633  \n",
              "10            26.000121  "
            ],
            "text/html": [
              "\n",
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              "  <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",
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              "      <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>197</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>Asia</td>\n",
              "      <td>9890400.0</td>\n",
              "      <td>77.97</td>\n",
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              "      <td>39.128852</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>Asia</td>\n",
              "      <td>1701583.0</td>\n",
              "      <td>77.29</td>\n",
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              "      <td>111.660730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\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>122</th>\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>53</th>\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>96</th>\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",
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              "    <tr>\n",
              "      <th>89</th>\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>157</th>\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",
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              "      <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",
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              "    <tr>\n",
              "      <th>199</th>\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",
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              "      <td>18472.377957</td>\n",
              "      <td>561.155633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
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              "      <td>Oceania</td>\n",
              "      <td>25499881.0</td>\n",
              "      <td>83.44</td>\n",
              "      <td>3.840</td>\n",
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              "      <td>26.000121</td>\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('highest_tests_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "highest_tests_df",
              "summary": "{\n  \"name\": \"highest_tests_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"United States\",\n          \"Bahrain\",\n          \"Israel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Europe\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107311533.67270595,\n        \"min\": 341250.0,\n        \"max\": 331002647.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          331002647.0,\n          1701583.0,\n          8655541.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4860381842748396,\n        \"min\": 72.58,\n        \"max\": 83.44,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          78.86,\n          77.29,\n          82.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8997959685772108,\n        \"min\": 1.2,\n        \"max\": 8.05,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.77,\n          2.0,\n          2.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19635.862293553666,\n        \"min\": 24765.954,\n        \"max\": 94277.965,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          54225.446,\n          43290.705,\n          33132.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5568733880862.82,\n        \"min\": 15862309417.5,\n        \"max\": 17948766160755.562,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          17948766160755.562,\n          73662727686.015,\n          286778154185.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1912321.1602205113,\n        \"min\": 1931.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6114406.0,\n          52440.0,\n          122539.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58268.23309488627,\n        \"min\": 10.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          185744.0,\n          190.0,\n          969.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26944351.12680865,\n        \"min\": 88829.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          83898416.0,\n          1118837.0,\n          2353984.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 188863.64988759023,\n        \"min\": 245326.51740610084,\n        \"max\": 725696.6351209254,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          253467.50776890313,\n          657527.1379650596,\n          271962.66530307004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8948.81706062444,\n        \"min\": 1016.5929793946882,\n        \"max\": 30818.361490447423,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          18472.377956542445,\n          30818.361490447423,\n          14157.289532797546\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 160.2070341751217,\n        \"min\": 26.000121333899557,\n        \"max\": 561.1556332961893,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          561.1556332961893,\n          111.66073003785299,\n          111.95140777451115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "highest_tests_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ug-VTfalNagT"
      },
      "source": [
        "**Q12: Create a dataframe with 10 countires that have highest number of positive cases per million people.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HxjUJq8oNagU"
      },
      "outputs": [],
      "source": [
        "highest_cases_df = combined_df.sort_values(['cases_per_million'], ascending = False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7EuO7nPENagY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "895a56a4-389e-437c-a773-09f75002aa1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          location      continent   population  life_expectancy  \\\n",
              "155          Qatar           Asia    2881060.0            80.23   \n",
              "14         Bahrain           Asia    1701583.0            77.29   \n",
              "147         Panama  North America    4314768.0            78.51   \n",
              "40           Chile  South America   19116209.0            80.18   \n",
              "162     San Marino         Europe      33938.0            84.97   \n",
              "9            Aruba  North America     106766.0            76.29   \n",
              "105         Kuwait           Asia    4270563.0            75.49   \n",
              "150           Peru  South America   32971846.0            76.74   \n",
              "27          Brazil  South America  212559409.0            75.88   \n",
              "199  United States  North America  331002647.0            78.86   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "155                        1.20      116935.600  3.368985e+11     119206.0   \n",
              "14                         2.00       43290.705  7.366273e+10      52440.0   \n",
              "147                        2.30       22267.037  9.607710e+10      94084.0   \n",
              "40                         2.11       22767.037  4.352194e+11     414739.0   \n",
              "162                        3.80       56861.470  1.929765e+09        735.0   \n",
              "9                           NaN       35973.781  3.840777e+09       2211.0   \n",
              "105                        2.00       65530.537  2.798523e+11      86478.0   \n",
              "150                        1.60       12236.706  4.034668e+11     663437.0   \n",
              "27                         2.20       14103.452  2.997821e+12    3997865.0   \n",
              "199                        2.77       54225.446  1.794877e+13    6114406.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "155         199.0     634745.0      220316.480740       41375.743650   \n",
              "14          190.0    1118837.0      657527.137965       30818.361490   \n",
              "147        2030.0     336345.0       77952.047480       21805.112117   \n",
              "40        11344.0    2458762.0      128621.841287       21695.671982   \n",
              "162          42.0          NaN                NaN       21657.139490   \n",
              "9            12.0          NaN                NaN       20708.839893   \n",
              "105         535.0     621616.0      145558.325682       20249.789079   \n",
              "150       29259.0     584232.0       17719.117092       20121.318048   \n",
              "27       123780.0    4797948.0       22572.268255       18808.224105   \n",
              "199      185744.0   83898416.0      253467.507769       18472.377957   \n",
              "\n",
              "     deaths_per_million  \n",
              "155           69.071800  \n",
              "14           111.660730  \n",
              "147          470.477208  \n",
              "40           593.423100  \n",
              "162         1237.550828  \n",
              "9            112.395332  \n",
              "105          125.276222  \n",
              "150          887.393445  \n",
              "27           582.331314  \n",
              "199          561.155633  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8e05dd6-a3ef-4016-a2ea-8ed329dbcc95\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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>155</th>\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>14</th>\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>147</th>\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>40</th>\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>162</th>\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>9</th>\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>105</th>\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>150</th>\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>27</th>\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>199</th>\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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "highest_cases_df",
              "summary": "{\n  \"name\": \"highest_cases_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Brazil\",\n          \"Bahrain\",\n          \"Aruba\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"North America\",\n          \"Europe\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115066072.5428885,\n        \"min\": 33938.0,\n        \"max\": 331002647.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          212559409.0,\n          1701583.0,\n          106766.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8508252216585377,\n        \"min\": 75.49,\n        \"max\": 84.97,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          75.88,\n          77.29,\n          76.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7364950780555155,\n        \"min\": 1.2,\n        \"max\": 3.8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0,\n          1.6,\n          1.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31521.033454425327,\n        \"min\": 12236.706,\n        \"max\": 116935.6,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          14103.452,\n          43290.705,\n          35973.781\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5584993050328.886,\n        \"min\": 1929764568.8600001,\n        \"max\": 17948766160755.562,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2997821421979.8677,\n          73662727686.015,\n          3840776702.2460003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2126140.16035718,\n        \"min\": 735.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3997865.0,\n          52440.0,\n          2211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65269.65371484328,\n        \"min\": 12.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          123780.0,\n          190.0,\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29167947.168856256,\n        \"min\": 336345.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1118837.0,\n          584232.0,\n          634745.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206859.83079591303,\n        \"min\": 17719.117091593842,\n        \"max\": 657527.1379650596,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          657527.1379650596,\n          17719.117091593842,\n          220316.48073972773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7146.358129151361,\n        \"min\": 18472.377956542445,\n        \"max\": 41375.74364990663,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          18808.224104537287,\n          30818.361490447423,\n          20708.839892849785\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 385.15817089082395,\n        \"min\": 69.0717999625138,\n        \"max\": 1237.5508279804349,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          582.3313142538893,\n          111.66073003785299,\n          112.39533184721728\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "highest_cases_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GX-WdUFHNagb"
      },
      "source": [
        "**Q13: Create a dataframe with 10 countires that have highest number of deaths cases per million people?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hH19qct0Nagc"
      },
      "outputs": [],
      "source": [
        "highest_deaths_df = combined_df.sort_values(['deaths_per_million'], ascending = False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lWhydo7ENagd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "38d2396a-ed75-4020-ca8e-91c932a41577"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location      continent   population  life_expectancy  \\\n",
              "162      San Marino         Europe      33938.0            84.97   \n",
              "150            Peru  South America   32971846.0            76.74   \n",
              "18          Belgium         Europe   11589616.0            81.63   \n",
              "3           Andorra         Europe      77265.0            83.73   \n",
              "177           Spain         Europe   46754783.0            83.56   \n",
              "198  United Kingdom         Europe   67886004.0            81.32   \n",
              "40            Chile  South America   19116209.0            80.18   \n",
              "97            Italy         Europe   60461828.0            83.51   \n",
              "27           Brazil  South America  212559409.0            75.88   \n",
              "182          Sweden         Europe   10099270.0            82.80   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "162                        3.80       56861.470  1.929765e+09        735.0   \n",
              "150                        1.60       12236.706  4.034668e+11     663437.0   \n",
              "18                         5.64       42658.576  4.943965e+11      85817.0   \n",
              "3                           NaN             NaN           NaN       1199.0   \n",
              "177                        2.97       34272.360  1.602397e+12     479554.0   \n",
              "198                        2.54       39753.244  2.698689e+12     338676.0   \n",
              "40                         2.11       22767.037  4.352194e+11     414739.0   \n",
              "97                         3.18       35220.084  2.129471e+12     271515.0   \n",
              "27                         2.20       14103.452  2.997821e+12    3997865.0   \n",
              "182                        2.22       46949.283  4.741535e+11      84532.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "162          42.0          NaN                NaN       21657.139490   \n",
              "150       29259.0     584232.0       17719.117092       20121.318048   \n",
              "18         9898.0    2281853.0      196887.713967        7404.645676   \n",
              "3            53.0          NaN                NaN       15518.022390   \n",
              "177       29194.0    6416533.0      137238.001939       10256.790198   \n",
              "198       41514.0   13447568.0      198090.434075        4988.892850   \n",
              "40        11344.0    2458762.0      128621.841287       21695.671982   \n",
              "97        35497.0    5214766.0       86248.897403        4490.684602   \n",
              "27       123780.0    4797948.0       22572.268255       18808.224105   \n",
              "182        5820.0          NaN                NaN        8370.109919   \n",
              "\n",
              "     deaths_per_million  \n",
              "162         1237.550828  \n",
              "150          887.393445  \n",
              "18           854.040375  \n",
              "3            685.950948  \n",
              "177          624.406705  \n",
              "198          611.525168  \n",
              "40           593.423100  \n",
              "97           587.097697  \n",
              "27           582.331314  \n",
              "182          576.279276  "
            ],
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              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>continent</th>\n",
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              "      <th>162</th>\n",
              "      <td>San Marino</td>\n",
              "      <td>Europe</td>\n",
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              "      <td>1.60</td>\n",
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              "      <th>18</th>\n",
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              "      <td>Europe</td>\n",
              "      <td>11589616.0</td>\n",
              "      <td>81.63</td>\n",
              "      <td>5.64</td>\n",
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              "      <td>4.943965e+11</td>\n",
              "      <td>85817.0</td>\n",
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              "      <td>7404.645676</td>\n",
              "      <td>854.040375</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",
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              "      <td>NaN</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>53.0</td>\n",
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              "      <td>15518.022390</td>\n",
              "      <td>685.950948</td>\n",
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              "    <tr>\n",
              "      <th>177</th>\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",
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              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Europe</td>\n",
              "      <td>67886004.0</td>\n",
              "      <td>81.32</td>\n",
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              "      <td>611.525168</td>\n",
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              "    <tr>\n",
              "      <th>40</th>\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>97</th>\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",
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              "      <td>86248.897403</td>\n",
              "      <td>4490.684602</td>\n",
              "      <td>587.097697</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
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              "      <td>75.88</td>\n",
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              "      <td>582.331314</td>\n",
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              "    <tr>\n",
              "      <th>182</th>\n",
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              "      <td>82.80</td>\n",
              "      <td>2.22</td>\n",
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              "      <td>84532.0</td>\n",
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              "type": "dataframe",
              "variable_name": "highest_deaths_df",
              "summary": "{\n  \"name\": \"highest_deaths_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Brazil\",\n          \"Peru\",\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"South America\",\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63261092.27665661,\n        \"min\": 33938.0,\n        \"max\": 212559409.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          212559409.0,\n          32971846.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.036353075648485,\n        \"min\": 75.88,\n        \"max\": 84.97,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          75.88,\n          76.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2141949779357695,\n        \"min\": 1.6,\n        \"max\": 5.64,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2.2,\n          1.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14981.251594784439,\n        \"min\": 12236.706,\n        \"max\": 56861.47,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          14103.452,\n          12236.706\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1127067748356.8257,\n        \"min\": 1929764568.8600001,\n        \"max\": 2997821421979.8677,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2997821421979.8677,\n          403466785779.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1202353.056205248,\n        \"min\": 735.0,\n        \"max\": 3997865.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3997865.0,\n          663437.0\n   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            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "highest_deaths_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUTDc8q4Nage"
      },
      "source": [
        "**(Optional) Q: Count number of countries that feature in both the lists of \"highest number of tests per million\" and \"highest number of cases per million\".**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nXat9wCrNagf"
      },
      "outputs": [],
      "source": [
        "count = 0\n",
        "for countries in highest_tests_df.location.tolist():\n",
        "  for s_country in highest_cases_df.location.tolist():\n",
        "    if countries == s_country:\n",
        "      count = count + 1\n",
        "      break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7y8D9OtNagg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ad73614-4948-4b5a-b6f0-e8a2ea90f441"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "count"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHWghQHUNagh"
      },
      "source": [
        "**(Optional) Q: Count number of countries that feature in both the lists \"20 countries with lowest GDP per capita\" and \"20 countries with the lowest number of hospital beds per thousand population\". Only consider countries with a population higher than 10 million while creating the list.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5C7khtW3Nagi"
      },
      "outputs": [],
      "source": [
        "countries_with_pop_10 = combined_df.loc[combined_df.population > 10000000]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countries_with_pop_10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "hqpKX0wsUjfR",
        "outputId": "45903e1f-ff3d-41e9-b5aa-35e70af892ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        location      continent  population  life_expectancy  \\\n",
              "0    Afghanistan           Asia  38928341.0            64.83   \n",
              "2        Algeria         Africa  43851043.0            76.88   \n",
              "4         Angola         Africa  32866268.0            61.15   \n",
              "7      Argentina  South America  45195777.0            76.67   \n",
              "10     Australia        Oceania  25499881.0            83.44   \n",
              "..           ...            ...         ...              ...   \n",
              "204    Venezuela  South America  28435943.0            72.06   \n",
              "205      Vietnam           Asia  97338583.0            75.40   \n",
              "207        Yemen           Asia  29825968.0            66.12   \n",
              "208       Zambia         Africa  18383956.0            63.89   \n",
              "209     Zimbabwe         Africa  14862927.0            61.49   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
              "2                          1.90       13913.839  6.101364e+11      45158.0   \n",
              "4                           NaN        5819.495  1.912651e+11       2729.0   \n",
              "7                          5.00       18933.907  8.557326e+11     428226.0   \n",
              "10                         3.84       44648.710  1.138537e+12      25923.0   \n",
              "..                          ...             ...           ...          ...   \n",
              "204                        0.80       16745.022  4.761605e+11      48883.0   \n",
              "205                        2.60        6171.884  6.007624e+11       1046.0   \n",
              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
              "208                        2.00        3689.251  6.782303e+10      12415.0   \n",
              "209                        1.70        1899.775  2.823622e+10       6638.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "0          1409.0          NaN                NaN         982.394806   \n",
              "2          1525.0          NaN                NaN        1029.804468   \n",
              "4           109.0          NaN                NaN          83.033462   \n",
              "7          8971.0    1118923.0       24757.246678        9474.911782   \n",
              "10          663.0    6255797.0      245326.517406        1016.592979   \n",
              "..            ...          ...                ...                ...   \n",
              "204         398.0          NaN                NaN        1719.056759   \n",
              "205          35.0     261004.0        2681.403324          10.745996   \n",
              "207         571.0          NaN                NaN          66.250993   \n",
              "208         292.0          NaN                NaN         675.317108   \n",
              "209         206.0      97272.0        6544.605918         446.614587   \n",
              "\n",
              "     deaths_per_million  \n",
              "0             36.194710  \n",
              "2             34.776824  \n",
              "4              3.316470  \n",
              "7            198.491996  \n",
              "10            26.000121  \n",
              "..                  ...  \n",
              "204           13.996371  \n",
              "205            0.359570  \n",
              "207           19.144391  \n",
              "208           15.883415  \n",
              "209           13.859989  \n",
              "\n",
              "[90 rows x 13 columns]"
            ],
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              "<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>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>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>7</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>South America</td>\n",
              "      <td>45195777.0</td>\n",
              "      <td>76.67</td>\n",
              "      <td>5.00</td>\n",
              "      <td>18933.907</td>\n",
              "      <td>8.557326e+11</td>\n",
              "      <td>428226.0</td>\n",
              "      <td>8971.0</td>\n",
              "      <td>1118923.0</td>\n",
              "      <td>24757.246678</td>\n",
              "      <td>9474.911782</td>\n",
              "      <td>198.491996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>25499881.0</td>\n",
              "      <td>83.44</td>\n",
              "      <td>3.84</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",
              "    <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>204</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>South America</td>\n",
              "      <td>28435943.0</td>\n",
              "      <td>72.06</td>\n",
              "      <td>0.80</td>\n",
              "      <td>16745.022</td>\n",
              "      <td>4.761605e+11</td>\n",
              "      <td>48883.0</td>\n",
              "      <td>398.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1719.056759</td>\n",
              "      <td>13.996371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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>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",
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              "    <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",
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              "            style=\"display:none;\">\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "countries_with_pop_10",
              "summary": "{\n  \"name\": \"countries_with_pop_10\",\n  \"rows\": 90,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"Japan\",\n          \"Czech Republic\",\n          \"Nigeria\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Asia\",\n          \"Africa\",\n          \"North America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209803195.9912688,\n        \"min\": 10099270.0,\n        \"max\": 1439323774.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          126476458.0,\n          10708982.0,\n          206139587.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.93173146261308,\n        \"min\": 54.24,\n        \"max\": 84.63,\n        \"num_unique_values\": 86,\n        \"samples\": [\n          77.15,\n          64.83,\n          65.31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6199256374330577,\n        \"min\": 0.1,\n        \"max\": 13.05,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.5,\n          0.8,\n          13.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14742.228237343736,\n        \"min\": 702.225,\n        \"max\": 54225.446,\n        \"num_unique_values\": 86,\n        \"samples\": [\n          25129.341,\n          1803.987,\n          4466.5070000000005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3205739639037.21,\n        \"min\": 8350003687.725,\n        \"max\": 22034193130919.086,\n        \"num_unique_values\": 86,\n        \"samples\": [\n          2119385174264.847,\n          70226221095.567,\n          195853066933.38303\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 860588.197487974,\n        \"min\": 274.0,\n        \"max\": 6114406.0,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          69599.0,\n          25773.0,\n          54463.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25723.368343385027,\n        \"min\": 0.0,\n        \"max\": 185744.0,\n        \"num_unique_values\": 87,\n        \"samples\": [\n          58.0,\n          1409.0,\n          5461.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13312959.698384311,\n        \"min\": 87646.0,\n        \"max\": 83898416.0,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          405916.0,\n          4797948.0,\n          2662508.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71116.05269659178,\n        \"min\": 1969.1317223799424,\n        \"max\": 254750.1597634993,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          1969.1317223799424,\n          22572.26825466004,\n          12053.419817458489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4713.796765415039,\n        \"min\": 8.52107987092757,\n        \"max\": 21695.67198182443,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          550.2921342088817,\n          2406.671334399479,\n          264.2044684022773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 212.7093840551815,\n        \"min\": 0.0,\n        \"max\": 887.3934446982435,\n        \"num_unique_values\": 90,\n        \"samples\": [\n          10.428818302296227,\n          39.686311920218,\n          4.9820610148015865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RKk0r3umNagj"
      },
      "outputs": [],
      "source": [
        "countries_lowest_gdp_df = countries_with_pop_10.sort_values(['gdp_per_capita'], ascending = True).head(20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "countries_lowest_gdp_df.sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "0P9uqx7NUM-6",
        "outputId": "3f8b3cc6-879b-4f32-ce19-16fa84593339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         location      continent   population  \\\n",
              "0                     Afghanistan           Asia   38928341.0   \n",
              "20                          Benin         Africa   12123198.0   \n",
              "31                   Burkina Faso         Africa   20903278.0   \n",
              "32                        Burundi         Africa   11890781.0   \n",
              "39                           Chad         Africa   16425859.0   \n",
              "52   Democratic Republic of Congo         Africa   89561404.0   \n",
              "63                       Ethiopia         Africa  114963583.0   \n",
              "82                         Guinea         Africa   13132792.0   \n",
              "85                          Haiti  North America   11402533.0   \n",
              "117                    Madagascar         Africa   27691019.0   \n",
              "118                        Malawi         Africa   19129955.0   \n",
              "121                          Mali         Africa   20250834.0   \n",
              "132                    Mozambique         Africa   31255435.0   \n",
              "135                         Nepal           Asia   29136808.0   \n",
              "140                         Niger         Africa   24206636.0   \n",
              "158                        Rwanda         Africa   12952209.0   \n",
              "176                   South Sudan         Africa   11193729.0   \n",
              "195                        Uganda         Africa   45741000.0   \n",
              "207                         Yemen           Asia   29825968.0   \n",
              "209                      Zimbabwe         Africa   14862927.0   \n",
              "\n",
              "     life_expectancy  hospital_beds_per_thousand  gdp_per_capita  \\\n",
              "0              64.83                         0.5        1803.987   \n",
              "20             61.77                         0.5        2064.236   \n",
              "31             61.58                         0.4        1703.102   \n",
              "32             61.58                         0.8         702.225   \n",
              "39             54.24                         NaN        1768.153   \n",
              "52             60.68                         NaN         808.133   \n",
              "63             66.60                         0.3        1729.927   \n",
              "82             61.60                         0.3        1998.926   \n",
              "85             64.00                         0.7        1653.173   \n",
              "117            67.04                         0.2        1416.440   \n",
              "118            64.26                         1.3        1095.042   \n",
              "121            59.31                         0.1        2014.306   \n",
              "132            60.85                         0.7        1136.103   \n",
              "135            70.78                         0.3        2442.804   \n",
              "140            62.42                         0.3         926.000   \n",
              "158            69.02                         NaN        1854.211   \n",
              "176            57.85                         NaN        1569.888   \n",
              "195            63.37                         0.5        1697.707   \n",
              "207            66.12                         0.7        1479.147   \n",
              "209            61.49                         1.7        1899.775   \n",
              "\n",
              "              gdp  total_cases  total_deaths  total_tests  tests_per_million  \\\n",
              "0    7.022622e+10      38243.0        1409.0          NaN                NaN   \n",
              "20   2.502514e+10       2194.0          40.0          NaN                NaN   \n",
              "31   3.560041e+10       1378.0          55.0          NaN                NaN   \n",
              "32   8.350004e+09        448.0           1.0          NaN                NaN   \n",
              "39   2.904343e+10       1017.0          77.0          NaN                NaN   \n",
              "52   7.237753e+10      10113.0         258.0          NaN                NaN   \n",
              "63   1.988786e+11      54409.0         846.0     869430.0        7562.655732   \n",
              "82   2.625148e+10       9479.0          59.0          NaN                NaN   \n",
              "85   1.885036e+10       8258.0         206.0          NaN                NaN   \n",
              "117  3.922267e+10      15023.0         196.0          NaN                NaN   \n",
              "118  2.094810e+10       5579.0         175.0          NaN                NaN   \n",
              "121  4.079138e+10       2802.0         126.0          NaN                NaN   \n",
              "132  3.550939e+10       4117.0          25.0          NaN                NaN   \n",
              "135  7.117551e+10      41649.0         251.0     705560.0       24215.418518   \n",
              "140  2.241534e+10       1177.0          69.0          NaN                NaN   \n",
              "158  2.401613e+10       4218.0          17.0     393237.0       30360.612618   \n",
              "176  1.757290e+10       2532.0          47.0          NaN                NaN   \n",
              "195  7.765482e+10       3112.0          32.0     381749.0        8345.882250   \n",
              "207  4.411699e+10       1976.0         571.0          NaN                NaN   \n",
              "209  2.823622e+10       6638.0         206.0      97272.0        6544.605918   \n",
              "\n",
              "     cases_per_million  deaths_per_million  \n",
              "0           982.394806           36.194710  \n",
              "20          180.975350            3.299459  \n",
              "31           65.922675            2.631166  \n",
              "32           37.676247            0.084099  \n",
              "39           61.914570            4.687730  \n",
              "52          112.916944            2.880705  \n",
              "63          473.271610            7.358852  \n",
              "82          721.781020            4.492571  \n",
              "85          724.225047           18.066161  \n",
              "117         542.522469            7.078107  \n",
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              "      <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",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Asia</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>64.83</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Benin</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12123198.0</td>\n",
              "      <td>61.77</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>180.975350</td>\n",
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              "    <tr>\n",
              "      <th>31</th>\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",
              "      <td>1378.0</td>\n",
              "      <td>55.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>65.922675</td>\n",
              "      <td>2.631166</td>\n",
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              "    <tr>\n",
              "      <th>32</th>\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",
              "      <td>448.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.676247</td>\n",
              "      <td>0.084099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\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",
              "      <td>1017.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.914570</td>\n",
              "      <td>4.687730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\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",
              "      <td>10113.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112.916944</td>\n",
              "      <td>2.880705</td>\n",
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              "    <tr>\n",
              "      <th>63</th>\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",
              "      <td>54409.0</td>\n",
              "      <td>846.0</td>\n",
              "      <td>869430.0</td>\n",
              "      <td>7562.655732</td>\n",
              "      <td>473.271610</td>\n",
              "      <td>7.358852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\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",
              "      <td>9479.0</td>\n",
              "      <td>59.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>721.781020</td>\n",
              "      <td>4.492571</td>\n",
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              "    <tr>\n",
              "      <th>85</th>\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",
              "      <td>8258.0</td>\n",
              "      <td>206.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>724.225047</td>\n",
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              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Madagascar</td>\n",
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              "      <td>27691019.0</td>\n",
              "      <td>67.04</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>196.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>542.522469</td>\n",
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              "    <tr>\n",
              "      <th>118</th>\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",
              "      <td>5579.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>291.636860</td>\n",
              "      <td>9.147957</td>\n",
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              "    <tr>\n",
              "      <th>121</th>\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",
              "      <td>2802.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>138.364672</td>\n",
              "      <td>6.221966</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\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",
              "      <td>4117.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131.721091</td>\n",
              "      <td>0.799861</td>\n",
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              "    <tr>\n",
              "      <th>135</th>\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",
              "      <td>41649.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>705560.0</td>\n",
              "      <td>24215.418518</td>\n",
              "      <td>1429.429058</td>\n",
              "      <td>8.614533</td>\n",
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              "    <tr>\n",
              "      <th>140</th>\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",
              "      <td>1177.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.623030</td>\n",
              "      <td>2.850458</td>\n",
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              "    <tr>\n",
              "      <th>158</th>\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",
              "      <td>4218.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>393237.0</td>\n",
              "      <td>30360.612618</td>\n",
              "      <td>325.658735</td>\n",
              "      <td>1.312517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\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",
              "      <td>2532.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>226.198079</td>\n",
              "      <td>4.198780</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\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",
              "      <td>3112.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>381749.0</td>\n",
              "      <td>8345.882250</td>\n",
              "      <td>68.035242</td>\n",
              "      <td>0.699591</td>\n",
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              "    <tr>\n",
              "      <th>207</th>\n",
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              "      <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",
              "      <td>1976.0</td>\n",
              "      <td>571.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>66.250993</td>\n",
              "      <td>19.144391</td>\n",
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              "    <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>1.7</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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"countries_lowest_gdp_df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Afghanistan\",\n          \"Uganda\",\n          \"Rwanda\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Asia\",\n          \"Africa\",\n          \"North America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26916705.95370358,\n        \"min\": 11193729.0,\n        \"max\": 114963583.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38928341.0,\n          45741000.0,\n          12952209.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.830919190234279,\n        \"min\": 54.24,\n        \"max\": 70.78,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          64.83,\n          66.6,\n          60.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4166833330000133,\n        \"min\": 0.1,\n        \"max\": 1.7,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.1,\n          0.4,\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 454.49808084463956,\n        \"min\": 702.225,\n        \"max\": 2442.804,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1803.987,\n          1697.707,\n          1854.211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41446628301.94677,\n        \"min\": 8350003687.725,\n        \"max\": 198878606248.44098,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          70226221095.567,\n          77654815887.0,\n          24016128402.099\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15377.829512508892,\n        \"min\": 448.0,\n        \"max\": 54409.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38243.0,\n          3112.0,\n          4218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344.9453198467991,\n        \"min\": 1.0,\n        \"max\": 1409.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1409.0,\n          258.0,\n          126.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 302385.6655635316,\n        \"min\": 97272.0,\n        \"max\": 869430.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          705560.0,\n          97272.0,\n          393237.0\n        ],\n        \"semantic_type\": \"\",\n        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\"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.623040881666606,\n        \"min\": 0.08409876525351867,\n        \"max\": 36.19470965896029,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          36.19470965896029,\n          0.6995911764062875,\n          1.3125174246338984\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XH7POtliNagj"
      },
      "outputs": [],
      "source": [
        "countries_lowest_no_of_hosp_beds_df =  countries_with_pop_10.sort_values(['hospital_beds_per_thousand'], ascending = True).head(20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countries_lowest_no_of_hosp_beds_df.sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "Jgiz1Pj_UNwD",
        "outputId": "0279fbbf-1329-46e6-eb30-50e16854a0eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         location      continent    population  life_expectancy  \\\n",
              "0     Afghanistan           Asia  3.892834e+07            64.83   \n",
              "20          Benin         Africa  1.212320e+07            61.77   \n",
              "31   Burkina Faso         Africa  2.090328e+07            61.58   \n",
              "32        Burundi         Africa  1.189078e+07            61.58   \n",
              "33       Cambodia           Asia  1.671897e+07            69.82   \n",
              "63       Ethiopia         Africa  1.149636e+08            66.60   \n",
              "80      Guatemala  North America  1.791557e+07            74.30   \n",
              "82         Guinea         Africa  1.313279e+07            61.60   \n",
              "85          Haiti  North America  1.140253e+07            64.00   \n",
              "90          India           Asia  1.380004e+09            69.66   \n",
              "117    Madagascar         Africa  2.769102e+07            67.04   \n",
              "121          Mali         Africa  2.025083e+07            59.31   \n",
              "132    Mozambique         Africa  3.125544e+07            60.85   \n",
              "135         Nepal           Asia  2.913681e+07            70.78   \n",
              "140         Niger         Africa  2.420664e+07            62.42   \n",
              "145      Pakistan           Asia  2.208923e+08            67.27   \n",
              "187      Tanzania         Africa  5.973421e+07            65.46   \n",
              "195        Uganda         Africa  4.574100e+07            63.37   \n",
              "204     Venezuela  South America  2.843594e+07            72.06   \n",
              "207         Yemen           Asia  2.982597e+07            66.12   \n",
              "\n",
              "     hospital_beds_per_thousand  gdp_per_capita           gdp  total_cases  \\\n",
              "0                          0.50        1803.987  7.022622e+10      38243.0   \n",
              "20                         0.50        2064.236  2.502514e+10       2194.0   \n",
              "31                         0.40        1703.102  3.560041e+10       1378.0   \n",
              "32                         0.80         702.225  8.350004e+09        448.0   \n",
              "33                         0.80        3645.070  6.094182e+10        274.0   \n",
              "63                         0.30        1729.927  1.988786e+11      54409.0   \n",
              "80                         0.60        7423.808  1.330017e+11      75664.0   \n",
              "82                         0.30        1998.926  2.625148e+10       9479.0   \n",
              "85                         0.70        1653.173  1.885036e+10       8258.0   \n",
              "90                         0.53        6426.674  8.868838e+12    3853406.0   \n",
              "117                        0.20        1416.440  3.922267e+10      15023.0   \n",
              "121                        0.10        2014.306  4.079138e+10       2802.0   \n",
              "132                        0.70        1136.103  3.550939e+10       4117.0   \n",
              "135                        0.30        2442.804  7.117551e+10      41649.0   \n",
              "140                        0.30         926.000  2.241534e+10       1177.0   \n",
              "145                        0.60        5034.708  1.112128e+12     297014.0   \n",
              "187                        0.70        2683.304  1.602851e+11        509.0   \n",
              "195                        0.50        1697.707  7.765482e+10       3112.0   \n",
              "204                        0.80       16745.022  4.761605e+11      48883.0   \n",
              "207                        0.70        1479.147  4.411699e+10       1976.0   \n",
              "\n",
              "     total_deaths  total_tests  tests_per_million  cases_per_million  \\\n",
              "0          1409.0          NaN                NaN         982.394806   \n",
              "20           40.0          NaN                NaN         180.975350   \n",
              "31           55.0          NaN                NaN          65.922675   \n",
              "32            1.0          NaN                NaN          37.676247   \n",
              "33            0.0          NaN                NaN          16.388568   \n",
              "63          846.0     869430.0        7562.655732         473.271610   \n",
              "80         2790.0          NaN                NaN        4223.366193   \n",
              "82           59.0          NaN                NaN         721.781020   \n",
              "85          206.0          NaN                NaN         724.225047   \n",
              "90        67376.0   44337201.0       32128.304433        2792.314316   \n",
              "117         196.0          NaN                NaN         542.522469   \n",
              "121         126.0          NaN                NaN         138.364672   \n",
              "132          25.0          NaN                NaN         131.721091   \n",
              "135         251.0     705560.0       24215.418518        1429.429058   \n",
              "140          69.0          NaN                NaN          48.623030   \n",
              "145        6328.0    2662508.0       12053.419817        1344.609832   \n",
              "187          21.0          NaN                NaN           8.521080   \n",
              "195          32.0     381749.0        8345.882250          68.035242   \n",
              "204         398.0          NaN                NaN        1719.056759   \n",
              "207         571.0          NaN                NaN          66.250993   \n",
              "\n",
              "     deaths_per_million  \n",
              "0             36.194710  \n",
              "20             3.299459  \n",
              "31             2.631166  \n",
              "32             0.084099  \n",
              "33             0.000000  \n",
              "63             7.358852  \n",
              "80           155.730488  \n",
              "82             4.492571  \n",
              "85            18.066161  \n",
              "90            48.823033  \n",
              "117            7.078107  \n",
              "121            6.221966  \n",
              "132            0.799861  \n",
              "135            8.614533  \n",
              "140            2.850458  \n",
              "145           28.647441  \n",
              "187            0.351557  \n",
              "195            0.699591  \n",
              "204           13.996371  \n",
              "207           19.144391  "
            ],
            "text/html": [
              "\n",
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              "<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>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",
              "      <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>20</th>\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",
              "      <td>2194.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>180.975350</td>\n",
              "      <td>3.299459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\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",
              "      <td>1378.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.922675</td>\n",
              "      <td>2.631166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\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",
              "      <td>448.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.676247</td>\n",
              "      <td>0.084099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\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",
              "      <td>274.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.388568</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\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",
              "      <td>54409.0</td>\n",
              "      <td>846.0</td>\n",
              "      <td>869430.0</td>\n",
              "      <td>7562.655732</td>\n",
              "      <td>473.271610</td>\n",
              "      <td>7.358852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\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",
              "      <td>75664.0</td>\n",
              "      <td>2790.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4223.366193</td>\n",
              "      <td>155.730488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\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",
              "      <td>9479.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>721.781020</td>\n",
              "      <td>4.492571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\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",
              "      <td>8258.0</td>\n",
              "      <td>206.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>724.225047</td>\n",
              "      <td>18.066161</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",
              "      <td>8.868838e+12</td>\n",
              "      <td>3853406.0</td>\n",
              "      <td>67376.0</td>\n",
              "      <td>44337201.0</td>\n",
              "      <td>32128.304433</td>\n",
              "      <td>2792.314316</td>\n",
              "      <td>48.823033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\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",
              "      <td>15023.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>542.522469</td>\n",
              "      <td>7.078107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\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",
              "      <td>2802.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>138.364672</td>\n",
              "      <td>6.221966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\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",
              "      <td>4117.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131.721091</td>\n",
              "      <td>0.799861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\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",
              "      <td>41649.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>705560.0</td>\n",
              "      <td>24215.418518</td>\n",
              "      <td>1429.429058</td>\n",
              "      <td>8.614533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\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",
              "      <td>1177.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.623030</td>\n",
              "      <td>2.850458</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",
              "      <td>1.112128e+12</td>\n",
              "      <td>297014.0</td>\n",
              "      <td>6328.0</td>\n",
              "      <td>2662508.0</td>\n",
              "      <td>12053.419817</td>\n",
              "      <td>1344.609832</td>\n",
              "      <td>28.647441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\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",
              "      <td>509.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.521080</td>\n",
              "      <td>0.351557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\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",
              "      <td>3112.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>381749.0</td>\n",
              "      <td>8345.882250</td>\n",
              "      <td>68.035242</td>\n",
              "      <td>0.699591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\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",
              "      <td>48883.0</td>\n",
              "      <td>398.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1719.056759</td>\n",
              "      <td>13.996371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\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",
              "      <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",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"countries_lowest_no_of_hosp_beds_df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Afghanistan\",\n          \"Uganda\",\n          \"Pakistan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"continent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Africa\",\n          \"South America\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 303339206.66697335,\n        \"min\": 11402533.0,\n        \"max\": 1380004385.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38928341.0,\n          45741000.0,\n          220892331.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life_expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.15444074391931,\n        \"min\": 59.31,\n        \"max\": 74.3,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          64.83,\n          74.3,\n          60.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_beds_per_thousand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21342507621620926,\n        \"min\": 0.1,\n        \"max\": 0.8,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.2,\n          0.4,\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3649.429981068947,\n        \"min\": 702.225,\n        \"max\": 16745.022,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1803.987,\n          1697.707,\n          5034.708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1968043061020.696,\n        \"min\": 8350003687.725,\n        \"max\": 8868838300965.49,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          70226221095.567,\n          77654815887.0,\n          1112128386024.348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 857078.0005157595,\n        \"min\": 274.0,\n        \"max\": 3853406.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38243.0,\n          3112.0,\n          297014.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14981.197459253981,\n        \"min\": 0.0,\n        \"max\": 67376.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1409.0,\n          32.0,\n          6328.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_tests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19332156.093841583,\n        \"min\": 381749.0,\n        \"max\": 44337201.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          44337201.0,\n          381749.0,\n          705560.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tests_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10831.173988964048,\n        \"min\": 7562.655732467907,\n        \"max\": 32128.304432887726,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          32128.304432887726,\n          8345.882250060122,\n          24215.41851804769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cases_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1093.210959327736,\n        \"min\": 8.52107987092757,\n        \"max\": 4223.366193210631,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          982.3948058819152,\n          68.03524190551147,\n          1344.6098316559483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths_per_million\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.985264269260206,\n        \"min\": 0.0,\n        \"max\": 155.7304884629105,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          36.19470965896029,\n          0.6995911764062875,\n          28.64744091092959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "for countries in countries_lowest_gdp_df.location.tolist():\n",
        "  for s_country in countries_lowest_no_of_hosp_beds_df.location.tolist():\n",
        "    if countries == s_country:\n",
        "      count = count + 1\n",
        "      break\n"
      ],
      "metadata": {
        "id": "I-Pp7L70T5xr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnowhufUUGXV",
        "outputId": "31578d05-6fec-49ee-fd68-72adf968b6a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
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      "cell_type": "markdown",
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        "id": "wvjafOzENagk"
      },
      "source": [
        "## Submission\n",
        "\n",
        "Congratulations on making it this far! You've reached the end of this assignment, and you just completed your first real-world data analysis problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LBOb5DhHNagl"
      },
      "outputs": [],
      "source": []
    }
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