Charulata_Chapter_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/Charulata_Chapter_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
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      "source": [
        "Downloading and preparing the data"
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        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
        "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
        "y = lifesat[[\"Life satisfaction\"]].values"
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        "lifesat.head()"
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            "text/plain": [
              "   Country  GDP per capita (USD)  Life satisfaction\n",
              "0   Russia          26456.387938                5.8\n",
              "1   Greece          27287.083401                5.4\n",
              "2   Turkey          28384.987785                5.5\n",
              "3   Latvia          29932.493910                5.9\n",
              "4  Hungary          31007.768407                5.6"
            ],
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              "      <td>Russia</td>\n",
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              "      <td>5.8</td>\n",
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              "      <th>1</th>\n",
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              "      <td>5.4</td>\n",
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              "      <td>Turkey</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lifesat",
              "summary": "{\n  \"name\": \"lifesat\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "Visualizing the data\n"
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      "cell_type": "code",
      "source": [
        "# Visualize the data\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(lifesat[\"GDP per capita (USD)\"], lifesat[\"Life satisfaction\"], color='blue', edgecolors='w', s=100)\n",
        "plt.title(\"Relationship between GDP per Capita and Life Satisfaction\")\n",
        "plt.xlabel(\"GDP per capita (USD)\")\n",
        "plt.ylabel(\"Life satisfaction\")\n",
        "plt.grid(True)"
      ],
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        "id": "HTJWDgbDcOOn",
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training and running a Linear Reression model\n"
      ],
      "metadata": {
        "id": "LZcMfNtsfrwq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
        "print(model.predict(X_new))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nS61E_ZdA_a",
        "outputId": "7178ed7f-2a99-4996-ccd7-7093a013c17b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replacing the Linear Regression model with k-Nearest Neighbors (here k = 3)\n"
      ],
      "metadata": {
        "id": "BlcwmH5Gfp6I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a 3-Nearest Neighbors regression model\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "print(model.predict(X_new))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcf6Rb5HgGWl",
        "outputId": "69484c75-0d61-45f3-d617-33e669602378"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load and prepare Life satisfaction data**"
      ],
      "metadata": {
        "id": "KqbfwcAug_HB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "from pathlib import Path\n",
        "datapath = Path() / \"datasets\" / \"lifesat\"\n",
        "datapath.mkdir(parents=True, exist_ok=True)\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "for filename in (\"oecd_bli.csv\", \"gdp_per_capita.csv\"):\n",
        "    if not (datapath / filename).is_file():\n",
        "        print(\"Downloading\", filename)\n",
        "        url = data_root + \"lifesat/\" + filename\n",
        "        urllib.request.urlretrieve(url, datapath / filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGiXUh8hhC3u",
        "outputId": "ae84b479-bd4b-483e-a213-f408a82dc7ed"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading oecd_bli.csv\n",
            "Downloading gdp_per_capita.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
      ],
      "metadata": {
        "id": "9_07MzuVhsYF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VIyca87hiu3i",
        "outputId": "6b7048d5-42e2-4378-e0a2-0f378fb22e8f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     LOCATION         Country INDICATOR                          Indicator  \\\n",
              "0         AUS       Australia   JE_LMIS           Labour market insecurity   \n",
              "1         AUT         Austria   JE_LMIS           Labour market insecurity   \n",
              "2         BEL         Belgium   JE_LMIS           Labour market insecurity   \n",
              "3         CAN          Canada   JE_LMIS           Labour market insecurity   \n",
              "4         CZE  Czech Republic   JE_LMIS           Labour market insecurity   \n",
              "...       ...             ...       ...                                ...   \n",
              "2364      LTU       Lithuania   WL_EWLH  Employees working very long hours   \n",
              "2365      COL        Colombia   WL_EWLH  Employees working very long hours   \n",
              "2366      LTU       Lithuania   WL_EWLH  Employees working very long hours   \n",
              "2367      COL        Colombia   WL_EWLH  Employees working very long hours   \n",
              "2368      LTU       Lithuania   WL_EWLH  Employees working very long hours   \n",
              "\n",
              "     MEASURE Measure INEQUALITY Inequality Unit Code        Unit  \\\n",
              "0          L   Value        TOT      Total        PC  Percentage   \n",
              "1          L   Value        TOT      Total        PC  Percentage   \n",
              "2          L   Value        TOT      Total        PC  Percentage   \n",
              "3          L   Value        TOT      Total        PC  Percentage   \n",
              "4          L   Value        TOT      Total        PC  Percentage   \n",
              "...      ...     ...        ...        ...       ...         ...   \n",
              "2364       L   Value        TOT      Total        PC  Percentage   \n",
              "2365       L   Value         MN        Men        PC  Percentage   \n",
              "2366       L   Value         MN        Men        PC  Percentage   \n",
              "2367       L   Value        WMN      Women        PC  Percentage   \n",
              "2368       L   Value        WMN      Women        PC  Percentage   \n",
              "\n",
              "      PowerCode Code PowerCode  Reference Period Code  Reference Period  \\\n",
              "0                  0     Units                    NaN               NaN   \n",
              "1                  0     Units                    NaN               NaN   \n",
              "2                  0     Units                    NaN               NaN   \n",
              "3                  0     Units                    NaN               NaN   \n",
              "4                  0     Units                    NaN               NaN   \n",
              "...              ...       ...                    ...               ...   \n",
              "2364               0     Units                    NaN               NaN   \n",
              "2365               0     Units                    NaN               NaN   \n",
              "2366               0     Units                    NaN               NaN   \n",
              "2367               0     Units                    NaN               NaN   \n",
              "2368               0     Units                    NaN               NaN   \n",
              "\n",
              "      Value  Flag Codes  Flags  \n",
              "0      5.40         NaN    NaN  \n",
              "1      3.50         NaN    NaN  \n",
              "2      3.70         NaN    NaN  \n",
              "3      6.00         NaN    NaN  \n",
              "4      3.10         NaN    NaN  \n",
              "...     ...         ...    ...  \n",
              "2364   0.54         NaN    NaN  \n",
              "2365  32.09         NaN    NaN  \n",
              "2366   0.67         NaN    NaN  \n",
              "2367  19.37         NaN    NaN  \n",
              "2368   0.43         NaN    NaN  \n",
              "\n",
              "[2369 rows x 17 columns]"
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              "      <td>Labour market insecurity</td>\n",
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              "      <td>Value</td>\n",
              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
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              "      <td>TOT</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>L</td>\n",
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              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
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              "      <td>Units</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.54</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2365</th>\n",
              "      <td>COL</td>\n",
              "      <td>Colombia</td>\n",
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              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
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              "      <td>32.09</td>\n",
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              "      <td>L</td>\n",
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              "      <td>19.37</td>\n",
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              "    <tr>\n",
              "      <th>2368</th>\n",
              "      <td>LTU</td>\n",
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              "      <td>0.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2369 rows × 17 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      try {\n",
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              "            '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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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"oecd_bli\",\n  \"rows\": 2369,\n  \"fields\": [\n    {\n      \"column\": \"LOCATION\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"SWE\",\n          \"ITA\",\n          \"DEU\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"Sweden\",\n          \"Italy\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDICATOR\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"JE_EMPL\",\n          \"EQ_WATER\",\n          \"JE_LMIS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Indicator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"Employment rate\",\n          \"Water quality\",\n          \"Labour market insecurity\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MEASURE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Measure\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Value\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INEQUALITY\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"MN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Inequality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Men\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"PC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PowerCode Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PowerCode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Units\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reference Period Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reference Period\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36765.671499134245,\n        \"min\": 0.0,\n        \"max\": 769053.0,\n        \"num_unique_values\": 898,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flag Codes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Flags\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_per_capita"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "p3Ri1VCbiw0R",
        "outputId": "d850051f-b60e-4ce4-dd03-c50384de869b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             GDP per capita (USD)\n",
              "Country                                          \n",
              "Afghanistan                           1978.961579\n",
              "Africa Eastern and Southern           3387.594670\n",
              "Africa Western and Central            4003.158913\n",
              "Albania                              13295.410885\n",
              "Algeria                              10681.679297\n",
              "...                                           ...\n",
              "Vanuatu                               2762.791388\n",
              "Vietnam                               8200.331867\n",
              "World                                16194.040310\n",
              "Zambia                                3270.035112\n",
              "Zimbabwe                              2744.690758\n",
              "\n",
              "[224 rows x 1 columns]"
            ],
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          "metadata": {},
          "execution_count": 16
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing:"
      ],
      "metadata": {
        "id": "HnT3cIzHihPW"
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    {
      "cell_type": "code",
      "source": [
        "gdp_year = 2020\n",
        "gdppc_col = \"GDP per capita (USD)\"\n",
        "lifesat_col = \"Life satisfaction\"\n",
        "\n",
        "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
        "gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
        "gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
        "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
        "\n",
        "gdp_per_capita.head()"
      ],
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        "id": "DizIT0ZBh6et",
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      "execution_count": 14,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                             GDP per capita (USD)\n",
              "Country                                          \n",
              "Afghanistan                           1978.961579\n",
              "Africa Eastern and Southern           3387.594670\n",
              "Africa Western and Central            4003.158913\n",
              "Albania                              13295.410885\n",
              "Algeria                              10681.679297"
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              "variable_name": "gdp_per_capita",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 224,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 224,\n        \"samples\": [\n          \"Armenia\",\n          \"Heavily indebted poor countries (HIPC)\",\n          \"Lesotho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18838.30235782942,\n        \"min\": 731.063229715588,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          42288.3480749219,\n          21608.4302710014,\n          4818.09473660531\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
        "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
        "\n",
        "oecd_bli.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 336
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        "id": "04alpMnAiljn",
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      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
              "Country                                                        \n",
              "Australia            5.0                                 NaN   \n",
              "Austria             16.0                                 0.9   \n",
              "Belgium             15.0                                 1.9   \n",
              "Brazil              10.0                                 6.7   \n",
              "Canada               7.0                                 0.2   \n",
              "\n",
              "Indicator  Educational attainment  Employees working very long hours  \\\n",
              "Country                                                                \n",
              "Australia                    81.0                              13.04   \n",
              "Austria                      85.0                               6.66   \n",
              "Belgium                      77.0                               4.75   \n",
              "Brazil                       49.0                               7.13   \n",
              "Canada                       91.0                               3.69   \n",
              "\n",
              "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
              "Country                                                           \n",
              "Australia             73.0                                 63.5   \n",
              "Austria               72.0                                 80.6   \n",
              "Belgium               63.0                                 70.1   \n",
              "Brazil                61.0                                 35.6   \n",
              "Canada                73.0                                 82.2   \n",
              "\n",
              "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
              "Country                                                              \n",
              "Australia            1.1                                   32759.0   \n",
              "Austria              0.5                                   33541.0   \n",
              "Belgium              1.0                                   30364.0   \n",
              "Brazil              26.7                                       NaN   \n",
              "Canada               1.3                                   30854.0   \n",
              "\n",
              "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
              "Country                                               ...                      \n",
              "Australia              427064.0                 20.0  ...            49126.0   \n",
              "Austria                308325.0                 21.0  ...            50349.0   \n",
              "Belgium                386006.0                 21.0  ...            49675.0   \n",
              "Brazil                      NaN                  NaN  ...                NaN   \n",
              "Canada                 423849.0                 22.0  ...            47622.0   \n",
              "\n",
              "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
              "Country                                                                         \n",
              "Australia                        95.0               NaN                  85.0   \n",
              "Austria                          92.0               1.6                  70.0   \n",
              "Belgium                          91.0               2.2                  74.0   \n",
              "Brazil                           90.0               NaN                   NaN   \n",
              "Canada                           93.0               2.6                  88.0   \n",
              "\n",
              "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
              "Country                                                                        \n",
              "Australia                                                2.7           502.0   \n",
              "Austria                                                  1.3           492.0   \n",
              "Belgium                                                  2.0           503.0   \n",
              "Brazil                                                   2.2           395.0   \n",
              "Canada                                                   2.9           523.0   \n",
              "\n",
              "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
              "Country                                                               \n",
              "Australia                                      14.35           91.0   \n",
              "Austria                                        14.55           80.0   \n",
              "Belgium                                        15.70           89.0   \n",
              "Brazil                                           NaN           79.0   \n",
              "Canada                                         14.56           68.0   \n",
              "\n",
              "Indicator  Water quality  Years in education  \n",
              "Country                                       \n",
              "Australia           93.0                21.0  \n",
              "Austria             92.0                17.0  \n",
              "Belgium             84.0                19.3  \n",
              "Brazil              73.0                16.2  \n",
              "Canada              91.0                17.3  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th>Employment rate</th>\n",
              "      <th>Feeling safe walking alone at night</th>\n",
              "      <th>Homicide rate</th>\n",
              "      <th>Household net adjusted disposable income</th>\n",
              "      <th>Household net wealth</th>\n",
              "      <th>Housing expenditure</th>\n",
              "      <th>...</th>\n",
              "      <th>Personal earnings</th>\n",
              "      <th>Quality of support network</th>\n",
              "      <th>Rooms per person</th>\n",
              "      <th>Self-reported health</th>\n",
              "      <th>Stakeholder engagement for developing regulations</th>\n",
              "      <th>Student skills</th>\n",
              "      <th>Time devoted to leisure and personal care</th>\n",
              "      <th>Voter turnout</th>\n",
              "      <th>Water quality</th>\n",
              "      <th>Years in education</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>81.0</td>\n",
              "      <td>13.04</td>\n",
              "      <td>73.0</td>\n",
              "      <td>63.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>32759.0</td>\n",
              "      <td>427064.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49126.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>502.0</td>\n",
              "      <td>14.35</td>\n",
              "      <td>91.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.66</td>\n",
              "      <td>72.0</td>\n",
              "      <td>80.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>33541.0</td>\n",
              "      <td>308325.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50349.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>492.0</td>\n",
              "      <td>14.55</td>\n",
              "      <td>80.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>15.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>77.0</td>\n",
              "      <td>4.75</td>\n",
              "      <td>63.0</td>\n",
              "      <td>70.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30364.0</td>\n",
              "      <td>386006.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49675.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>15.70</td>\n",
              "      <td>89.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>3.69</td>\n",
              "      <td>73.0</td>\n",
              "      <td>82.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>30854.0</td>\n",
              "      <td>423849.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47622.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>523.0</td>\n",
              "      <td>14.56</td>\n",
              "      <td>68.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 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-22c823f8-630b-4be6-98a7-50b46d06fb99')\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-22c823f8-630b-4be6-98a7-50b46d06fb99');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a4d38b2-3e56-427f-b618-3446b7df7056')\"\n",
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              "            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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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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",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a4d38b2-3e56-427f-b618-3446b7df7056 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "oecd_bli"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
        "                              left_index=True, right_index=True)\n",
        "full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
        "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
        "\n",
        "full_country_stats.head()\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "OL99_qPFl6f0",
        "outputId": "75b800a7-6da4-4dad-f195-19183a64fc91"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5"
            ],
            "text/html": [
              "\n",
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              "      <th>GDP per capita (USD)</th>\n",
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              "    <tr>\n",
              "      <th>Colombia</th>\n",
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              "      <td>6.3</td>\n",
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              "    <tr>\n",
              "      <th>Brazil</th>\n",
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              "        document.querySelector('#df-e2fdf147-866f-49ff-a2f5-31ba2f704a53 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e2fdf147-866f-49ff-a2f5-31ba2f704a53');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0eff03dd-399d-4aa1-8d9d-a45096acde29\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0eff03dd-399d-4aa1-8d9d-a45096acde29')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0eff03dd-399d-4aa1-8d9d-a45096acde29 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "full_country_stats",
              "summary": "{\n  \"name\": \"full_country_stats\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"Luxembourg\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19969.978699662497,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          110261.157353302,\n          36215.4475907307,\n          50922.3580234484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7758405579940837,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          4.7,\n          6.9,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_gdp = 23_500\n",
        "max_gdp = 62_500\n",
        "\n",
        "country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
        "                                   (full_country_stats[gdppc_col] <= max_gdp)]\n",
        "country_stats.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "0k8eXLOFmSGU",
        "outputId": "8390ef2e-aa5f-420e-be28-9857093ee665"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         GDP per capita (USD)  Life satisfaction\n",
              "Country                                         \n",
              "Russia           26456.387938                5.8\n",
              "Greece           27287.083401                5.4\n",
              "Turkey           28384.987785                5.5\n",
              "Latvia           29932.493910                5.9\n",
              "Hungary          31007.768407                5.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da5bb2f4-7e2c-4a9a-834d-2af49784adb4\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
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              "      <th>Country</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>26456.387938</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latvia</th>\n",
              "      <td>29932.493910</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da5bb2f4-7e2c-4a9a-834d-2af49784adb4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da5bb2f4-7e2c-4a9a-834d-2af49784adb4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da5bb2f4-7e2c-4a9a-834d-2af49784adb4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5f3b9c70-6b6e-49a8-82e0-dd7b4bf3a35f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f3b9c70-6b6e-49a8-82e0-dd7b4bf3a35f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5f3b9c70-6b6e-49a8-82e0-dd7b4bf3a35f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "country_stats",
              "summary": "{\n  \"name\": \"country_stats\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
        "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
      ],
      "metadata": {
        "id": "V8pPiQCOmzWF"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "min_life_sat = 4\n",
        "max_life_sat = 9\n",
        "\n",
        "position_text = {\n",
        "    \"Turkey\": (29_500, 4.2),\n",
        "    \"Hungary\": (28_000, 6.9),\n",
        "    \"France\": (40_000, 5),\n",
        "    \"New Zealand\": (28_000, 8.2),\n",
        "    \"Australia\": (50_000, 5.5),\n",
        "    \"United States\": (59_000, 5.3),\n",
        "    \"Denmark\": (46_000, 8.5)\n",
        "}\n",
        "\n",
        "for country, pos_text in position_text.items():\n",
        "    pos_data_x = country_stats[gdppc_col].loc[country]\n",
        "    pos_data_y = country_stats[lifesat_col].loc[country]\n",
        "    country = \"U.S.\" if country == \"United States\" else country\n",
        "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
        "                 xytext=pos_text, fontsize=12,\n",
        "                 arrowprops=dict(facecolor='black', width=0.5,\n",
        "                                 shrink=0.08, headwidth=5))\n",
        "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "plt.show()\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "awcnKzQNne4g",
        "outputId": "cae86d38-ac00-431b-f7da-c73febee1c75"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter and sort the DataFrame\n",
        "highlighted_countries = country_stats.loc[country_stats.index.intersection(position_text.keys())]\n",
        "sorted_highlighted_countries = highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)\n",
        "\n",
        "\n",
        "sorted_highlighted_countries.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "dDIEBtOZniwR",
        "outputId": "ff32cf65-a88a-4fbf-897e-7f219d63c6b3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             GDP per capita (USD)  Life satisfaction\n",
              "Country                                             \n",
              "Turkey               28384.987785                5.5\n",
              "Hungary              31007.768407                5.6\n",
              "France               42025.617373                6.5\n",
              "New Zealand          42404.393738                7.3\n",
              "Australia            48697.837028                7.3"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>42025.617373</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>48697.837028</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc31f458-c186-463c-90ed-9aceda781b2f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "        document.querySelector('#df-fc31f458-c186-463c-90ed-9aceda781b2f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc31f458-c186-463c-90ed-9aceda781b2f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7527eee5-753f-40b7-be73-2372f58ae516 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sorted_highlighted_countries",
              "summary": "{\n  \"name\": \"sorted_highlighted_countries\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Turkey\",\n          \"Hungary\",\n          \"Denmark\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11882.70029953928,\n        \"min\": 28384.9877846263,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28384.9877846263,\n          31007.7684065437,\n          55938.2128086032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8420496535523418,\n        \"min\": 5.5,\n        \"max\": 7.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.5,\n          5.6,\n          6.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "PLOT_WIDTH = 5\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "MIN_GDP = 23_500\n",
        "MAX_GDP = 62_500\n",
        "X = np.linspace(MIN_GDP, MAX_GDP, 1000)\n",
        "\n",
        "def plot_line(ax, w1, w2, color, x_pos, y_pos1, y_pos2):\n",
        "    ax.plot(X, w1 + w2 * 1e-5 * X, color)\n",
        "    ax.text(x_pos, y_pos1, fr\"$\\theta_0 = {w1}$\", color=color)\n",
        "    ax.text(x_pos, y_pos2, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=color)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, grid=True, ax=ax)\n",
        "\n",
        "plot_line(ax, 4.2, 0, \"r\", 40_000, 4.9, 4.4)\n",
        "plot_line(ax, 10, -9, \"g\", 26_000, 8.5, 8.0)\n",
        "plot_line(ax, 3, 8, \"b\", 48_000, 8.5, 8.0)\n",
        "\n",
        "\n",
        "ax.set_xlim([MIN_GDP, MAX_GDP])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Life Satisfaction vs GDP per Capita with Regression Lines\")\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "dP9MU82-nsvN",
        "outputId": "abc16561-600a-4adb-dc0b-6ccdbafb7224"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PLOT_WIDTH = 5\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "MIN_GDP = 23_500\n",
        "MAX_GDP = 62_500\n",
        "\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "linear_model = LinearRegression()\n",
        "linear_model.fit(X_sample, y_sample)\n",
        "theta_0 = linear_model.intercept_[0]\n",
        "theta_1 = linear_model.coef_[0][0]\n",
        "\n",
        "\n",
        "print(f\"θ0={theta_0:.2f}, θ1={theta_1:.2e}\")\n",
        "\n",
        "\n",
        "def plot_regression_line(ax, theta_0, theta_1, color, label_pos_x, label_pos_y):\n",
        "    X_range = np.linspace(MIN_GDP, MAX_GDP, 1000)\n",
        "    y_range = theta_0 + theta_1 * X_range\n",
        "    ax.plot(X_range, y_range, color, label=\"Regression Line\")\n",
        "    ax.text(label_pos_x, label_pos_y, fr\"$\\theta_0 = {theta_0:.2f}$\", color=color)\n",
        "    ax.text(label_pos_x, label_pos_y - 0.5, fr\"$\\theta_1 = {theta_1 * 1e5:.2f} \\times 10^{{-5}}$\", color=color)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, grid=True, ax=ax)\n",
        "\n",
        "\n",
        "plot_regression_line(ax, theta_0, theta_1, \"b\", MAX_GDP - 20_000, MIN_LIFE_SAT + 1.5)\n",
        "\n",
        "\n",
        "ax.set_xlim([MIN_GDP, MAX_GDP])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Life Satisfaction vs GDP per Capita with Regression Line\")\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "V2-z_O_vn9sr",
        "outputId": "768d2283-6677-49ab-f58b-b5f0a24e93eb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_gdp_per_capita"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrAHK6_8oZXR",
        "outputId": "6bd1c8d4-af1b-4576-d008-ddfa0b931267"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PLOT_WIDTH = 5\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "MIN_GDP = 23_500\n",
        "MAX_GDP = 62_500\n",
        "\n",
        "def plot_regression_line(ax, theta_0, theta_1, color, label_pos_x, label_pos_y):\n",
        "    X_range = np.linspace(MIN_GDP, MAX_GDP, 1000)\n",
        "    y_range = theta_0 + theta_1 * X_range\n",
        "    ax.plot(X_range, y_range, color, label=\"Regression Line\")\n",
        "    ax.text(label_pos_x, label_pos_y, fr\"$\\theta_0 = {theta_0:.2f}$\", color=color)\n",
        "    ax.text(label_pos_x, label_pos_y - 0.5, fr\"$\\theta_1 = {theta_1 * 1e5:.2f} \\times 10^{{-5}}$\", color=color)\n",
        "\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "\n",
        "linear_model = LinearRegression()\n",
        "linear_model.fit(X_sample, y_sample)\n",
        "theta_0 = linear_model.intercept_[0]\n",
        "theta_1 = linear_model.coef_[0][0]\n",
        "\n",
        "\n",
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_predicted_life_satisfaction = linear_model.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, grid=True, ax=ax)\n",
        "\n",
        "\n",
        "plot_regression_line(ax, theta_0, theta_1, \"b\", MAX_GDP - 20_000, MIN_LIFE_SAT + 1.5)\n",
        "\n",
        "\n",
        "ax.plot([cyprus_gdp_per_capita], [cyprus_predicted_life_satisfaction], \"ro\")\n",
        "ax.text(cyprus_gdp_per_capita + 1000, cyprus_predicted_life_satisfaction,\n",
        "        fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
        "ax.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
        "        [MIN_LIFE_SAT, cyprus_predicted_life_satisfaction], \"r--\")\n",
        "\n",
        "ax.set_xlim([MIN_GDP, MAX_GDP])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Life Satisfaction vs GDP per Capita with Regression Line\")\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "KUY04k_rqwpp",
        "outputId": "7f00786d-cd67-48b6-ea62-7e8a612bd790"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
        "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
        "missing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "haFVJptDrENb",
        "outputId": "fa4cc21d-06b1-4f10-879a-672202da86a7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5\n",
              "Norway                63585.903514                7.6\n",
              "Switzerland           68393.306004                7.5\n",
              "Ireland               89688.956958                7.0\n",
              "Luxembourg           110261.157353                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c66bcb8c-b642-4724-b8d6-397984d8158d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>11466.189672</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>13441.492952</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>14063.982505</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>17887.750736</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>63585.903514</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>68393.306004</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>89688.956958</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>110261.157353</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                    [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",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\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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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('missing_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
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              "        document.querySelector('#id_94a55bbe-4075-422b-9b08-d4840d7ee970 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing_data');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_data",
              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Ireland\",\n          \"Colombia\",\n          \"Norway\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37791.49262311812,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          89688.9569584859,\n          13441.4929522564,\n          63585.9035142969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.852936105461599,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.3,\n          7.5,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_text_missing_countries = {\n",
        "    \"South Africa\": (20_000, 4.2),\n",
        "    \"Colombia\": (6_000, 8.2),\n",
        "    \"Brazil\": (18_000, 7.8),\n",
        "    \"Mexico\": (24_000, 7.4),\n",
        "    \"Chile\": (30_000, 7.0),\n",
        "    \"Norway\": (51_000, 6.2),\n",
        "    \"Switzerland\": (62_000, 5.7),\n",
        "    \"Ireland\": (81_000, 5.2),\n",
        "    \"Luxembourg\": (92_000, 4.7),\n",
        "}"
      ],
      "metadata": {
        "id": "mOrLozHYrFht"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PLOT_WIDTH = 8\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "\n",
        "\n",
        "def plot_regression_line(ax, theta_0, theta_1, color, label):\n",
        "    X_range = np.linspace(0, 115_000, 1000)\n",
        "    y_range = theta_0 + theta_1 * X_range\n",
        "    ax.plot(X_range, y_range, color, label=label)\n",
        "\n",
        "\n",
        "def annotate_countries(ax, country_data, position_text):\n",
        "    for country, pos_text in position_text.items():\n",
        "        pos_data_x = country_data.loc[country, gdppc_col]\n",
        "        pos_data_y = country_data.loc[country, lifesat_col]\n",
        "        ax.annotate(country, xy=(pos_data_x, pos_data_y),\n",
        "                    xytext=pos_text, fontsize=12,\n",
        "                    arrowprops=dict(facecolor='black', width=0.5,\n",
        "                                    shrink=0.08, headwidth=5))\n",
        "        ax.plot(pos_data_x, pos_data_y, \"rs\")\n",
        "\n",
        "\n",
        "X_full = full_country_stats[[gdppc_col]].values\n",
        "y_full = full_country_stats[[lifesat_col]].values\n",
        "\n",
        "\n",
        "lin_reg_full = LinearRegression()\n",
        "lin_reg_full.fit(X_full, y_full)\n",
        "theta_0_full = lin_reg_full.intercept_[0]\n",
        "theta_1_full = lin_reg_full.coef_[0][0]\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "full_country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, grid=True, ax=ax)\n",
        "\n",
        "\n",
        "annotate_countries(ax, missing_data, position_text_missing_countries)\n",
        "\n",
        "\n",
        "plot_regression_line(ax, theta_0, theta_1, \"b\", \"Initial Model\")\n",
        "plot_regression_line(ax, theta_0_full, theta_1_full, \"k\", \"Full Model\")\n",
        "\n",
        "\n",
        "ax.set_xlim([0, 115_000])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Life Satisfaction vs GDP per Capita with Regression Lines\")\n",
        "\n",
        "\n",
        "plt.savefig('representative_training_data_scatterplot')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "5Vsc1Q5ErMaB",
        "outputId": "fbd2f2e2-6191-466d-87fb-46c6e8445fc6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Overfitting:\n"
      ],
      "metadata": {
        "id": "3OsL5L29rsZV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing, pipeline, linear_model\n",
        "\n",
        "PLOT_WIDTH = 8\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "\n",
        "\n",
        "def plot_data(df, x_col, y_col, ax):\n",
        "    df.plot(kind='scatter', x=x_col, y=y_col, grid=True, ax=ax)\n",
        "\n",
        "\n",
        "def fit_and_plot_poly_regression(ax, X, y, degree=10):\n",
        "    # Define the pipeline\n",
        "    poly = preprocessing.PolynomialFeatures(degree=degree, include_bias=False)\n",
        "    scaler = preprocessing.StandardScaler()\n",
        "    lin_reg = linear_model.LinearRegression()\n",
        "    pipeline_reg = pipeline.Pipeline([\n",
        "        ('poly', poly),\n",
        "        ('scal', scaler),\n",
        "        ('lin', lin_reg)\n",
        "    ])\n",
        "\n",
        "\n",
        "    pipeline_reg.fit(X, y)\n",
        "\n",
        "\n",
        "    X_range = np.linspace(0, 115_000, 1000).reshape(-1, 1)\n",
        "    curve = pipeline_reg.predict(X_range)\n",
        "\n",
        "\n",
        "    ax.plot(X_range, curve, label=f'Polynomial Regression (degree={degree})')\n",
        "\n",
        "\n",
        "X_full = full_country_stats[[gdppc_col]].values\n",
        "y_full = full_country_stats[[lifesat_col]].values\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "\n",
        "\n",
        "plot_data(full_country_stats, gdppc_col, lifesat_col, ax)\n",
        "\n",
        "\n",
        "fit_and_plot_poly_regression(ax, X_full, y_full, degree=10)\n",
        "\n",
        "ax.set_xlim([0, 115_000])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "plt.title(\"Life Satisfaction vs GDP per Capita with Polynomial Regression\")\n",
        "\n",
        "\n",
        "plt.savefig('overfitting_model_plot')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "tG93Cjt-rxW9",
        "outputId": "5d6ddcc8-c057-4307-cb30-48a73c8f3f6e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
        "full_country_stats.loc[w_countries][lifesat_col]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Al2-uiBTsT7d",
        "outputId": "3c9e16a8-9007-473a-b4a4-767bca60b2d7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "New Zealand    7.3\n",
              "Sweden         7.3\n",
              "Norway         7.6\n",
              "Switzerland    7.5\n",
              "Name: Life satisfaction, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
        "gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "MhWsB9ggsVK4",
        "outputId": "de6037b8-ecf9-4adb-f306-2ebcc999fb7f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            GDP per capita (USD)\n",
              "Country                                         \n",
              "Malawi                               1486.778248\n",
              "Rwanda                               2098.710362\n",
              "Zimbabwe                             2744.690758\n",
              "Africa Western and Central           4003.158913\n",
              "Papua New Guinea                     4101.218882\n",
              "Lower middle income                  6722.809932\n",
              "Eswatini                             8392.717564\n",
              "Low & middle income                 10293.855325\n",
              "Arab World                          13753.707307\n",
              "Botswana                            16040.008473\n",
              "World                               16194.040310\n",
              "New Zealand                         42404.393738\n",
              "Sweden                              50683.323510\n",
              "Norway                              63585.903514\n",
              "Switzerland                         68393.306004"
            ],
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              "      <th>GDP per capita (USD)</th>\n",
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              "      <th>Malawi</th>\n",
              "      <td>1486.778248</td>\n",
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              "      <td>2098.710362</td>\n",
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              "      <td>16040.008473</td>\n",
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              "      <th>World</th>\n",
              "      <td>16194.040310</td>\n",
              "    </tr>\n",
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              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Botswana\",\n          \"New Zealand\",\n          \"Malawi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23331.713640984548,\n        \"min\": 1486.77824843475,\n        \"max\": 68393.306003945,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          16040.0084730125,\n          42404.3937381567,\n          1486.77824843475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "\n",
        "PLOT_WIDTH = 8\n",
        "PLOT_HEIGHT = 3\n",
        "MIN_LIFE_SAT = 4\n",
        "MAX_LIFE_SAT = 9\n",
        "GDP_RANGE = np.linspace(0, 115_000, 1000)\n",
        "\n",
        "\n",
        "def plot_scatter_data(ax, df, x_col, y_col, marker='o', color='b', label=None):\n",
        "    df.plot(kind='scatter', x=x_col, y=y_col, marker=marker, color=color, grid=True, ax=ax, label=label)\n",
        "\n",
        "\n",
        "def plot_regression_line(ax, X, intercept, coef, style, label):\n",
        "    y_pred = intercept + coef * X\n",
        "    ax.plot(X, y_pred, style, label=label)\n",
        "\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "\n",
        "lin_reg = linear_model.LinearRegression()\n",
        "lin_reg.fit(X_sample, y_sample)\n",
        "t0, t1 = lin_reg.intercept_[0], lin_reg.coef_[0][0]\n",
        "\n",
        "\n",
        "X_full = full_country_stats[[gdppc_col]].values\n",
        "y_full = full_country_stats[[lifesat_col]].values\n",
        "lin_reg_full = linear_model.LinearRegression()\n",
        "lin_reg_full.fit(X_full, y_full)\n",
        "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
        "\n",
        "\n",
        "ridge = linear_model.Ridge(alpha=10**9.5)\n",
        "ridge.fit(X_sample, y_sample)\n",
        "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(PLOT_WIDTH, PLOT_HEIGHT))\n",
        "\n",
        "\n",
        "plot_scatter_data(ax, country_stats, gdppc_col, lifesat_col)\n",
        "plot_scatter_data(ax, missing_data, gdppc_col, lifesat_col, marker='s', color='r', label='Missing data')\n",
        "\n",
        "\n",
        "plot_regression_line(ax, GDP_RANGE, t0, t1, \"b:\", \"Linear model on partial data\")\n",
        "plot_regression_line(ax, GDP_RANGE, t0full, t1full, \"k-\", \"Linear model on all data\")\n",
        "plot_regression_line(ax, GDP_RANGE, t0ridge, t1ridge, \"b--\", \"Regularized linear model on partial data\")\n",
        "\n",
        "\n",
        "ax.set_xlim([0, 115_000])\n",
        "ax.set_ylim([MIN_LIFE_SAT, MAX_LIFE_SAT])\n",
        "ax.set_xlabel(\"GDP per capita (USD)\")\n",
        "ax.set_ylabel(\"Life satisfaction\")\n",
        "ax.set_title(\"Life Satisfaction vs GDP per Capita with Linear Models\")\n",
        "\n",
        "\n",
        "ax.legend(loc=\"lower right\")\n",
        "\n",
        "\n",
        "plt.savefig('ridge_model_plot')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "rIz1qjUAs3pK",
        "outputId": "83296bf2-75c4-48c6-f422-bc8ce66c1a95"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}