Subina_Chapter_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ],
      "metadata": {
        "id": "soZCpdYpvKdM"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tfnzA2V-vHQN"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Update rcParams using a dictionary\n",
        "plt.rcParams.update({\n",
        "    'font.size': 12,\n",
        "    'axes.labelsize': 14,\n",
        "    'axes.titlesize': 14,\n",
        "    'legend.fontsize': 12,\n",
        "    'xtick.labelsize': 10,\n",
        "    'ytick.labelsize': 10\n",
        "})\n"
      ],
      "metadata": {
        "id": "RXiCSobWv-y1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(100)"
      ],
      "metadata": {
        "id": "lYYNRtV0v_AI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Download and prepare the data\n",
        "data_url = \"https://github.com/ageron/data/raw/main/lifesat/lifesat.csv\"\n",
        "lifesat = pd.read_csv(data_url)\n",
        "X = lifesat[\"GDP per capita (USD)\"].values.reshape(-1, 1)\n",
        "y = lifesat[\"Life satisfaction\"].values.reshape(-1, 1)\n",
        "\n",
        "# Visualize the data\n",
        "plt.scatter(X, y, marker='s', color='orange')\n",
        "plt.title('Life Satisfaction vs GDP per capita')\n",
        "plt.xlabel('GDP per capita (USD)')\n",
        "plt.ylabel('Life satisfaction')\n",
        "plt.axis([23500, 62500, 4, 9])\n",
        "plt.show()\n",
        "\n",
        "# Select and train a linear model\n",
        "model = LinearRegression().fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "X_new = np.array([[37655.2]])  # Cyprus' GDP per capita in 2020\n",
        "prediction = model.predict(X_new)\n",
        "print(prediction)  # outputs [[6.30165767]]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "pGK7OnK3v_Oo",
        "outputId": "587b436f-2967-4e9e-f742-c4178c0a9369"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Replacing the Linear Regression model with k-Nearest Neighbors, k = 3) **"
      ],
      "metadata": {
        "id": "NAKit7Kayh66"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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)) # outputs [[6.33333333]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvaPGwr3v_b_",
        "outputId": "277b3f19-2403-40e6-997c-0cf6b43d25ad"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load and prepare Life satisfaction data**"
      ],
      "metadata": {
        "id": "KZV8XuF40Fci"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "from pathlib import Path\n",
        "\n",
        "\n",
        "datapath = Path() / \"datasets\" / \"lifesat\"\n",
        "datapath.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "# Define the root URL for the data\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "\n",
        "# List of filenames to download\n",
        "filenames = [\"oecd_bli.csv\", \"gdp_per_capita.csv\"]\n",
        "\n",
        "# Download the files if they do not already exist\n",
        "for filename in filenames:\n",
        "    file_path = datapath / filename\n",
        "    if not file_path.is_file():\n",
        "        print(\"Downloading\", filename)\n",
        "        url = data_root + \"lifesat/\" + filename\n",
        "        try:\n",
        "            urllib.request.urlretrieve(url, file_path)\n",
        "            print(f\"Successfully downloaded {filename}\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error downloading {filename}: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IwX1rmyz_SH",
        "outputId": "bbcf88de-ba8f-4459-b8c8-47532b23ebef"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading oecd_bli.csv\n",
            "Successfully downloaded oecd_bli.csv\n",
            "Downloading gdp_per_capita.csv\n",
            "Successfully downloaded gdp_per_capita.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "oecd_bli"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "w8hSQGap09WK",
        "outputId": "06433ecb-792e-4b19-8d8a-a5e55d204614"
      },
      "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]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>LOCATION</th>\n",
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              "      <th>MEASURE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AUS</td>\n",
              "      <td>Australia</td>\n",
              "      <td>JE_LMIS</td>\n",
              "      <td>Labour market insecurity</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
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              "      <td>PC</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUT</td>\n",
              "      <td>Austria</td>\n",
              "      <td>JE_LMIS</td>\n",
              "      <td>Labour market insecurity</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <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>3.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BEL</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>JE_LMIS</td>\n",
              "      <td>Labour market insecurity</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CAN</td>\n",
              "      <td>Canada</td>\n",
              "      <td>JE_LMIS</td>\n",
              "      <td>Labour market insecurity</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CZE</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>JE_LMIS</td>\n",
              "      <td>Labour market insecurity</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2364</th>\n",
              "      <td>LTU</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>WL_EWLH</td>\n",
              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>TOT</td>\n",
              "      <td>Total</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.54</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2365</th>\n",
              "      <td>COL</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>WL_EWLH</td>\n",
              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>MN</td>\n",
              "      <td>Men</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2366</th>\n",
              "      <td>LTU</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>WL_EWLH</td>\n",
              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>MN</td>\n",
              "      <td>Men</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.67</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2367</th>\n",
              "      <td>COL</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>WL_EWLH</td>\n",
              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>WMN</td>\n",
              "      <td>Women</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2368</th>\n",
              "      <td>LTU</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>WL_EWLH</td>\n",
              "      <td>Employees working very long hours</td>\n",
              "      <td>L</td>\n",
              "      <td>Value</td>\n",
              "      <td>WMN</td>\n",
              "      <td>Women</td>\n",
              "      <td>PC</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>Units</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2369 rows × 17 columns</p>\n",
              "</div>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
              "  <div id=\"id_06abdca1-9b70-4af3-9307-0cc092732945\">\n",
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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": "markdown",
      "source": [
        "# **Preprocess the GDP per capita data to keep only the year 2020:**"
      ],
      "metadata": {
        "id": "qJKxnpdw1LaE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")\n",
        "gdp_per_capita"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "JrfRwMQY1sq6",
        "outputId": "0a7e5dc7-f002-40af-c3d3-20339c20f2ec"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Entity Code  Year  \\\n",
              "0     Afghanistan  AFG  2002   \n",
              "1     Afghanistan  AFG  2003   \n",
              "2     Afghanistan  AFG  2004   \n",
              "3     Afghanistan  AFG  2005   \n",
              "4     Afghanistan  AFG  2006   \n",
              "...           ...  ...   ...   \n",
              "7104     Zimbabwe  ZWE  2016   \n",
              "7105     Zimbabwe  ZWE  2017   \n",
              "7106     Zimbabwe  ZWE  2018   \n",
              "7107     Zimbabwe  ZWE  2019   \n",
              "7108     Zimbabwe  ZWE  2020   \n",
              "\n",
              "      GDP per capita, PPP (constant 2017 international $)  \n",
              "0                                           1189.784668    \n",
              "1                                           1235.810063    \n",
              "2                                           1200.278013    \n",
              "3                                           1286.793659    \n",
              "4                                           1315.789117    \n",
              "...                                                 ...    \n",
              "7104                                        3173.610829    \n",
              "7105                                        3274.611198    \n",
              "7106                                        3341.665418    \n",
              "7107                                        3027.656038    \n",
              "7108                                        2744.690758    \n",
              "\n",
              "[7109 rows x 4 columns]"
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdp_per_capita",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 7109,\n  \"fields\": [\n    {\n      \"column\": \"Entity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 240,\n        \"samples\": [\n          \"Bolivia\",\n          \"Antigua and Barbuda\",\n          \"IBRD only\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"PHL\",\n          \"BEL\",\n          \"SYC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1990,\n        \"max\": 2020,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          1998,\n          2017,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita, PPP (constant 2017 international $)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19167.65069513024,\n        \"min\": 1.96015186935512,\n        \"max\": 161971.034869809,\n        \"num_unique_values\": 7050,\n        \"samples\": [\n          3878.8298699884,\n          3195.78104025627,\n          9732.45128381993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "e8o1a0uV1vEQ",
        "outputId": "4c838e88-4e8e-4450-90b7-89b40e80039e"
      },
      "execution_count": 20,
      "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"
            ],
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              "      <th>Afghanistan</th>\n",
              "      <td>1978.961579</td>\n",
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              "      <td>4003.158913</td>\n",
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              "      <th>Albania</th>\n",
              "      <td>13295.410885</td>\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "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}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocess the OECD BLI data to keep only the Life satisfaction column:"
      ],
      "metadata": {
        "id": "ToK1JwiY16gc"
      }
    },
    {
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "aOufNzQm24ll",
        "outputId": "dd6c5fae-0592-443b-d8b6-4581fc7e3f3b"
      },
      "execution_count": 21,
      "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",
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              "      <td>7.13</td>\n",
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              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "oecd_bli"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# merging the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
      ],
      "metadata": {
        "id": "PxMZn1S43JON"
      }
    },
    {
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "yDi5CvFy3Mc-",
        "outputId": "6f107a85-baa3-46aa-c164-166f61e3fdc1"
      },
      "execution_count": 23,
      "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>Colombia</th>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb724f1a-3ade-4fb5-bf0a-8fab0ce8c735')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eb724f1a-3ade-4fb5-bf0a-8fab0ce8c735 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-eb724f1a-3ade-4fb5-bf0a-8fab0ce8c735');\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-749676ea-8d09-44fd-a922-c67502ef2700\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-749676ea-8d09-44fd-a922-c67502ef2700')\"\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-749676ea-8d09-44fd-a922-c67502ef2700 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": 23
        }
      ]
    },
    {
      "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": "hJyQ2qUl3-F9",
        "outputId": "74fd35c6-7d90-483e-e97c-4ae9553d21be"
      },
      "execution_count": 24,
      "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-4fbca0d9-132f-4837-b068-52bfa3239009\" 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>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",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fbca0d9-132f-4837-b068-52bfa3239009')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4fbca0d9-132f-4837-b068-52bfa3239009 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-4fbca0d9-132f-4837-b068-52bfa3239009');\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-a5e1466a-91fc-49c7-9fa3-b37c86f0cef4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a5e1466a-91fc-49c7-9fa3-b37c86f0cef4')\"\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-a5e1466a-91fc-49c7-9fa3-b37c86f0cef4 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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
        "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
      ],
      "metadata": {
        "id": "kRqxIuzY4IAm"
      },
      "execution_count": 25,
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "XiKYC-kX4IZ-",
        "outputId": "584df8a4-e95f-4f8b-acf7-70eb07366cb0"
      },
      "execution_count": 27,
      "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": [
        "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
        "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IcYHb4Ba4sKD",
        "outputId": "d08e7e22-bc11-4327-c4e2-0f0c1b7deb45"
      },
      "execution_count": 28,
      "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\n",
              "Denmark                55938.212809                7.6\n",
              "United States          60235.728492                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d1e2ca77-942b-4840-8a3f-70b48d242d12\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>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",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>55938.212809</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>60235.728492</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"highlighted_countries[[gdppc_col, lifesat_col]]\",\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "\n",
        "w1, w2 = 4.2, 0\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
        "plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
        "plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
        "\n",
        "w1, w2 = 10, -9\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
        "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
        "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
        "\n",
        "w1, w2 = 3, 8\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
        "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
        "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "z_bx4pBB5yPk",
        "outputId": "a9e66d0e-cfd8-4106-e727-6dd6699f4220"
      },
      "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": [],
      "metadata": {
        "id": "7FQ6JRPM57LT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "lin1 = linear_model.LinearRegression()\n",
        "lin1.fit(X_sample, y_sample)\n",
        "\n",
        "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
        "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUNpF5ax57dU",
        "outputId": "6cf95f71-da28-4fcb-a4fd-262b9b53188f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b\")\n",
        "\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.9,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"g\")\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"r\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "6lPBGcFy6GNU",
        "outputId": "50cb14f4-05f8-4009-e7cc-1765fb7a73e7"
      },
      "execution_count": 35,
      "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": [
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_gdp_per_capita"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxMZKpXd6WHR",
        "outputId": "deb2dfc9-82b0-4ab6-c869-9c5b60376d6d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
        "cyprus_predicted_life_satisfaction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhRwpOxx6Xss",
        "outputId": "55516e51-4b78-4172-cbfa-7369b0564e13"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.301656332738056"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b\")\n",
        "\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 1.1,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"b\")\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
        "         [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
        "plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
        "         fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
        "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "PvbHq1r-6gTM",
        "outputId": "460a0f17-77d9-4ed4-a6c2-60ecf90f9978"
      },
      "execution_count": 38,
      "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": 363
        },
        "id": "nmlKPHwE6iq8",
        "outputId": "b69ed402-2aaa-4127-f293-9016f76d0658"
      },
      "execution_count": 39,
      "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-9eeb0809-552b-44aa-8881-b12934943ecc\" 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",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eeb0809-552b-44aa-8881-b12934943ecc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9eeb0809-552b-44aa-8881-b12934943ecc 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-9eeb0809-552b-44aa-8881-b12934943ecc');\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-89fcf6db-35c6-49e3-8c0a-03174b6c9e87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89fcf6db-35c6-49e3-8c0a-03174b6c9e87')\"\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-89fcf6db-35c6-49e3-8c0a-03174b6c9e87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c095518f-08e2-47bc-8971-813b3f104010\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c095518f-08e2-47bc-8971-813b3f104010 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",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "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": 39
        }
      ]
    },
    {
      "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": "iN3F4tXY7H_Y"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
        "\n",
        "for country, pos_text in position_text_missing_countries.items():\n",
        "    pos_data_x, pos_data_y = missing_data.loc[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, \"rs\")\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b:\")\n",
        "\n",
        "lin_reg_full = linear_model.LinearRegression()\n",
        "Xfull = np.c_[full_country_stats[gdppc_col]]\n",
        "yfull = np.c_[full_country_stats[lifesat_col]]\n",
        "lin_reg_full.fit(Xfull, yfull)\n",
        "\n",
        "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0full + t1full * X, \"k\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "QVKuCQVB7OWw",
        "outputId": "c93d57b3-8c03-46b4-caec-89efe6e54068"
      },
      "execution_count": 41,
      "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": [
        "from sklearn import preprocessing\n",
        "from sklearn import pipeline\n",
        "\n",
        "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
        "\n",
        "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
        "scaler = preprocessing.StandardScaler()\n",
        "lin_reg2 = linear_model.LinearRegression()\n",
        "\n",
        "pipeline_reg = pipeline.Pipeline([\n",
        "    ('poly', poly),\n",
        "    ('scal', scaler),\n",
        "    ('lin', lin_reg2)])\n",
        "pipeline_reg.fit(Xfull, yfull)\n",
        "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
        "plt.plot(X, curve)\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "LqsQL7U-7dLe",
        "outputId": "53da6dfd-683f-4fe1-83ff-7e35b5fb5034"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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Q4YGR4Yh9egzuGxFulkQVsI3FZtRynFklczFpZnXNmjUIDQ3F1KlTGz3m6NGjWLt2Lb744guTgyMiksoOSi1ZECWVmE1Voa3G57uT8VHMOZRW1rQfurF3IJ69IQohFkgcG/vZAjX1sP5uarO/JlmeYYEVa1ZtkrD1HayMUVFRAaWSVQZE1DpS2kFp5fT+GN7Ft859DS2IklLMLaHTCfwSn45x7+zA238moLSyGn07eeD7h4bio5kDLZKo6jX0s1UpZCitrMbzPx+32OuS5XBTADIXk7PJxmoahBC4cOEC/vjjDwQFBZkcGBGR1HZ5MmZBlNRiNtaBlDws33QKRy8WAgCCPBzxzA1RmNo3CHK55WvYGvrZ5pZocNen+/BL/CWMjfLHtH4dLR4HmY+GravITIxOVuVyeZ0EddmyZVi2bFmjxwsh8Oyzz7YqOCKyfa1p8m7OhTfGxmHMcU0tiLK1xUIpOaV4448z2HIyE0DNx+4LRkfgvhHhcFSZpya1Ja7+2Yb5uuCRsV3w3rZEvPjzCQwK9UZHT6c2j4lMo2XrKptmk90ARo4caUhWd+7cieDgYISGhtY7TqFQwNvbG2PHjsX9999vtkCJyLaYo26ztQtvtNU6nEwvxAs/n8DJjCLD/R08HDGyqx+cVAqoFDJ4OjvAQSHDT4cv4XTmleNMqTO1lcVCWUUV+Cj2PL7enwpttYBcBtwVHYzFE7rCT0I1oovGdMHOs9k4nFaAZb+exP9mD7J2SGQkfbKq5syqTZFgMwDjk9WrNwKQy+WYO3culixZYomYiMgOmKOBfkt3ecooLMf+pDzEpeThYEoezmeXolpXf3ogo7AC3x640Ozr70zMxg3v78Sj4yLRM8gdXQPcmp1tlPrOVNnFGnyy4zy+2pdq+Jh2VFc/PH9jd3QLdLNqbA1RKuR48/Y+mPz+Lmw9dRl/n7mMsVEB1g6LjHCldRWTVWodk2pWuSkAETXFnHWbze3ydCGvDJuPZ2DziUwcvVDQojhnXVfTJ/RiXhk2n8hs8JiMwgr8+6eaBT4qhQy9OnogOtQb0aHeGBTiBS8XhxbHbA0pOaVYvScZ3x28iHJtzQr/gSFeWDyha72FTVITGeCG+deHYdXOJCz99SSGRfhapUSBjKfTCVTV/qHImlVqLZOS1YsXL+Lw4cMYOXIkPD096z2en5+PXbt2YeDAgejYkQXxRO2NOes2G1p4E+ztjNiELKzdm4qdZ68khDIZ0EefTIZ5o0RThSdrtwRtyNju/hjTzR8xCVmNJqsA0CPIHRkF5cgv0+JIWgGOpBXg051JAIBIf1dEh3ljcKg3Bod5I8jTSTI7UwkhkFgow4Kvj2B7QrahBq1fZ088MaErRkb6SrIBeEMeHReJX49ewoW8cqzbm4IHRkZYOyRqgn6rVYCbAtgqCZWsmpasLl++HN9//z0uXaq/PR4AODs7Y968ebj77rvxwQcftCpAImobrVkIdS1L1G2G+bqgs5cTfjqcjjlfxCGtNiGWyYDrwnxwY58OmNQzAP5ujobnJGWXGBVHc/F+OGMAQn2ccTG/HAdS8nAgJQ9xyTVlBolZJUjMKsE3+9MAAB09nTA4rGbmdXCYN0Z39WvzhDAttww/Hr6Inw5fxIV8BYCahH5MNz/Mvz4cw7v42EySqueiVmLxhK54+odj+Cj2PKYPDoabiVu8kuVp6ySrnFm1JTJI773BpGT177//xsSJE6FWN1yEr1arMXHiRGzbtq1VwRGR5Vmigb256zZ1OoHfjl3Ce9sSDbtBeTipcFd0Z9wzJATBPg0nm8bGYexxnb2d0dnbGbcN6AQAyC3R4GBqPg4k1ySwJy4VIb2gHD8fScfPR9IBAD4uDhgU6oXoUG8MCPFCVKAbnB3M24O6skqH4+kF+PtMFrafzsKZzGLDY2q5wG0DO2P+iAh08Xc16+u2tVv7d8QnO87jfHYpPtuVjCcmdLV2SNQIfb0qwE0BqPVMesdMT0/H7bff3uQxISEh+O2330wKiojajjkWQjXEXHWbJy8V4sWNJ3AkrQAA4O3igIdHRxi9L72xcZgSr4+rGpN6BmJSz0AAQKmmCofTapLX/cl5iL9QgNzSSvx58jL+PHkZQM1McJiPC7p3cEe3QDcEezujk5cTOns7w89V3WRPU51OILe0Eim5pUjOKcW5rBIcScvHsYuFhsVSACCX1cQ+rW8HiLQjuOXmHlCpbH8WUqmQ48mJ3fDw14fx2a4k3DsstMGaYbI+/YYASrmsTfr0kvnZZOuqqzk4OKCoqKjJY4qKimzuYyai9saSDexbW7dZoqnCO38lYO0/KdCJmv6fD42KwNzrw+CqNv6ty9g4zFFn6qJWYkSkH0ZE+gEANFXVOJFeiP3JeTiQXDPzml2sQVJOKZJySvH78Yw6z5fJAFe1Eh5OKjipFIYWMpVVOhSWa1FYrkUDzQ0AAJ7OKoyI9MO4KH+M6uoHLxcHaLVabE6X9q5ZLXVDz0B07+CO0xlF+GpfKh4ZF2ntkKgBldwQwGZJMXUzKVnt3bs3fvvtN7z77rsNlgJUVFTg119/Re/evVsdIBFZTls0sG+qgX5jDqXm4fFv43EhrxwAMKVPB7w0pQcCPRybeWbr4zAl3saolQoMDPHGwBBvYHTNfdnFGpzOKMLpjCIkZpXgYn4ZLuaXI6OwAtU6geKKKhRXVDV6TpkMCPJwMsTZp5MHBoR4IdzXpV1MEMjlMjw0KhyPbYjH2r0puH+kdTYvoKZVckMAMiOTktW5c+di/vz5mDp1Kj7++GOEh4cbHjt//jwefvhhXLp0Ca+88orZAiUi85NaA3tttQ4rtyfig5hz0ImaxUqv3dYbo7r6tWkcjTHHIjQ/NzX83Pww8prvqapah/wyLYoqamZQKyqroV/noFLI4emkgoezCp5ODu1+turG3h3w1pYEpBeU46fD6ZgxJNjaIdE19Aus2vu1asuEhPoBmJysbt68GT/++COioqIQFhaGjh07Ij09HcnJyaiqqsJdd92FuXPnmjteIjIjKTWwzyqqwMNfH8bB1HwAwG39O2LZtJ5wl8CKb0ssQruWUiGvTWSls3uUVKkUcsy7PgyvbjqF/+1Kwt3RnVkXKTGGMgDOrNocKf5PMvkq+u677/Df//4XXbp0QWJiImJjY5GYmIiuXbviww8/xPr1680ZJxFZyMrp/es1hW/rBvaHUvNw08rdOJiaDze1Eiun98e7d/WTRKIKNL0Ijazj7ujOcFMrkZxTij3nc5p/ArUpzqySOZncP0Umk2HRokVYtGgRSktLUVhYCA8PD7i4SGPfayIyjrUb2K+PS8OSX05AWy0Q6e+KT2cPsvqWpFez5CI0Mp2LWonbBnTE2r2p+HpfmmFRG0nDla1WpThPR0aRThWA6TOrV3NxcUFQUJBZEtXQ0FDIZLJ6t4ULF5ohUiJqTJivC8Z082+zxEunE3hzyxn8+6fj0FYL3Ng7EBsXDpdM4peUXYKYhCzEJec1eVxKbmkbRUTXmjEkBACw9fRlXC6qsHI0dDX9AqsKrc7QG5lsgxTXaZq3M7UZHDhwANXV1YavT5w4gQkTJuDOO++0YlREZE6aqmo888Mx/BJfswveY+Mi8fj4SEmsZm+oPrUpbb0Ija7oFuiG6FAvHEjJx3cHLrCNlUQUlFXirS0JAIC0vDKM+U+s2Wu8qX0xeWb1woULePDBBxEREQEnJycoFIp6N6Wy5bmwn58fAgMDDbdNmzYhIiICo0aNMjVUIpKQUk0V5q4+gF/iL0Epl+HtO/rgiQldJZGoAg3XpzZEIZNhZKSfZGaC2yt9J4ANBy5A11gTWmpTj66Px5mMur3YWeNNrWHSzGpSUhKGDBmC/Px89OzZExqNBiEhIXB0dERSUhK0Wi369u0LT0/PVgVXWVmJr776CosXL270F5lGo4FGozF8rd+sQKvVQqvVtur1yfL0Y8Sxsg2tHa/iCi3mrzuMIxcK4eKgwMrpfTGii69kxj8lpxT7k7KglDf/5jg8wgdv39FLMrE3pD38/xrfzReuaiXSC8rxz7ksDAnztnZIJrGXsdL/H5LLAJ2QQQaBmo3mBPYnZeFcZiFCGtke2ZbYy3g1pLq2hKNap7Po99eSc8uEaPmGWnPmzMH69euxdetWjBo1CnK5HMuWLcOSJUuQkZGBBQsW4NSpU9i7dy98fHxaenqD7777DjNmzEBaWhqCgoIaPGbZsmV4+eWX693/zTffwNnZ9v9DENmLUi3w8WkFLpTK4KQQWNC9GiFu1o6K7ME35+TYny3HUH8d7o7QNf8Esrh9WTKsP69AD08dHuzOMbElf1+S4ZdUBQb56jAr0nJjV1ZWhhkzZqCwsBDu7u5NHmtSstqxY0dER0dj48aNAAC5XI6lS5di6dKlAGpmO3v37o0xY8Zg1apVLf8Oak2aNAkODg747bffGj2moZnVzp07IyMjo1WJMrUNrVaLrVu3YsKECXaxd7m9M3W8cko0uHfNISRcLoGXswpr7x2E7h0sn6mm5JTiQn4Zgr1djJrNSckpxU0f7G708d8fGWFTs0L68XINH4AQP3ebir0l9ifn4Z4vDsLNUYm9z4yC2gZ3tLKX90L9/6FqHVAlZJBD4OrhsLX/Q42xl/FqyOd7UvDGlrMYGu6FV6f2tNh4FRUVwdfX16hk1aQygJycHERFRV05iVKJsrIr2zaq1WpMmDDBkMyaIjU1Fdu2bcNPP/3U5HFqtbrBLV9VKpXdXUD2jONlW1oyXvmllbh3zWEkXC6Bv5sa39w/BF38LZuomtrEP7KDJ4aE+ze6SUKXQA+Lxm1OBWWVWLzhGKb5AA9vOApNtcxuF7kM6+KPIA9HXCqswM7z+bixdwdrh2QyW38v1P8f2lX7f08HGTTVtvl/yBi2Pl7XKiirxHcH0wEAe5PyMf693RZ732jJz82kBVa+vr4oLS2t83VKSkqdY5RKJQoKCkw5PQBg9erV8Pf3x5QpU0w+BxFZV1GFFrO/iEPC5WIEuKvx3YNDLZ6oAq1r4i+FTRLM4dH18diXlFvnPntd5CKXyzCtf0cAwM9H0q0cDa2c3h+h1yw8tMX/Q+3Ro+vj67Uak8L7hkkzq5GRkTh//rzh68GDB+PPP/9EUlISwsPDkZ2djR9++AEREREmBaXT6bB69WrMmTPHpI4CRGR9ZZVVmL/mAI6nF8LbxQFf3zek3i8wS2htE39rb5JgDvqfgVpRt8rLnjcymNo3CB/HnseOs9ko0VTBVc3fHdbi4azCbf074p2tZzGqqx+WTe1pd9ebPZLyBigmzaxOnjwZMTExhpnTxx9/HMXFxejTpw+io6PRtWtXZGZm4pFHHjEpqG3btiEtLQ3z5s0z6flEZD1J2SX462QmZn8ehwMp+XBzVGLdvMFtMqMKAKl5ZU0+bmwT/7beJMGczPUzsCVRgW4I9XFGZZUOMWeyrB1Ou6ffbjXEx9km/w+1R1J+3zApWV2wYAFiY2OhUNRUTY8ePRobNmxASEgITpw4gYCAAPz3v//F/fffb1JQEydOhBACXbt2Nen5RNT2CsoqMfvzOIx9Zwce+PIQDqbmQy4DPpw+AL06tl2dWoh304sB2kMT//b4M5DJZJhcW6v6x4kMK0dDmtpk1UFhlo0yqQ1I+X3DqKvo2LFjyMq68pequ7s7hgwZAje3KzMld955J06ePIny8nKcOXOG26MStTOPro/H7ms/QhLAZ7uT2zSOcD9XjIz0g+Ka3sztqYl/e/0ZTO4VCACIOZON8srqZo4mS9JW1ZSgqJRMVm2F/n3j2q72UnjfMOoq6t+/Pz755BPD12PHjsW6dessFhQR2RZ9rdO1Hfl0gKHWqS3ZyyKp1lg5vT+uC6/bvs/efwa9O3qgo6cTyrXV2HHWuO1yyTIqa7dN58yqbVk5vX+9pFQK7xtGVaArFApUV1/5KzU2NhajR4+2VExEZGOMqXVqy7/K7WGRVGt5OKuwatZAbN68GR/PHIAwfw+7/xnIZDLc0CsQn+9Oxp8nM3FD7UwrtT39zKoDZ1ZtioezCjOvC8Grm05hSLg33ritjyTeN4xKVjt16oT4+HgLh0JSp63WITYhGzvOZiHxcgkKy7VwUMoR5uuCqEB3TOjh32aLaEhacos1TT5urVqnMN/2l6Q2ZESkn131gmzKhB4B+Hx3MmITslCtE1DIG96qmyxLy5pVmxfg5iiZ90+jktWbb74ZK1euRPfu3dGhQ00B+5o1axAbG9vk82QyGbZv397qIMm6dDqB7w9dwP9tTURmUUW9x49dLMQvuIQ3t5xBVKAb7h8Rjqn9gqDim1S7cDqjCC//dgoAIANwdbMkfSNwqbzhkf0bGOIFN0cl8su0iL9QgIEhXtYOqV3SL7BSKfjHArWeUcnq8uXLodFo8Pvvv2PHjh2QyWRISUmptxHAtWQyXqS2Lq+0Eo+sP4w952qai/u6qnFTnw7o19kTPq4OKKusRlJ2KeKSc7H7XA7OZBbjye+P4v3tiXhxSndM6BHA68COZRVXYN6aAyjWVGFQiBcclQrsPn+lGb8Uap2ofVEp5BjV1Q+bjmUg5kwWk1Ur0VbVJqssA7BZovlD2oxRyaqbm1udBVZyuRzLli3DkiVLLBYYWV96QTmmf7oPaXllcFIpsHhCV8weFgK1sv6+2wtGR6CwTItv4tLw+e5kpOWV4YEvD2FclD/euL0P/Nzqb4lLtq1CW40HvzyEjMIKRPi54PM50fBwVrXrOlGShrFR/th0LAPbz2ThqUndrB1Ou1TJMgCbJcXpJZOuojlz5qBfv35mDoWkJKu4wpCodvZ2wsaFw3H/yPAGE1U9D2cVFoyOwI6nR+Ph0RFwUMix/UwWbnhvJ7afvtyG0ZOlCSHwws8ncCStAB5OKnxWm6gCtt1Mn+zD6G7+kMlqSlQyCsutHU67ZKhZ5cwqmYFJV9Hq1asxdepUc8dCElFZpcPDXx1GWl4Zgr2d8d2DQ9Et0PiFUy5qJZ65IQq/PjIcUYFuyC2txPy1B/HWljPQ6aT0wQKZavU/qfjx8EUo5DJ8OGMAE1OSFG8XB/Tv7Amgpucqtb3KKs6s2johpPP72qSr6Pjx4/jiiy9QVFRkuK+8vBwLFixAx44d0aVLlzplA2Rb3v7zDA6m1myTuWZuNDp4OJl0nqhAd2xcOBxzh4cCAD6KPY8HvjyI4gqtGaOltnYqX4Y3/zwLAHhxSndcH+nbzDOI2t7obv4AgN3nmKxaQ2V17aYATFZtjhSXmZh0FS1fvhwvvfRSnR2snn/+eaxatQrFxcW4cOECFi5ciK1bt5otUGobB1PyDDsOvfuvfgj3c23V+RxVCiy9uSf+766+cFDKse10Fu78ZC+yGugqQNJ3PrsUaxPl0AngrkGdce+wUGuHRNQg/aYQ/5zP5Sc6VqBfYMUyADIHk66iuLg4jBkzxrDKu6qqCqtXr8bgwYORlZWF5ORk+Pn54f333zdrsGRZ2modnvvpOIQA7hjYCRN6BJjt3Lf274TvHxwKPzc1zmQW445P9iI1t213NaLWKSzTYsHXR1BRLcOgEE+8eksvdnogyerbyQOuaiUKyrQ4lVHU/BPIrCoNrauYrNoqKf2JZ9JVlJ2djc6dOxu+PnDgAIqKivDQQw/B0dERQUFBmDZtGo4ePWq2QMny1sel4VxWCbxdHPDSlB4mnycpuwQxCVn1ttjs29kTPz40DMHezkjLK8PtH+812y+Rxl6TzKOqWodF6w8jObcMng4CH9zdt9kZE44JWZNSIcd14d4AgN3ncpo5msyNC6xslxSnIIxqXVXvSUolNJorO9bExsZCJpNhzJgxhvt8fHyQk8M3CFtRoqnC/22tqUN8YnykYWV3SxSUVeLR9fHYmXilRmxkpB9WTu9vOF+wjzN+WDAUc744gNMZRZj1xUE8EGl63Ma8JrXe63+cwa7EHDip5Lg/qhI+ro23IuOYkFQM7+KLbaezsOdcDh4aFWHtcNoVLrAiczLpKgoNDUVMTIzh6++//x5hYWEICQkx3Jeeng4fH5/WR0ht4qt9qcgv0yLc1wXTBwebdI5H18djzzUzGHvO5eCR9Ufq3Ofv5ogND1yHgSFeKKqowkenFDh5ybQZVmNfk0z33cEL+Ly2jvmt23ujUzML/zkmJBXX19atxiXnoUJbbeVo2hf9zKpKKcV5OjKKhOoATEpWZ82ahaNHj2LIkCEYOXIkjh49ihkzZtQ55tixY4iMbMWUGbWZ8spqfLYrCQDw8JguUJrwl3BSdgl2Jmaj+ppWF9VCYGdidr2Pgj2cVFgzNxr9O3ugrFqGOWsO4kR6oUVfk1ruUGoeXvz5BADgsXGRuKFn03XMHBOSki7+rvB3U0NTpcPh1Hxrh9OuaDizarOkuBbBpKto0aJFuPPOO3Hw4EHs3r0bkydPxvPPP294/OTJkzh69CjGjh1rtkDJcn46chE5JZXo5OWEaf2CTDpHal5Zk4+nNLCYys1Rhc9nD0Soq0BheRVmfrYfJy8Zn7Ca8ppkvPSCcjz45SFUVuswuVcgHhvX/B+fHBOSEplMZugKwLrVtqXlAisyI5OuIrVajW+//Rb5+fkoLCzEpk2b4OjoaHg8ICAAR44cwaOPPmq2QMkyzmcV46OY8wCAe4eFmvzGEuLt3OTjoT4Nf3bs5qjEQ92r0beTBwrLtZj9eRzOZ5dY9DWpeeWV1Xhg3UHklFSiewd3vPOvvpDLm/9rm2NCUqNPVq8tTSHL0tesqrnAisygVVeRu7t7nV6rer6+vujbty88PDxac3qyoIKySsz+PA7j3t2J9IKa7Qi3n85CYZlpDfvD/VwxMtIPims+PlDIZBgZ6dfkDkdOSmD1nAHo1dEduaWVmPXZfkNMlnpNapwQAk/9cBQnLxXBx8UB/5s9EM4Oxq3F5JiQ1AzvUrN24nh6ITckaSPVOgF9a1vOrNouIaGiVV5F7VRDi2DikvNatQhm5fT+hlkMveFdfLFyev9mn+vmqMLauYMR7ueCS4UVmPXZfuSUaJp9Xmtekxr2wd/n8PuxDKgUMnx8z0B08mp6tvRaHBOSkg4eTujk5QSdAI6kFVg7nHZBXwIAsHWVLZJgyapxravCw8Mhk8mwbds2hIWFITw83KiTy2QynD9/vlUBkvnpF8Fc6+pFMKbMgHk4q7Bu/mAk55QiJbcUoT4uLTqPj6saX80fgjs/2YuknFLM+SIO6x+4Du6Ojbc7au1rUl1bTmTindoWZq9O64XBYd4tPgfHhKRmcKg3Luan40BKHkZ29bN2OHZPv7gK4MwqmYdRV5FOp4NOp6vztRCi2dvVzyHp2J+c2+TjrV0EE+brgjHd/E1KUII8nfDl/MHwdXXAyUtFmL/mAMorm28505rXtFctbcp/OqMIi7+LB1BTv3y3iS3M9DgmJBWDQmv+6DqQkmflSNoHTdWV92yVQoLTdGQUIZ0qAONmVlNSUpr8mmxDQ83aG2LtRTDhfq5YO28w7v50Hw6k5GPB14fw6axB/DjJSKY05c8p0eC+tQdRVlmN4V188OKU7m0VLpHFDQ7zAlBTBlBZpeN7iYUZNgRQyiXZBomaJsUR4//YdqShOtWrSWkRTM8gD6y+NxqOKjliE7Lx1PdHodNJ6M88CWtpU35NVTUWfHUI6QXlCPVxxoczBpjUa5dIqiL8XOHlrIKmSocTLWiPR6YxdALg+wiZiUlX0tixY7Fu3bomj/nqq6/YZ1VCGmvWfjWpLYIZFOqNT+4ZCKVchl+PXsLSX09CSOlzCQlqaVN+IQRe/PkEDqTkw81Ric/mRMPT2aEtQyayOJlMdqUUIJmlAJZWWbvASq1ismrLpPTr1qQrKTY2ttlSgNTUVOzYscOU05MFNNes/fXbemPd/MGS27t9dDd/vHtXP8hkwJf7UvF/tYt/qGEtbcr/2a5kfH/oIuQy4IMZA9DF39WS4RFZTXRoTSkA61Ytr5K7V9k2CZZuWOxKKi0thUolrcSnPWuuWft14T5tFEnLTe0bhFem9QIA/Pfvc/iidp96qq8lTfljzmThtT9OAwBenNIDo7hKmuxYdO3M6sHUfJYUWdjVNatE5mBcp28AaWlpdb4uKCiodx8AVFdX48KFC/jxxx8RGhra6gDJPPTN2vecy6nzEbEcwPUSqVNtyqzrQpBfWol3t57FK5tOwdNZhdsGdLJ2WJLT2Dgrared1I/z2cvFeGT9EQgBTB/cGXOHh1opYqK20TPIA44qOQrKtDiXXYKuAfU3tCHzYLJqH2xyU4DQ0FCEhYUhLCwMMpkM77//vuHrq29dunTBmDFjkJiYiPvvv9+SsVMLNdSs/fraVeK24JGxXQxJ1dM/HMO2U5etG5BENdeUP6+0EvetPYgSTRWGhHnj5am9uGKX7J6DUo7+nVkK0BY01UxWbZkUfxsYPbM6e/ZsyGQyCCGwbt069O3bF/369at3nEKhgLe3N8aOHYsbbrjBnLFSK3k4q7B2XjSGvfE3MgorsPyWXrjnuhBrh2U0mUyGl6b0QGGZFj8dScfCbw5j3bzBGCLhEgZraKopv6aqGg99dQhpeWUI9nbGx/cM5C8UajcGhHhib1Iu4tMKMHOI7bz32RrWrJK5GZ2srlmzxvDvHTt2YO7cuXj00UctERNZ0PnsEmQUVsBBKcdtAzpaO5wWk8tlePOOPiiq0GLb6Szct/Yg1j9wHXp19LB2aJIT5lt35yidTuCp748hLjkPbmolPpszCN4uXPlP7Ue/2pnVoxcLrBuInWMZAJmbSVdScnIyE1Ubtedcze5Vg0O94exg9N8qkqJSyPHBjAEYHOaNYk0V5nwRh6TsEmuH1Sot3W3KFG/9mYDfjl6CUi7DJ7MGsmaP2p2+nWv+qE3MKkFxhdbK0dgvjSFZVVg5EmoNm29dRbZLv9XqdeEt3/NdShxVCnw2ZxB6Brkjt7QSsz6PQ0ZhubXDarGCskrM/jwOY9/ZgbmrD2DMf2Ix+/M4FJaZ9xfpl/tS8cmO8wCAN2/vU6+mlag98HdzREdPJwgBHE/n5gCWwjIA2ybFJQwmX0nFxcV4/fXXMW7cOHTv3h3h4eH1bhEREeaMlVpJCIG42obY9lDn6e6owtp5gxHm64L0gnLM+jwO+aWV1g6rRVq625Qptp++jKW/nAAALJ7QFbcPZBcFar/6dfYEAMRfKLBqHPassqoaAKBmGQCZiUlXUnZ2NgYMGIAXXngBhw4dQkJCAvLz83H58mWkpKQgJSUFlZWV0Ol05o6XWuF8dglySiqhVsrRp5N91Hj6uqrx5fzBCHR3xLmsEsz+Ig6F5bbx8V5Ld5syxaHUfCz65gh0AvjXoE54ZGyXVp+TyJbpSwGOMlm1mEp2A7ALEqoCMC1ZXbZsGc6fP49169YhPz8fAPDEE0+gtLQU+/fvx+DBgxEaGoqTJ0+aNVhqnX1JNbOqA4K9oLajWqJOXs74cv5geLs44Hh6IWZ/vt8mEtaW7jbVUqczijB3dRzKtdUY2dUPK27tbXSLqraooSWyBv0iK86sWo6+DIAzq7ZJJsHmVSZdSZs3b8a4ceNwzz331PvlFx0djT/++AMpKSl4+eWXTQoqPT0d99xzD3x8fODk5ITevXvj4MGDJp2LrthvKAGw7XrVhkQGuOHr+4bAy1mFoxcLMeeLOBRJfAFFS3abaqmUnFLM/iIORRVVGBjihU/uGQCVEfVjbVVDS2QtvTq6QyGX4XKRBpmFFdYOxy6xGwCZm0lXUkZGBvr3v9JIXqFQoLz8yuIWLy8vTJ48Gd99912Lz52fn4/hw4dDpVLhjz/+wKlTp/DOO+/Ay8vLlFCplhAC+5NqFlcNCbP9etWGdO/gjq/vuw6ezirEXyjAnC/iJL3iV7/blOKaP/gUMhlGtmJXsczCCtzz+X5kF2sQFeiGL+ZEG935oS1qaImsydlBaeiEEX8h38rR2CfDpgBcYGXTpNQNwKTeRR4eHtBqryQBXl5euHjxYp1j3N3dcflyy3cYevPNN9G5c2esXr3acF9YWFijx2s0Gmg0GsPXRUVFAACtVlsnxvYuNa8MWcUaqBQy9OrgIpmfjT4Oc8UT6eeEtfcOxOzVB3EkrQCzP9+Pz2cPgJujyiznN7f/u7MXnv7hGPacv5IgDo/wwdt39DLpZ5JVrMGsLw7iYn45QrydsXrOADirjPv5puSUYn9SFpTya98YBPYnZeFcZiGC3Gt+jlK5fqhp5v7/ZS/6dHTH6YwiHE7Nw7hu0uiMYU9jVVFZBQBQyu3j+2mIPY3XtXS6mgVyQugs+v215NwyIVqeOw8dOhSBgYH4+eefAQCTJk3C0aNHcfLkSfj4+KC8vBx9+/aFXC7HmTNnWnTuHj16YNKkSbh48SJ27NiBjh074uGHH25069Zly5Y1WG7wzTffwNm56Y9Z25NDOTKsS1QgxFVgce9qa4djcRdLgQ9PKlBWLUMnF4GHulfDTZr5qtkUVgIfnFQgq0IGTweBR3tWw8fR2lERSc++LBnWn1egi7sOj/TkQmBz+y5Jjj2X5bihUzUmd5bQ9BwZ5Z/LMnybpEAvLx3uj7Lc/4+ysjLMmDEDhYWFcHd3b/JYk2ZWJ06ciP/7v/9DWVkZnJ2d8eCDD+KOO+5A3759MXToUBw+fBgpKSlYsWJFi8+dlJSEjz/+GIsXL8bzzz+PAwcO4NFHH4WDgwPmzJlT7/h///vfWLx4seHroqIidO7cGWPGjIGPj31+3G2KI5vPAIlpGNkzGDfe2N3a4RhotVps3boVEyZMgEpl3mxyxPXFmLv2EC6WVuLzZHesuXcggjydzPoajUnJKcWF/DIEe7sgxMfyfzRlFlVg1hcHkVVRhiAPR3w5bxCCa2tiU3JKcTA1HzIAg0K9G40nJacUN32wu9HX+P2REQhyV1lsvMj8LPn/y5Z1uVyM9R/sRUaFCjfcMBZyufUXlNjTWO38+QRw+RJ6dY/CjSMb/2TUltnTeF2r+OBFfJt0Cv7+Abjxxv7NP8FE+k/CjWFSsvrQQw+hR48ehmT1tttuw9tvv43ly5fjxx9/hJOTExYvXoynn366xefW6XQYNGgQXnvtNQBA//79ceLECXzyyScNJqtqtRpqtbre/SqVyu4uoNY4cakYANA/xFuSPxdLjFefYG/8sGAY7vlsP5JzyzD9swP48r4hiPBzNevrXK2grBKPro/HzsRsw30jI/2wcnp/eDhb5ud+qaAcs744iJTcMnT0dMKGB65DZ29nFJRVYsFXh7G3tlZZb1iEDz6eObBePJEdPDEk3B97zuXUaaelkMkwvIsvugReKf/h/y/bwvGqq1sHT6iVcpRWVuNSsdbk+nBLsIexql1fBUcHpc1/L82xh/G6llJR0y1ILpdZ9HtryblNqn7u0KED7rrrLvj6Xqn1efLJJ5GTk4OMjAyUlJTg7bffhkLR8vZIHTp0QI8ePerc1717d6SlpZkSKgGoqtbhxKWa3Vr6dPK0bjBtLMzXBd8/NBThfi64VFiBOz7+BwdS8iz2em29QCnxcjHu+PgfpOSWobO3E759sCZR1cdybaIKAP+cz200npXT+9fb3Wp4F1+snG65v66J2ppSIUf3DjUfO57gTlZmp9GydRWZl1mvJIVCgYCAAKN7OTZk+PDhSEhIqHPf2bNnERIS0trw2q2zl0tQodXBTa1EuIRmENpKkKcTvn9wKPp08kB+mRYz/7cfG4+km/112qLJ/9UOpebhjk/24lJhBcL9XLDhgaHo5OVcJ5bGNBaPh7MK6+YPRsxTo7F6bjRinhqNdfMHW2xWmMhaegbVJKsnLxn/USQZh5sCkLmZdCVduHABf//9N8rKrjQ11+l0ePPNNzF8+HCMGzcOv//+u0kBPfHEE9i3bx9ee+01nDt3Dt988w0+/fRTLFy40KTzEXDsYgEAoFdHD0nUZlmDj6sa3z4wFDf0DERltQ6PfxuP/9t6FiasL2yUpZv8X+3Pk5mY8b+azQ/6B3vix4eGoeNV9bjNxdJcPGG+LhjTzV9SH48SmVOvjjU7WZ28xJlVc2OfVfsgpdZVJl1JL730Eu6888469QYrVqzAv//9b+zduxcxMTG45ZZbcODAgRafOzo6Gj///DPWr1+PXr164dVXX8V7772HmTNnmhIqATh6sebNuG/tntjtlZODAh/NHIAHR4UDAN7fnoiHvz5s6MXa2l2bLNnkX08Igf9uT8SDXx6CpkqHsVH+NZshuDi0KBZzxUNkq/QzqyfSC836RytdvYOV/eyU2J604sNxizFpgdWePXswfvx4Q7IqhMAHH3yAqKgo/PXXX8jMzMT48ePx9ttvm7QxwE033YSbbrrJlNCoAfo9sPt28rBuIBIgl8vw78ndEebjgpd+OYE/TmTi5KUi+LmqcSjtSoNwUxZF6Zv8N7ZAqbWzlKWaKjz9w1FsPp4JAJgzNAQv3tSjwZ2p9LE0VgrQmk0HiOxB1wA3KOUy5JdpkVFY0WadQtoDbgpA5mbSlZSVlVWnhjQ+Ph7Z2dl45JFH0KlTJwwaNMjkmVUyL01VNc5erukE0Kedz6xe7e7Bwfj2waEI8nBEWl5ZnUQVMH1RlKUWKJ26VIRpH+7B5uOZUClkeOO23nh5Wq8mt1BdOb0/hobXb982LMKHC6ao3XNUKdDFv6YzCBdZmRfLAOyDlD5vMGlmVafTQae70ig2NjYWMpkMY8eONdzXsWNHZGZmtj5CapVzWSWo0gl4OKkQ5MEO8VcbEOyFD2cMwK0f/1PvsasXRbVkBlK/QCk5pxQpuaUI9XFp1QymTifwxZ5kvLUlAZXVOvi7qfHxPQMwMMTbqFjWP3AdknNKsS8pFzIAQ8J9OKNKVKtXRw+cySzGiUtFmNgz0Nrh2I3KqpqNZ5is2iYZpFcHYFKyGhwcjLi4OMPXGzduRIcOHdCtWzfDfZmZmfD09Gx1gNQ6ZzJqZlW7Bbq1qkuDvSqoaHq7t5TcliWremG+rUtSgZom/S9uPIHdta2wxncPwFt39IH3NfWpV0vKLkFqXlmdJNkcsRDZo55B7vjhEHCKi6zMit0AyNxMSlZvv/12rFixAnfccQccHR2xe/duLFq0qM4xp06dQnh4uFmCJNMl1JYAdA90s3Ik0tTcQqQyTVUbRXJFhbYan+5Mwgcx51BZpYNaKcdLN/XAzCHBjf7BYY3NCIhsnb4jwIl0tq8yJ0MZAGtWbZqUFh6adCU99dRTiI6Oxk8//YRvvvkGvXv3xrJlywyPp6amIi4uDqNHjzZTmGSq0xk1b8JRHZred7e90i9EUjSSBC785ggWfXPYUPdrSVXVOnx7IA1j/hOLd7eeRWWVDiMiffHXEyNxz3UhTc6Mt/VmBET2oHsHd8hkNdsV55RorB2O3bjSDYDJqk2S4IewJs2suru7Y9++fThx4gSAmh2mrt2t6qeffsKgQYNaHyG1ypnMK2UA1LCV0/vjkfVH6sxKXhfmgwAPNX6Jv4RNxzKw6VgGJvcKxJxhoRgS5m3WkopSTRW+P3gBX+xJQVptf9QgD0c8OzkKU/sGNftajW0AYGrdLVF74apWIszHBUk5pTiRXojR3fytHZJd0HCBFZmZScmqXq9evRq8PyQkhDtOSUBuiQbZxTWzBd0CmKw2pqlFUQ+MDMcHf5/DHycyDbcIPxfcPrATJvYINKwmbqmqah32J+fhl/h0/HE8E8W15QZeziosHNMF91wXAkeVcT0KjdmMgMkqUcN6BLkjKacUpzOKmayaCbsB2AfpFAG0MlklaUuonVUN9naGi5pD3ZyGFiL1DPLAx/cMREJmMdb8k4xf4i/hfHYp3tqSgLe2JCDM1wXRoV7oH+yFboFu6OTlBD9XdZ3ZUG21DpeLKrA7MQeH0/JxMb8cx9MLUVxRVee1510fhtsHdISzQ8vGqi02IyCyV907uGPTsQwkZLJu1Rx0OoEqXU2aw5pV2yTBKgAmq/ZMXwIQxRKAVusW6IbXb+uD52/sjk3HMrDlRCb+OZ+D5JxSJOeU4ruDFw3HKuUyOKoUcFDKUaGtRllldYPn9HBSYUqfDpjaNwiDQ71N3grX0psRENkz/adO+vdLah19JwAAUBv56RBRc5is2rEzmVxcZW5ujipMHxyM6YODUVShRVxSHg6n5eNIWgFSc0uRUVSBKp1AiaYKaGK9hhxAn44eeO3W3maJq6G6W3NsRkBk7/T1/OezS1BZpeNH162kr1cFOLNK5sNk1Y4lcGbVotwdVRjfIwDjewQY7qus0iG3VAONVgdNlQ5ZxRWY9XlcvefqAOw6l2O2xU/m3oyAqL3o5OUEV7USJZoqJOWUICqQf9y3RuVVyapKIcUPlMlYEupcZVrrKpI+IQTOZZUAALoGmLYIiBqWlF2CmIQsJOeU1nvMQSlHBw8nhPq6oFugm6F2qzEpufXP0Rphvi4Y082fiSqRkWQymWF2NYGlAK129YYA3IjGNklx3DizaqcuF2lQWlkNhVyGYG8mLuZgSuN9Ln4ikr6oQDccSs3HmcxiTLN2MDbO0GOVJQBkRq2+mk6dOoWffvoJX375pTniITNJyq6ZVQ32dmYNlpmY0ni/sU0HFDIZRkb6cQaUSAL0pVJnMtgRoLXYtsp+SKgKwPRk9cCBA+jXrx969+6NO++8E/fee6/hsZ07d8LZ2Rm//vqrOWIkE5yv/Yg6nMmQWegb71dfU8RzdeP9xqyc3h/Du/jWuY+Ln4ikQ78IlWUArcdk1fZJrwjAxDKAkydPYuzYsZDL5XjiiSdw5swZ/PHHH4bHR4wYAV9fX3z//feYOnWq2YIl4+lnVsP9mKyaQ2sa73PxE5G0da1tX3WpsAKFZdpGy3qoeZXVNa36mKySOZl0NS1duhQAcOjQIfznP/9BdHR0ncdlMhmGDh2KAwcOtD5CMsn57NqZVT8urjLV1QupzFF72tTip6YWbRGRZXk4qdDR0wkAkHCZs6utodHWzqyyZtXmCQm1AzBpZnXHjh24/fbb0aVLl0aPCQ4OxpYtW0wOjFrHMLPKGbwWa2wh1bAIH+xPyjNr431TFm0Rkfl1C3RDekE5zmQWYXCYt7XDsVmaapYB2DoJNgMwbWa1uLgY/v5N76FcXl6O6uqGd+4hy6rQViO9oBwAEGHi3vXtWWMLqYSA2WtPTVm0RUTmp29fxZ2sWoc1q2QJJs2sdu7cGcePH2/ymMOHDyMiIsKkoKh1UnJLIQTg7qiEj4uDtcOxKfqFVNeqFgJ7k3IR89RoADBL7WlTr6VftMXaVqK2wY4A5mFoXcVklczIpKvppptuwl9//YVt27Y1+Ph3332Hffv24ZZbbmlNbGSipKvqVaXY3FfKjF1IZY7G+8a8FhG1Df3OVYmXSyRVq2drrsysKqwcCZlKimmDScnq888/j6CgINx44424//77cfDgQQDARx99hFmzZmHGjBkIDQ3F4sWLzRosGYedAEzXlk38uWEAkXSE+jpDIZehWFOFy0Uaa4djsww7WHGBFZmRSVeTn58fYmNjER0djc8//xy///47hBBYtGgRvv76a0RHR+Pvv/+Gh4eHueMlI+g7AUSwE0CLtWUTf24YQCQdaqUCIT41f0AmZrFu1VQsAyBLMHm71YiICOzZswfx8fHYt28f8vLy4O7ujiFDhtRrZUVtS//xMZMd06yc3h+PrD9Sp57UUk382/K1iKhpkf6uSMouReLlEoyI9LN2ODaJC6zsh5SqYYxKVm+77Tbcfffd+Ne//gWgZoeq0NBQBAcHo1+/fujXr58lY6QWulBbCxnczMfM1LC2bOLPDQOIpCPS3w1/nryMxKwSa4dis1gGYPtkEtzDyqiraePGjThz5ozh6zFjxmDNmjWWiolaoayyCjkllQCAzkxWW8VcC6mk9lpE1LDIgJrSqfNMVk2m4cwqWYBRV5OnpyeKiq608+BKSem6kFfTX9XDSQUPJzaVJyIyVpfavtRns4r5e85Emiput2ovBKTzf8CoMoAePXpg/fr1iI6ORocOHQAAKSkp2LlzZ7PPHTlyZOsipBZJYwkAEZFJIvxcIZMBBWVa5JZWwtdVbe2QbA5rVm2fFFtXGZWsLlmyBLfccgtmzJhhuG/t2rVYu3Zts8/lLlZti8kqEZFpHFUKdPZyRlpeGRIvlzBZNYEhWWXNKpmRUcnqxIkTcfr0aWzbtg3p6elYtmwZRo0ahVGjRlk6Pmoh/eIq1qsSEbVcpL8r0vLKcC6rGEMjfKwdjs0xtK5SMVm1dVKqhDG6dVVISAjmz58PAFi2bBlGjx6NJUuWWCwwMs2VZNXJypEQEdmeLgGu2H4mix0BTMRuAGQJJvVZTU5Ohqenp5lDIXNgGQARkeki/d0A1Gy7Si3HTQHIEkxKVkNCQswdB5mBEILJKhFRK0TWdgTgzKppuMDKfthcGcArr7wCmUyGhQsXwtvbG6+88opRJ5fJZHjppZdaFSAZL7tYA02VDnIZEOTJMgAiopaKqE1Wc0o0yC+thJeLg5Ujsi2GMgAmqzZLJsF2AEYlq8uWLYNMJsNdd90Fb29vLFu2zKiTM1ltWxfya2ZVO3g4QcV6ISKiFnNVK9HR0wnpBeU4l12CaBdva4dkUwybAigUVo6E7IlRyWpMTAwAIDg4uM7XlrBs2TK8/PLLde7r1q1bnR20qGEsASAiar0If9eaZDWrBNGhTFZbgmUAZAlGJavXtqiydMuqnj17Ytu2bYavlUqTSmvbnbTcmt2rmKwSEZku3NcFO89mIzmn1Nqh2Bxut2o/pLSDlcWupjfffBPjxo0z6blKpRKBgYGGm6+vr5mjs08Xa8sAOnmxXpWIyFQRfi4AgKRsLrJqqUr9dqssRbNZ0qtYNbEbgDHOnDmD2NhYk56bmJiIoKAgODo6YujQoXj99dcNJQjX0mg00Gg0hq+LiooAAFqtFlqt1qTXt1XptclqoLuDzXzv+jhtJd72juNlWzhepgn2cgQAnM8qabOfnb2MlX5mVQGdzX8vTbGX8WqIfudRIYRFv7+WnFtyn68PGTIEa9asQbdu3ZCRkYGXX34ZI0aMwIkTJ+Dm5lbv+Ndff71ejStQU1fr7Ny+Pg4/d0kBQIbU00ex+VK8tcNpka1bt1o7BGoBjpdt4Xi1TL4GAJRIzSvFb5s2oy0nCW19rIpLan4Pxe37BxknrB2N5dn6eDXkSI4MgAK5uXnYvHmzxV6nrKzM6GNlQlimk9bcuXOxbt06Q4ZuqoKCAoSEhODdd9817KB1tYZmVjt37oyMjAz4+LSfrfKEEOjz6nZUaHXY9sT1CLGRulWtVoutW7diwoQJUKlU1g6HmsHxsi0cL9PodAL9lm9HuVaHvx4bjjBfF4u/pr2M1eDXY5BfpsXmRcMQGeBq7XAsxl7GqyGbj2fise+OYXCoF76eH22x1ykqKoKvry8KCwvh7u7e5LGSm1m9lqenJ7p27Ypz5841+LharYZara53v0qlsrsLqCl5pZWo0NZ8/NLZxxUqpW21DWlv42XrOF62hePVcqG+rjidUYS0fA26dvBss9e19bHSlwG4Oqlt+vswlq2PV0MUtfmDTCaz6PfWknNLvgK6pKQE58+fR4cOHawdiqRdKqjpBODrqobaxhJVIiKpCa9dZMWOAC2jT1bVKsmnF9QM6fQCkGCy+tRTT2HHjh1ISUnBP//8g1tvvRUKhQLTp0+3dmiSllFYAQAI8nS0ciRERLYvovaj/6QcdgQwVlW1DtW6mhRHzdZVNksmwX4ARpcB3HjjjS068fHjx1scDABcvHgR06dPR25uLvz8/HD99ddj37598PPzM+l87YV+ZrWDB5NVIqLWCqudWT2fzZlVY+m3WgXAT/jIrIxOVrds2dLik5uyv+yGDRta/BwCLhXWJKtBnuyxSkTUWuG+NYuDWAZgPI32SrLKTQHsgITqAIxOVpOTky0ZB7VSRkFtGYAHk1UiotbSz6xmF2tQXKGFm6N9LaKxBH29qkohg0IuvY+SyTgmzDNanNHJakhIiCXjoFYylAGwZpWIqNXcHVXwdVUjp0SD5JxS9Onkae2QJE/D3avIQnhF2Ym0vJrmuhL8g4iIyCaFG7ZdZSmAMa50AmC9KpkXk1UbV1BWiVmf7UdWcc3GCAu/OYLZn8ehsMz+toAjImpLEYZklR0BjKGvWWUnAPsgJFS0yivKxj26Ph57zuXUuW/PuRw8sv6IlSIiIrIPYYb2VZxZNYa+DIDJqm2T4ie0vKJsWFJ2CXYmZkN3zf3VQmBnYjZXsRIRtYK+IwDLAIxjKANg2yoyMyarNiy1tk61MSm5fIMlIjLV1btY6XTS+UhUqgwzq9y9yi4ICV3yvKJsWIi3c5OPh/q4tFEkRET2p7O3M5RyGcq11bhcXGHtcCSPNav2QYqtq3hF2bBwP1eMjPSrV1+ikMkwMtLPUG9FREQtp1LIEVw7KcBSgObpd7BiGQCZG5NVG7dyen/4uqrr3De8iy9WTu9vpYiIiOyHYZEVOwI0izOr9kVCVQDGbwpA0uThrEK4nwuySzR4aFQ47ooO5owqEZGZ6N9PU3KbXiNArFm1H9KrA+AVZQeya3usju7mz0SViMiMQmrfU1OZrDZL3w2AO1iRufGKsgOXi2oK/wPcudUqEZE56ReyprK7SrPYusq+CAm1A2CyauNKNFUoraz56MXfTd3M0URE1BL6riqpeWVsX9UMjZZlAPaA3QDI7LJqZ1Vd1Uq4qFmCTERkTkGejlDKZais0rF9VTOuzKwytSDz4hVl4y4X1dSrclaViMj8lAo5Onk5AQBScli32hSWAZClMFm1cVm1f+n7uzNZJSKyhBB9KQDrVptk6AbAmVW7IKWiF15RNi6rdmaVi6uIiCwjxKd2kVUzW1y3d4Y+q6xZtWkSLFllsmrrDDOrLAMgIrIIzqwah2UAZClMVm3cZc6sEhFZVGjtzCprVpvGBVb2RUKdq5is2jr9zKofZ1aJiCxCXwaQllcmqd6TUqOvWXVgsmrTZBLsXcUrysaxZpWIyLI6eTlDJqvpa51bWmntcCSLZQBkKUxWbZx+9yrWrBIRWYajSoEgj5r2VaxbbRzLAOyLlD5D4BVlw+rsXsWZVSIiiwk2bLvKutXGcAcr+yC9IgAmqzZNv3uVi4MCrty9iojIYkJ9axdZMVltVCXLAMhCmKzasKxi1qsSEbWFYG+2r2oOywDsjIQWE/KKsmH6ZNWX9apERBalb1/FMoDGGXawYhmATZNgMwAmq7Yst6QmWfVzZbJKRGRJ3BigeYYdrFgGQGbGZNWG5ZbUtFDxdXWwciRERPYtuHZmNb9Mi8JyrZWjkSaWAZCl8IqyYTm1M6s+nFklIrIoV7USvrXvtWksBahHCIHK6ppklZsC2AfpVKwyWbVpObUzqz6cWSUisjjDtqssBahHP6sKcGbV1rFmlcxKP7Pqy5lVIiKLC75q21Wqq26yyppVMi8mqzYst1SfrHJmlYjI0kLYvqpR+k4AMhmgUkhwao5aTEKdq5is2jL9AisfF86sEhFZWmfvmi1XL+SVWzkS6bnSCUAOmRQ/RyajySS4hxWTVRtVVlmFstqtVtlnlYjI8vRbrrIMoD4Nd68iC5J0svrGG29AJpPh8ccft3YokqOfVVUr5XBx4JsDEZGlda5NVjMKy6Gt1jVzdPti2BCAi6vshpBQPwDJXlUHDhzAqlWr0KdPH2uHIknZVy2u4kcuRESW5+eqhoNSDp0AMgoqrB2OpBhmVrl7le2TYEohyauqpKQEM2fOxP/+9z94eXlZOxxJ4oYARERtSy6XoZNXbd1qPksBrsbdq8iSlNYOoCELFy7ElClTMH78eCxfvrzJYzUaDTQajeHrwsJCAEBeXp5FY7S2lEuXodOUwUXmhNzcXGuHYzKtVouysjLk5uZCpVJZOxxqBsfLtnC8zC9ArcM5TRlOpVxClJf5pqBsfayyc3Oh05RBVim36d9JxrL18WpKUUE+dJoyaMosO5bFxcUAajaUaJaQmPXr14tevXqJ8vJyIYQQo0aNEo899lijxy9dulSgZqMF3njjjTfeeOONN95s6HbhwoVmc0OZENLppHXhwgUMGjQIW7duNdSqjh49Gv369cN7773X4HOunVktKChASEgI0tLS4OHh0RZhUysUFRWhc+fOuHDhAtzd3a0dDjWD42VbOF62g2NlWzherSeEQHFxMYKCgiCXN12VKqkygEOHDiErKwsDBgww3FddXY2dO3figw8+gEajgUJRtx5GrVZDra7fusnDw4MXkA1xd3fneNkQjpdt4XjZDo6VbeF4tY6xk4qSSlbHjRuH48eP17lv7ty5iIqKwrPPPlsvUSUiIiIi+yapZNXNzQ29evWqc5+Liwt8fHzq3U9ERERE9k+SrataQ61WY+nSpQ2WBpD0cLxsC8fLtnC8bAfHyrZwvNqWpBZYERERERFdze5mVomIiIjIfjBZJSIiIiLJYrJKRERERJLFZJWIiIiIJMvuktUPP/wQoaGhcHR0xJAhQxAXF2ftkOzK66+/jujoaLi5ucHf3x+33HILEhIS6hxTUVGBhQsXwsfHB66urrj99ttx+fLlOsekpaVhypQpcHZ2hr+/P55++mlUVVXVOSY2NhYDBgyAWq1Gly5dsGbNmnrxcLxb5o033oBMJsPjjz9uuI/jJS3p6em455574OPjAycnJ/Tu3RsHDx40PC6EwJIlS9ChQwc4OTlh/PjxSExMrHOOvLw8zJw5E+7u7vD09MT8+fNRUlJS55hjx45hxIgRcHR0ROfOnfHWW2/Vi+X7779HVFQUHB0d0bt3b2zevNky37SNqq6uxksvvYSwsDA4OTkhIiICr776ap29zjle1rNz507cfPPNCAoKgkwmw8aNG+s8LqWxMSaWdq3ZDVltyIYNG4SDg4P44osvxMmTJ8X9998vPD09xeXLl60dmt2YNGmSWL16tThx4oSIj48XN954owgODhYlJSWGYx566CHRuXNnsX37dnHw4EFx3XXXiWHDhhker6qqEr169RLjx48XR44cEZs3bxa+vr7i3//+t+GYpKQk4ezsLBYvXixOnTolVq5cKRQKhdiyZYvhGI53y8TFxYnQ0FDRp08f8dhjjxnu53hJR15enggJCRH33nuv2L9/v0hKShJ//vmnOHfunOGYN954Q3h4eIiNGzeKo0ePiqlTp4qwsDBRXl5uOOaGG24Qffv2Ffv27RO7du0SXbp0EdOnTzc8XlhYKAICAsTMmTPFiRMnxPr164WTk5NYtWqV4Zg9e/YIhUIh3nrrLXHq1Cnx4osvCpVKJY4fP942PwwbsGLFCuHj4yM2bdokkpOTxffffy9cXV3F+++/bziG42U9mzdvFi+88IL46aefBADx888/13lcSmNjTCztmV0lq4MHDxYLFy40fF1dXS2CgoLE66+/bsWo7FtWVpYAIHbs2CGEEKKgoECoVCrx/fffG445ffq0ACD27t0rhKh5A5HL5SIzM9NwzMcffyzc3d2FRqMRQgjxzDPPiJ49e9Z5rbvuuktMmjTJ8DXH23jFxcUiMjJSbN26VYwaNcqQrHK8pOXZZ58V119/faOP63Q6ERgYKN5++23DfQUFBUKtVov169cLIYQ4deqUACAOHDhgOOaPP/4QMplMpKenCyGE+Oijj4SXl5dh/PSv3a1bN8PX//rXv8SUKVPqvP6QIUPEgw8+2Lpv0o5MmTJFzJs3r859t912m5g5c6YQguMlJdcmq1IaG2Niae/spgygsrIShw4dwvjx4w33yeVyjB8/Hnv37rViZPatsLAQAODt7Q0AOHToELRabZ1xiIqKQnBwsGEc9u7di969eyMgIMBwzKRJk1BUVISTJ08ajrn6HPpj9OfgeLfMwoULMWXKlHo/U46XtPz6668YNGgQ7rzzTvj7+6N///743//+Z3g8OTkZmZmZdX6OHh4eGDJkSJ3x8vT0xKBBgwzHjB8/HnK5HPv37zccM3LkSDg4OBiOmTRpEhISEpCfn284pqkxJWDYsGHYvn07zp49CwA4evQodu/ejcmTJwPgeEmZlMbGmFjaO7tJVnNyclBdXV3nFyoABAQEIDMz00pR2TedTofHH38cw4cPN2yHm5mZCQcHB3h6etY59upxyMzMbHCc9I81dUxRURHKy8s53i2wYcMGHD58GK+//nq9xzhe0pKUlISPP/4YkZGR+PPPP7FgwQI8+uijWLt2LYArP++mfo6ZmZnw9/ev87hSqYS3t7dZxpTjdcVzzz2Hu+++G1FRUVCpVOjfvz8ef/xxzJw5EwDHS8qkNDbGxNLeKa0dANmuhQsX4sSJE9i9e7e1Q6FGXLhwAY899hi2bt0KR0dHa4dDzdDpdBg0aBBee+01AED//v1x4sQJfPLJJ5gzZ46Vo6Nrfffdd/j666/xzTffoGfPnoiPj8fjjz+OoKAgjheRGdnNzKqvry8UCkW9VcyXL19GYGCglaKyX4sWLcKmTZsQExODTp06Ge4PDAxEZWUlCgoK6hx/9TgEBgY2OE76x5o6xt3dHU5OThxvIx06dAhZWVkYMGAAlEollEolduzYgf/+979QKpUICAjgeElIhw4d0KNHjzr3de/eHWlpaQCu/Lyb+jkGBgYiKyurzuNVVVXIy8szy5hyvK54+umnDbOrvXv3xqxZs/DEE08YPsXgeEmXlMbGmFjaO7tJVh0cHDBw4EBs377dcJ9Op8P27dsxdOhQK0ZmX4QQWLRoEX7++Wf8/fffCAsLq/P4wIEDoVKp6oxDQkIC0tLSDOMwdOhQHD9+vM6bwNatW+Hu7m74RT106NA659Afoz8Hx9s448aNw/HjxxEfH2+4DRo0CDNnzjT8m+MlHcOHD6/XCu7s2bMICQkBAISFhSEwMLDOz7GoqAj79++vM14FBQU4dOiQ4Zi///4bOp0OQ4YMMRyzc+dOaLVawzFbt25Ft27d4OXlZTimqTEloKysDHJ53V+jCoUCOp0OAMdLyqQ0NsbE0u5Ze4WXOW3YsEGo1WqxZs0acerUKfHAAw8IT0/POquYqXUWLFggPDw8RGxsrMjIyDDcysrKDMc89NBDIjg4WPz999/i4MGDYujQoWLo0KGGx/WtkCZOnCji4+PFli1bhJ+fX4OtkJ5++mlx+vRp8eGHHzbYConj3XJXdwMQguMlJXFxcUKpVIoVK1aIxMRE8fXXXwtnZ2fx1VdfGY554403hKenp/jll1/EsWPHxLRp0xpst9O/f3+xf/9+sXv3bhEZGVmn3U5BQYEICAgQs2bNEidOnBAbNmwQzs7O9drtKJVK8Z///EecPn1aLF26tN23QrrWnDlzRMeOHQ2tq3766Sfh6+srnnnmGcMxHC/rKS4uFkeOHBFHjhwRAMS7774rjhw5IlJTU4UQ0hobY2Jpz+wqWRVCiJUrV4rg4GDh4OAgBg8eLPbt22ftkOwKgAZvq1evNhxTXl4uHn74YeHl5SWcnZ3FrbfeKjIyMuqcJyUlRUyePFk4OTkJX19f8eSTTwqtVlvnmJiYGNGvXz/h4OAgwsPD67yGHse75a5NVjle0vLbb7+JXr16CbVaLaKiosSnn35a53GdTideeuklERAQINRqtRg3bpxISEioc0xubq6YPn26cHV1Fe7u7mLu3LmiuLi4zjFHjx4V119/vVCr1aJjx47ijTfeqBfLd999J7p27SocHBxEz549xe+//27+b9iGFRUViccee0wEBwcLR0dHER4eLl544YU6bYw4XtYTExPT4O+rOXPmCCGkNTbGxNKeyYS4aqsNIiIiIiIJsZuaVSIiIiKyP0xWiYiIiEiymKwSERERkWQxWSUiIiIiyWKySkRERESSxWSViIiIiCSLySoRERERSRaTVSIiIiKSLCarRETtyLJlyyCTyRAbG2u1GMrKytCxY0c88MADVovBVAkJCVAqlfjoo4+sHQpRu8FklYjqiY+Px0MPPYQePXrA3d0dDg4OCAwMxIQJE/DOO+8gOzu73nNkMlmdm5OTEwIDA3H99dfjqaeewtGjRxt8rZSUlHrPdXBwQOfOnTFjxgwcO3bM0t9uu6cfg3vvvbdNXu/tt99GTk4OXnzxxTr3h4aGQiaTNfncxo5JS0vDww8/jMjISDg6OsLV1RVhYWGYMmUK3nzzTZSWltY53tTrtVu3bpg+fTpefvllFBcXt/A7JyJTcLtVIjLQ6XR45pln8M4770ChUGDkyJHo06cPXFxckJWVhb179+LkyZNwcXFBQkICOnbsaHiuTCaDj48PFi1aBADQarXIycnBkSNHcODAAQDAvHnz8NFHH0GtVhuel5KSgrCwMEREROCee+4BAJSUlGDfvn3Ys2cP1Go1tm/fjuHDh7fhT8J+5eTkICcnB8HBwXB2dgZwZQzmzJmDNWvWWPT1i4qK0LFjR9x6661Yt25dncdCQ0ORmpqKpn4tNXTM0aNHMXr0aBQUFGD48OEYMGAAXF1dkZaWhl27diEtLQ2JiYno0qWL4TmmXq8AcPz4cfTp0wfLly/HCy+80OqfCRE1QxAR1XruuecEADFgwACRmJjY4DGHDh0S48ePr/c4ANGtW7cGn3P8+HHRr18/AUDcc889dR5LTk4WAMSkSZPqPe+FF14QAMSoUaNM+4bIKPoxmDNnjsVf64MPPhAAxNatW+s9FhISIpr7tdTQMWPHjhUAxLp16xp8zj///CPy8/Pr3Gfq9arXp08fERISIqqrq5uMl4haj8kqEQkhhEhISBAKhUL4+fmJrKysZo/XarV1vm7ql78QQmRlZQk/Pz8BQOzfv99wf1PJamZmpgAgnJ2dm41n9erVAoBYvXq12Lhxo4iOjhZOTk7C19dXzJ07V2RmZjb4vKSkJDF//nzRuXNn4eDgIAIDA8WcOXNESkpKvWP1ifPFixfFrFmzREBAgJDJZCImJqbZ+DQajXj33XfFoEGDhKurq3BxcRHdu3cXTzzxhMjLyzMc9/fff4u5c+eKrl27ChcXF+Hi4iIGDhwoVq1a1eB59TFduHBB3H333cLHx0c4OTmJYcOGNZgQLl26VAAwxKz/uTV00x+Tnp4ulixZIoYMGSL8/PyEg4ODCAkJEQsWLBCXL19u9nu/2sCBA4W3t3eDSZ6pyaqTk5Pw9PRsURymXq96y5cvFwDEtm3bWvS6RNRyrFklIgDA2rVrUV1djQcffBB+fn7NHq9UKlt0fj8/Pzz00EMAgG+//bZFz22ujvFqP/74I+6880506dIFjz/+OHr37o3Vq1fj+uuvR35+fp1j9+/fj/79+2Pt2rUYOHAgHnvsMYwYMQJff/01Bg8ejKSkpHrnz83NxdChQ3Hs2DHcfffdeOCBB+Du7t5kTOXl5Rg7diwWL16MwsJCzJ07FwsWLEDXrl2xatUqpKamGo598803sXPnTkRHR2PRokW45557kJOTgwcffBBPPvlkg+fPz8/H8OHDkZiYiPvuuw/Tp0/H0aNHccMNN2Djxo1NxtavXz889thjAIC+ffti6dKlhltoaCgAYOfOnXjnnXcQEBCA6dOn45FHHkFERAQ+/vhjDB06FIWFhU2+xtVxHjlyBIMHD4Zcbr5fPz4+PigpKcGlS5fMds7mrtehQ4cCALZv32621ySiRlg7WyYiaRgzZowAILZv327S89HMTJUQQmzfvl0AECNGjDDc19TM6pIlSwQAMWbMmGZf/+oZwi1bttR5TF/esGjRIsN9lZWVIjQ0VLi5uYnDhw/XOX7Xrl1CoVCIm266qd73CEDMnTtXVFVVNRuT3pNPPikAiFmzZtV7XkFBgSguLjZ8nZSUVO/5Wq1WTJgwQSgUCpGamtpgTDNmzBA6nc5w/9GjR4WDg4Pw8/MTZWVlhvuvnVkVovkygMuXL9eJUW/t2rUCgFi+fHmT37/e77//LgCIF154ocHHTZ1ZXbx4sQAgwsLCxJtvvin++ecfUVpa2uR5TL1e9QoLCwUAMXLkyCbPQUStx5lVIgIAZGZmAgCCgoLqPRYbG4tly5bVuZnS+kh/7pycnHqPnTt3znDup59+GiNHjsQrr7wCR0dHrFixwujXGD9+PCZNmlTnvhdeeAGenp5Yt24ddDodAGDTpk1ISUnB008/jf79+9c5/vrrr8e0adOwefNmFBUV1XnMwcEBb731FhQKhVHxVFVV4dNPP4WHhwfef//9es/z8PCAq6ur4euwsLB651AqlXjooYdQXV2NmJiYeo8rFAq89tprdWag+/Tpg1mzZiE7OxubN282KtbG+Pv714lRb9asWXB3d8e2bduMOs/FixcBAAEBAa2K51orVqzAvffei9TUVDz77LMYNmwY3N3dMXDgQCxfvhwFBQUmnbep69Xd3R2Ojo6G74mILKdln+MRUbsUGxuLl19+ud79o0ePNttrnD9/3vAaKpUKAQEBmDFjBp577jn07t3b6POMGDGi3n2urq7o168fYmNjkZSUhC5dumDfvn0AavpmLlu2rN5zMjMzodPpcPbsWQwaNMhwf1hYGHx9fY2O58yZMyguLsb48ePh5eXV7PHFxcX4z3/+g40bN+L8+fP1Wi419FF3cHAwQkJC6t0/YsQIfP755zhy5Ahuv/12o2NuyE8//YRVq1bh8OHDyM/PR3V1dZMxNSQ3NxcA4Onp2apYruXo6IjVq1fj1VdfxebNmxEXF4e4uDgcPnwYhw8fxqpVq7Bjxw6Eh4eb9XW9vb0bTGSJyLyYrBIRgJrZrtOnT+PSpUuIioqq85h+xhMANmzYgOnTp5v0GvqkpqGa2EmTJmHLli0mnfdqjc3a6e/X11fm5eUBAL7++usmz3dtstjSWUH9613d5qsxlZWVGD16NA4fPoz+/ftj1qxZ8PHxgVKpREpKCtauXQuNRlPvecZ+z6Z655138NRTT8HPzw8TJ05Ep06d4OTkBAB47733GoypIfrnVFRUNPi4vo5Vp9M1WtOq0+karWHu1KkTHnjgAcNmA+fPn8e8efOwc+dOPPHEE/jll1+MilOvqesVqKlF1rf/IiLLYbJKRACAYcOGITY2FjExMRg7dqxFXkNfOhAdHW2R8wPA5cuXm7zfw8MDAAyLon777TfcdNNNRp+/JYu9gCuziOnp6c0e+8svv+Dw4cOYP38+PvvsszqPbdiwAWvXrm3wecZ+z6aoqqrCq6++ig4dOiA+Ph7+/v6Gx4QQeOutt4w+lz7p0/+hcC19nLm5uQ0miEII5OXlGf39REREYM2aNQgPD8fff/9tdJx6TV2vOp0OhYWF6NmzZ4vPS0Qtw5pVIgIAzJkzB3K5HJ9++qlFPtrMzs7GqlWrAAB333232c+vt2vXrnr3lZSUID4+Hu7u7oaPgocMGQIA2Lt3r8ViAWp2PHJ3d8eBAwfqdSO41vnz5wEA06ZNq/dYQ9+XXlpaWp2OAtc+59qa3Gvp62iv/mhfLycnB4WFhRg6dGidRBUADh48iPLy8ibPfTV9OUdCQkKTjzc2JseOHUNpaSn69Olj9Gs2VGtrjOau18TEROh0uhaVqBCRaZisEhEAoGvXrnjmmWeQlZWFyZMn49y5cw0eZ8pilZMnT2LixInIysrCnDlz6tSAmtu2bdvw559/1rlvxYoVKCgowOzZsw0fL0+bNg3BwcF49913sXPnznrn0Wq12L17d6vjUSqVePDBB1FYWIjHHnusXkJYWFiIkpISADDUnV77ujt27MD//ve/Rl+juroazz//fJ1dnY4dO4Yvv/wSfn5+uPHGG5uM0cvLCzKZDBcuXKj3mL+/P5ycnHD48GGUlZUZ7s/Pz8cjjzzS5Hmv1bt3b3h7e2P//v0NPj5nzhwAwJIlS+pdZxqNBs888wwAYPbs2XUee+WVVxqMXQiBN954A0DNojljGXO96r+HUaNGGX1eIjINywCIyGDFihWorKzEu+++i6ioKIwcORJ9+/aFs7MzsrKycOzYMcTFxRkWLF0rJyfHUNtaVVWF3NxcHD58GHFxcQCA++67Dx9++KFFv4ebbroJN998M+644w6EhoZi3759iImJQUREBF555RXDcWq1Gj/88AMmT56MUaNGYezYsejduzdkMhlSU1Oxa9cu+Pj44MyZM62O6ZVXXsG+ffvw5ZdfYt++fZg8eTLUajWSkpKwZcsW7N69G/369cPNN9+M0NBQvPXWWzhx4gR69eqFhIQEbNq0Cbfeeit++OGHBs/fp08f7N69G9HR0Rg/fjyys7Px7bffGjoR6GtFG+Pq6oro6Gjs3LkTs2bNQmRkJORyOWbNmoWQkBA8/PDDeOedd9C3b1/cfPPNKCoqwh9//IGQkJAGu0c0RiaTYdq0aVizZg0uXryITp061Xl83LhxeOyxx/D++++ja9eumDp1KgIDA5Gbm4vNmzcjLS0Nt956K+bOnVvnee+++y6WLVuGQYMGYeDAgfD29kZubi5iYmJw9uxZ+Pj44J133qkXT2uu161bt0KpVLaohISITGTdzllEJEWHDx8WDzzwgIiKihKurq5CpVKJgIAAMXbsWPH22283uGsRrtn9SK1WC39/fzF8+HDx1FNPiaNHjzb4Wk31WW2Jxnaw8vHxEffee6/IyMho8HkXL14Ujz32mIiMjBRqtVq4u7uL7t27i/vuu69ez1m0YuvXiooK8Z///Ef069dPODk5CVdXV9GjRw/x5JNP1tkKNCkpSdx+++3Cz89PODs7i+joaLFhwwYRExMjAIilS5c2GNOFCxfEXXfdJby9vYWjo6MYOnSo+Ouvv+rF0VCfVSFqdjC78cYbhaenp5DJZHWOqaysFCtWrDD8jIKDg8WTTz4piouLRUhIiAgJCTH657B//34BQLz55puNHvPjjz+KSZMmCV9fX6FUKoWnp6cYOXKk+Oyzzxrc+Wrnzp3iueeeE0OHDhVBQUFCpVIJV1dX0adPH/HUU0+JS5cu1XuOqderEEKUlpYKV1dXccsttxj9fROR6WRCXPW5ERGRjVqzZg3mzp2L1atX495777V2OG1GJpNh1KhRJvW9tZYRI0YgOzsbp06dMutOVm3ls88+w/33348dO3Zg5MiR1g6HyO7Z3rsEERHZtLfffhsJCQnYsGGDtUNpsaqqKrz22muYOnUqE1WiNsKaVSIialPXXXcdVq1a1WD3AalLS0vD7NmzMWvWLGuHQtRuMFklIqI2p2/cb2vCw8Mb3PGMiCyHNatEREREJFmsWSUiIiIiyWKySkRERESSxWSViIiIiCSLySoRERERSRaTVSIiIiKSLCarRERERCRZTFaJiIiISLKYrBIRERGRZP0//YhxdgYJHTsAAAAASUVORK5CYII=\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": "UPhMqQd97fRd",
        "outputId": "18d18677-3e50-4827-d55d-e74c1c9e7f55"
      },
      "execution_count": 43,
      "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": 43
        }
      ]
    },
    {
      "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": 551
        },
        "id": "hgMr12bu7lxW",
        "outputId": "1ee7f317-122a-4510-cd8b-5179faeb2324"
      },
      "execution_count": 44,
      "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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              "\n",
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            ],
            "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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))\n",
        "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
        "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
        "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
        "\n",
        "ridge = linear_model.Ridge(alpha=10**9.5)\n",
        "X_sample = country_stats[[gdppc_col]]\n",
        "y_sample = country_stats[[lifesat_col]]\n",
        "ridge.fit(X_sample, y_sample)\n",
        "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
        "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
        "         label=\"Regularized linear model on partial data\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "grV4YVgv7nH3",
        "outputId": "ec271b14-efb2-4e0e-e15c-8cb17176becd"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}