Copy_of_Data_Puzzle_Earth's_PSA.ipynb

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      "name": "Copy of Data-Puzzle / Earth's PSA",
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        "# Intro\n",
        "\n",
        "Here's is the message from Earth:\n",
        "'I remember that if you sort the countries (or dependencies) [Guinea, Iran, Trinidad And Tobago, Honduras, Lebanon, Ethiopia, Niger, Afghanistan, India, American Samoa, Cuba, Gabon, Nicaragua, Channel Islands, Martinique] by density of population (DESCENDING) and then only look at the first letter of each country, you'll get my message back!! Can you help me please?'\n",
        "\n",
        "To help our planet, we preloaded world population and geographical data from the website Worldometer (https://worldometers.info/). Use it to do what the earth says and decode the message!"
      ],
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      "source": [
        "import pandas as pd\n",
        "\n",
        "countries = ['Guinea', 'Iran', 'Trinidad and Tobago', 'Honduras', 'Lebanon',\n",
        "             'Ethiopia', 'Niger', 'Afghanistan', 'India', 'American Samoa',\n",
        "             'Cuba', 'Gabon', 'Nicaragua', 'Channel Islands', 'Martinique']\n",
        "\n",
        "country_pop = pd.read_csv('https://raw.githubusercontent.com/hyperc54/data-puzzles-assets/master/misc/earth/population_by_country.csv')"
      ]
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        "country_pop.head()"
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              "  Country (or dependency)  Population (2020) Yearly Change Urban Pop %  \\\n",
              "0                   China         1440297825        0.39 %        61 %   \n",
              "1                   India         1382345085        0.99 %        35 %   \n",
              "2           United States          331341050        0.59 %        83 %   \n",
              "3               Indonesia          274021604        1.07 %        56 %   \n",
              "4                Pakistan          221612785        2.00 %        35 %   \n",
              "\n",
              "  World Share  \n",
              "0     18.47 %  \n",
              "1     17.70 %  \n",
              "2      4.25 %  \n",
              "3      3.51 %  \n",
              "4      2.83 %  "
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              "      <th>1</th>\n",
              "      <td>India</td>\n",
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              "      <td>0.99 %</td>\n",
              "      <td>35 %</td>\n",
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        "country_surface = pd.read_csv('https://raw.githubusercontent.com/hyperc54/data-puzzles-assets/master/misc/earth/surface_by_country.csv')"
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              "  Country (or dependency)  Land Area (Km²)\n",
              "0                   China          9388211\n",
              "1                   India          2973190\n",
              "2           United States          9147420\n",
              "3               Indonesia          1811570\n",
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        "df1=country_pop.loc[country_pop['Country (or dependency)'].isin(countries)]\n",
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              "   Country (or dependency)  Population (2020) Yearly Change Urban Pop %  \\\n",
              "1                    India         1382345085        0.99 %        35 %   \n",
              "11                Ethiopia          115434444        2.57 %        21 %   \n",
              "17                    Iran           84176929        1.30 %        76 %   \n",
              "36             Afghanistan           39074280        2.33 %        25 %   \n",
              "55                   Niger           24346468        3.84 %        17 %   \n",
              "\n",
              "   World Share  \n",
              "1      17.70 %  \n",
              "11      1.47 %  \n",
              "17      1.08 %  \n",
              "36      0.50 %  \n",
              "55      0.31 %  "
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              "      <th>1</th>\n",
              "      <td>India</td>\n",
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              "      <td>0.99 %</td>\n",
              "      <td>35 %</td>\n",
              "      <td>17.70 %</td>\n",
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              "      <th>11</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>115434444</td>\n",
              "      <td>2.57 %</td>\n",
              "      <td>21 %</td>\n",
              "      <td>1.47 %</td>\n",
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              "      <th>17</th>\n",
              "      <td>Iran</td>\n",
              "      <td>84176929</td>\n",
              "      <td>1.30 %</td>\n",
              "      <td>76 %</td>\n",
              "      <td>1.08 %</td>\n",
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              "      <th>36</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>39074280</td>\n",
              "      <td>2.33 %</td>\n",
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              "      <th>55</th>\n",
              "      <td>Niger</td>\n",
              "      <td>24346468</td>\n",
              "      <td>3.84 %</td>\n",
              "      <td>17 %</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=country_surface.loc[country_surface['Country (or dependency)'].isin(countries)]\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "z7Z7b1HG0srA",
        "outputId": "c1b1a791-e62f-4c4c-d8bf-a28a6ceae474"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country (or dependency)  Land Area (Km²)\n",
              "1                    India          2973190\n",
              "11                Ethiopia          1000000\n",
              "17                    Iran          1628550\n",
              "36             Afghanistan           652860\n",
              "55                   Niger          1266700"
            ],
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              "      <th></th>\n",
              "      <th>Country (or dependency)</th>\n",
              "      <th>Land Area (Km²)</th>\n",
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              "      <td>2973190</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Ethiopia</td>\n",
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              "      <td>1628550</td>\n",
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              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>652860</td>\n",
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              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Niger</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 21
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Your turn now!\n",
        "country=pd.merge(df1, df2)"
      ],
      "metadata": {
        "id": "ShldPu2-Frfh"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country.head()"
      ],
      "metadata": {
        "id": "uxxA4b9nbAWO",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6b9508bb-345d-4181-e7b3-253c5128c795"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Country (or dependency)  Population (2020) Yearly Change Urban Pop %  \\\n",
              "0                   India         1382345085        0.99 %        35 %   \n",
              "1                Ethiopia          115434444        2.57 %        21 %   \n",
              "2                    Iran           84176929        1.30 %        76 %   \n",
              "3             Afghanistan           39074280        2.33 %        25 %   \n",
              "4                   Niger           24346468        3.84 %        17 %   \n",
              "\n",
              "  World Share  Land Area (Km²)  \n",
              "0     17.70 %          2973190  \n",
              "1      1.47 %          1000000  \n",
              "2      1.08 %          1628550  \n",
              "3      0.50 %           652860  \n",
              "4      0.31 %          1266700  "
            ],
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              "      <td>17.70 %</td>\n",
              "      <td>2973190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ethiopia</td>\n",
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              "      <td>2.57 %</td>\n",
              "      <td>21 %</td>\n",
              "      <td>1.47 %</td>\n",
              "      <td>1000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iran</td>\n",
              "      <td>84176929</td>\n",
              "      <td>1.30 %</td>\n",
              "      <td>76 %</td>\n",
              "      <td>1.08 %</td>\n",
              "      <td>1628550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>39074280</td>\n",
              "      <td>2.33 %</td>\n",
              "      <td>25 %</td>\n",
              "      <td>0.50 %</td>\n",
              "      <td>652860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Niger</td>\n",
              "      <td>24346468</td>\n",
              "      <td>3.84 %</td>\n",
              "      <td>17 %</td>\n",
              "      <td>0.31 %</td>\n",
              "      <td>1266700</td>\n",
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              "          const element = document.querySelector('#df-91030eb1-029a-4bd7-a01d-67ec0689e2c1');\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country['density']=country['Population (2020)']/country['Land Area (Km²)']"
      ],
      "metadata": {
        "id": "BeXl-OCjYhjw"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country.sort_values('density', ascending= False)"
      ],
      "metadata": {
        "id": "_WQEYpMCXAiC",
        "outputId": "cf5f56e4-c6c3-4f9e-b9f0-2e4d7f0ddeef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country (or dependency)  Population (2020) Yearly Change Urban Pop %  \\\n",
              "13         Channel Islands             174140        0.93 %        30 %   \n",
              "8                  Lebanon            6819976       -0.44 %        78 %   \n",
              "0                    India         1382345085        0.99 %        35 %   \n",
              "12              Martinique             375213       -0.08 %        92 %   \n",
              "14          American Samoa              55169       -0.22 %        88 %   \n",
              "11     Trinidad and Tobago            1400283        0.32 %        52 %   \n",
              "1                 Ethiopia          115434444        2.57 %        21 %   \n",
              "6                     Cuba           11325391       -0.06 %        78 %   \n",
              "7                 Honduras            9931333        1.63 %        57 %   \n",
              "3              Afghanistan           39074280        2.33 %        25 %   \n",
              "9                Nicaragua            6638075        1.21 %        57 %   \n",
              "5                   Guinea           13191279        2.83 %        39 %   \n",
              "2                     Iran           84176929        1.30 %        76 %   \n",
              "4                    Niger           24346468        3.84 %        17 %   \n",
              "10                   Gabon            2234448        2.45 %        87 %   \n",
              "\n",
              "   World Share  Land Area (Km²)     density  \n",
              "13      0.00 %              190  916.526316  \n",
              "8       0.09 %            10230  666.664321  \n",
              "0      17.70 %          2973190  464.936679  \n",
              "12      0.00 %             1060  353.974528  \n",
              "14      0.00 %              200  275.845000  \n",
              "11      0.02 %             5130  272.959649  \n",
              "1       1.47 %          1000000  115.434444  \n",
              "6       0.15 %           106440  106.401644  \n",
              "7       0.13 %           111890   88.759791  \n",
              "3       0.50 %           652860   59.850933  \n",
              "9       0.08 %           120340   55.161002  \n",
              "5       0.17 %           245720   53.684189  \n",
              "2       1.08 %          1628550   51.688268  \n",
              "4       0.31 %          1266700   19.220390  \n",
              "10      0.03 %           257670    8.671743  "
            ],
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              "      <th>Country (or dependency)</th>\n",
              "      <th>Population (2020)</th>\n",
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              "      <th>Urban Pop %</th>\n",
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              "      <th>0</th>\n",
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              "      <td>17.70 %</td>\n",
              "      <td>2973190</td>\n",
              "      <td>464.936679</td>\n",
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              "      <th>12</th>\n",
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              "      <td>92 %</td>\n",
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              "      <td>88 %</td>\n",
              "      <td>0.00 %</td>\n",
              "      <td>200</td>\n",
              "      <td>275.845000</td>\n",
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              "      <th>11</th>\n",
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              "      <td>-0.06 %</td>\n",
              "      <td>78 %</td>\n",
              "      <td>0.15 %</td>\n",
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              "      <td>1.63 %</td>\n",
              "      <td>57 %</td>\n",
              "      <td>0.13 %</td>\n",
              "      <td>111890</td>\n",
              "      <td>88.759791</td>\n",
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              "      <th>3</th>\n",
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              "      <td>39074280</td>\n",
              "      <td>2.33 %</td>\n",
              "      <td>25 %</td>\n",
              "      <td>0.50 %</td>\n",
              "      <td>652860</td>\n",
              "      <td>59.850933</td>\n",
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              "      <td>1.21 %</td>\n",
              "      <td>57 %</td>\n",
              "      <td>0.08 %</td>\n",
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              "      <td>55.161002</td>\n",
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              "      <td>39 %</td>\n",
              "      <td>0.17 %</td>\n",
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              "      <td>76 %</td>\n",
              "      <td>1.08 %</td>\n",
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              "    <tr>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TB-znPoN4R11"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}