Vaibhav_chapter_1.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyMIb0zljsl4MPA/T0eJtpBi",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/Vaibhav_chapter_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "X1d_B54gcHYH"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "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",
        "link=\"https://github.com/ageron/data/raw/main/lifesat/lifesat.csv\"\n",
        "data=pd.read_csv(link)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "CMu4_lQLdAes",
        "outputId": "ecd5f75a-78db-47ff-b25a-4c278e6b774e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country  GDP per capita (USD)  Life satisfaction\n",
              "0   Russia          26456.387938                5.8\n",
              "1   Greece          27287.083401                5.4\n",
              "2   Turkey          28384.987785                5.5\n",
              "3   Latvia          29932.493910                5.9\n",
              "4  Hungary          31007.768407                5.6"
            ],
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>Russia</td>\n",
              "      <td>26456.387938</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Greece</td>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>29932.493910</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(kind='scatter', grid=True, color='purple',x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n",
        "plt.axis([23_500, 70_000, 4, 9])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "OGttF9TFdNVK",
        "outputId": "014bd0e3-7f75-4358-af16-fba75c01c32b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "X = data[[\"GDP per capita (USD)\"]].values\n",
        "y = data[[\"Life satisfaction\"]].values\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "X_new=[[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
        "y_pred=model.predict(X_new)\n",
        "print(y_pred)\n",
        "\n",
        "[[6.30165767]]\n",
        "\n",
        "# Plot the data points\n",
        "plt.scatter(X, y, color='green', label='Actual data')\n",
        "X_values = [[40_655.2],[35_000],[49_560],[32_500],[51_000]]\n",
        "y_pred_values=model.predict(X_values)\n",
        "# Plot the regression line\n",
        "plt.plot(X_values, y_pred_values, color='maroon', linewidth=3, label='Linear Regression')\n",
        "\n",
        "# Add labels and legend\n",
        "plt.xlabel('GDP per capita (USD)')\n",
        "plt.ylabel('Life Satisfaction')\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Vi49EpJIdbx4",
        "outputId": "1eb84d9b-ab26-4382-bab6-378351dbbf87"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "model.fit(X, y)\n",
        "print(model.predict(X_new))\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGVFabWUdtDN",
        "outputId": "6ce439c4-bf2d-4274-c0ad-b299d7f3f5ea"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Where to save the figures\n",
        "IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ],
      "metadata": {
        "id": "Kr_7bIR4erYO"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib.request\n",
        "\n",
        "datapath = Path() / \"datasets\" / \"lifesat\"\n",
        "datapath.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "for filename in (\"oecd_bli.csv\", \"gdp_per_capita.csv\"):\n",
        "    if not (datapath / filename).is_file():\n",
        "        print(\"Downloading\", filename)\n",
        "        url = data_root + \"lifesat/\" + filename\n",
        "        urllib.request.urlretrieve(url, datapath / filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfyfKBT5eyVI",
        "outputId": "cf061858-fdbe-401a-ec4a-3dae88c7c896"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading oecd_bli.csv\n",
            "Downloading gdp_per_capita.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")\n",
        ""
      ],
      "metadata": {
        "id": "rOjqjUELfBh_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_per_capita.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "VRut7xGbfKdX",
        "outputId": "0150fbd8-9fa8-4d49-dd11-15dedcea6b9b"
      },
      "execution_count": 13,
      "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",
              "5                   Afghanistan  AFG  2007   \n",
              "6                   Afghanistan  AFG  2008   \n",
              "7                   Afghanistan  AFG  2009   \n",
              "8                   Afghanistan  AFG  2010   \n",
              "9                   Afghanistan  AFG  2011   \n",
              "10                  Afghanistan  AFG  2012   \n",
              "11                  Afghanistan  AFG  2013   \n",
              "12                  Afghanistan  AFG  2014   \n",
              "13                  Afghanistan  AFG  2015   \n",
              "14                  Afghanistan  AFG  2016   \n",
              "15                  Afghanistan  AFG  2017   \n",
              "16                  Afghanistan  AFG  2018   \n",
              "17                  Afghanistan  AFG  2019   \n",
              "18                  Afghanistan  AFG  2020   \n",
              "19  Africa Eastern and Southern  NaN  1990   \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",
              "5                                         1460.825751    \n",
              "6                                         1484.114461    \n",
              "7                                         1758.904477    \n",
              "8                                         1957.029070    \n",
              "9                                         1904.559926    \n",
              "10                                        2075.491614    \n",
              "11                                        2116.465258    \n",
              "12                                        2102.384604    \n",
              "13                                        2068.265904    \n",
              "14                                        2057.067978    \n",
              "15                                        2058.400221    \n",
              "16                                        2033.804389    \n",
              "17                                        2065.036235    \n",
              "18                                        1978.961579    \n",
              "19                                        2867.192554    "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>Entity</th>\n",
              "      <th>Code</th>\n",
              "      <th>Year</th>\n",
              "      <th>GDP per capita, PPP (constant 2017 international $)</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2002</td>\n",
              "      <td>1189.784668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2003</td>\n",
              "      <td>1235.810063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2004</td>\n",
              "      <td>1200.278013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2005</td>\n",
              "      <td>1286.793659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2006</td>\n",
              "      <td>1315.789117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2007</td>\n",
              "      <td>1460.825751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2008</td>\n",
              "      <td>1484.114461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2009</td>\n",
              "      <td>1758.904477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2010</td>\n",
              "      <td>1957.029070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2011</td>\n",
              "      <td>1904.559926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2012</td>\n",
              "      <td>2075.491614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2013</td>\n",
              "      <td>2116.465258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2014</td>\n",
              "      <td>2102.384604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2015</td>\n",
              "      <td>2068.265904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2016</td>\n",
              "      <td>2057.067978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2017</td>\n",
              "      <td>2058.400221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2018</td>\n",
              "      <td>2033.804389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2019</td>\n",
              "      <td>2065.036235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2020</td>\n",
              "      <td>1978.961579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1990</td>\n",
              "      <td>2867.192554</td>\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdppc_col = \"GDP per capita (USD)\"\n",
        "lifesat_col = \"Life satisfaction\"\n",
        "\n",
        "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == 2020]\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",
        "gdp_per_capita.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "MoaOCFCSgN-8",
        "outputId": "16487ae7-041f-4d25-d1f5-f021f0e29ac6"
      },
      "execution_count": 17,
      "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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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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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": 17
        }
      ]
    },
    {
      "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": 386
        },
        "id": "H5tUjip7fapl",
        "outputId": "e2e64147-9f5c-4275-9ae4-6035a7596fbf"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
              "Country                                                        \n",
              "Australia            5.0                                 NaN   \n",
              "Austria             16.0                                 0.9   \n",
              "Belgium             15.0                                 1.9   \n",
              "Brazil              10.0                                 6.7   \n",
              "Canada               7.0                                 0.2   \n",
              "\n",
              "Indicator  Educational attainment  Employees working very long hours  \\\n",
              "Country                                                                \n",
              "Australia                    81.0                              13.04   \n",
              "Austria                      85.0                               6.66   \n",
              "Belgium                      77.0                               4.75   \n",
              "Brazil                       49.0                               7.13   \n",
              "Canada                       91.0                               3.69   \n",
              "\n",
              "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
              "Country                                                           \n",
              "Australia             73.0                                 63.5   \n",
              "Austria               72.0                                 80.6   \n",
              "Belgium               63.0                                 70.1   \n",
              "Brazil                61.0                                 35.6   \n",
              "Canada                73.0                                 82.2   \n",
              "\n",
              "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
              "Country                                                              \n",
              "Australia            1.1                                   32759.0   \n",
              "Austria              0.5                                   33541.0   \n",
              "Belgium              1.0                                   30364.0   \n",
              "Brazil              26.7                                       NaN   \n",
              "Canada               1.3                                   30854.0   \n",
              "\n",
              "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
              "Country                                               ...                      \n",
              "Australia              427064.0                 20.0  ...            49126.0   \n",
              "Austria                308325.0                 21.0  ...            50349.0   \n",
              "Belgium                386006.0                 21.0  ...            49675.0   \n",
              "Brazil                      NaN                  NaN  ...                NaN   \n",
              "Canada                 423849.0                 22.0  ...            47622.0   \n",
              "\n",
              "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
              "Country                                                                         \n",
              "Australia                        95.0               NaN                  85.0   \n",
              "Austria                          92.0               1.6                  70.0   \n",
              "Belgium                          91.0               2.2                  74.0   \n",
              "Brazil                           90.0               NaN                   NaN   \n",
              "Canada                           93.0               2.6                  88.0   \n",
              "\n",
              "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
              "Country                                                                        \n",
              "Australia                                                2.7           502.0   \n",
              "Austria                                                  1.3           492.0   \n",
              "Belgium                                                  2.0           503.0   \n",
              "Brazil                                                   2.2           395.0   \n",
              "Canada                                                   2.9           523.0   \n",
              "\n",
              "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
              "Country                                                               \n",
              "Australia                                      14.35           91.0   \n",
              "Austria                                        14.55           80.0   \n",
              "Belgium                                        15.70           89.0   \n",
              "Brazil                                           NaN           79.0   \n",
              "Canada                                         14.56           68.0   \n",
              "\n",
              "Indicator  Water quality  Years in education  \n",
              "Country                                       \n",
              "Australia           93.0                21.0  \n",
              "Austria             92.0                17.0  \n",
              "Belgium             84.0                19.3  \n",
              "Brazil              73.0                16.2  \n",
              "Canada              91.0                17.3  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
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              "      <th>Indicator</th>\n",
              "      <th>Air pollution</th>\n",
              "      <th>Dwellings without basic facilities</th>\n",
              "      <th>Educational attainment</th>\n",
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              "      <th>Australia</th>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>81.0</td>\n",
              "      <td>13.04</td>\n",
              "      <td>73.0</td>\n",
              "      <td>63.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>32759.0</td>\n",
              "      <td>427064.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49126.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>502.0</td>\n",
              "      <td>14.35</td>\n",
              "      <td>91.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.66</td>\n",
              "      <td>72.0</td>\n",
              "      <td>80.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>33541.0</td>\n",
              "      <td>308325.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50349.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>492.0</td>\n",
              "      <td>14.55</td>\n",
              "      <td>80.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
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              "      <td>63.0</td>\n",
              "      <td>70.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30364.0</td>\n",
              "      <td>386006.0</td>\n",
              "      <td>21.0</td>\n",
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              "      <td>49675.0</td>\n",
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              "      <td>2.2</td>\n",
              "      <td>74.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>15.70</td>\n",
              "      <td>89.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
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              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>395.0</td>\n",
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              "      <td>79.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>7.0</td>\n",
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              "      <td>523.0</td>\n",
              "      <td>14.56</td>\n",
              "      <td>68.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "    [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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2b8df0d0-11da-433a-aee1-547ba5b5e797 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-2b8df0d0-11da-433a-aee1-547ba5b5e797');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b40bb7b0-7772-4007-9a84-084690931753\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b40bb7b0-7772-4007-9a84-084690931753')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    border: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-b40bb7b0-7772-4007-9a84-084690931753 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": "oecd_bli"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
        "                              left_index=True, right_index=True)\n",
        "final_df.sort_values(by=gdppc_col, inplace=True)\n",
        "final_df = final_df[[gdppc_col, lifesat_col]]\n",
        "\n",
        "final_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "Fo6pjk0xgceT",
        "outputId": "d0e122b0-97ce-4101-e639-5dfb0efac5a0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5"
            ],
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              "\n",
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              "      <th>GDP per capita (USD)</th>\n",
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              "      <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",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-5cab5017-518b-4537-9a86-78f5e4aee0ff');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    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-2d6ceee4-4f88-4277-b0a3-a14a050b94e5 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": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"Luxembourg\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19969.978699662497,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          110261.157353302,\n          36215.4475907307,\n          50922.3580234484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7758405579940837,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          4.7,\n          6.9,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_gdp = 23_500\n",
        "max_gdp = 62_500\n",
        "\n",
        "country_stats = final_df[(final_df[gdppc_col] >= min_gdp) &\n",
        "                                   (final_df[gdppc_col] <= max_gdp)]\n",
        "country_stats.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "wx1NUX-4ghA9",
        "outputId": "6611d567-4646-4b46-a957-293ac573bdb2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         GDP per capita (USD)  Life satisfaction\n",
              "Country                                         \n",
              "Russia           26456.387938                5.8\n",
              "Greece           27287.083401                5.4\n",
              "Turkey           28384.987785                5.5\n",
              "Latvia           29932.493910                5.9\n",
              "Hungary          31007.768407                5.6"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "country_stats",
              "summary": "{\n  \"name\": \"country_stats\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(country_stats.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pgYVnSpgwsU",
        "outputId": "19213818-a065-483a-9f65-98b3470fa9a1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Russia', 'Greece', 'Turkey', 'Latvia', 'Hungary', 'Portugal', 'Poland',\n",
            "       'Estonia', 'Spain', 'Slovenia', 'Lithuania', 'Israel', 'Italy',\n",
            "       'United Kingdom', 'France', 'New Zealand', 'Canada', 'Finland',\n",
            "       'Belgium', 'Australia', 'Sweden', 'Germany', 'Austria', 'Iceland',\n",
            "       'Netherlands', 'Denmark', 'United States'],\n",
            "      dtype='object', name='Country')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col, color= 'violet')\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='brown', width=0.5,\n",
        "                                 shrink=0.10, headwidth=6))\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",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "ocJceitsg2Ov",
        "outputId": "c2bafd62-c547-40d2-ebf2-2886d37638e7"
      },
      "execution_count": 31,
      "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": [
        "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": 316
        },
        "id": "yNFSene8hzU1",
        "outputId": "6af08ae5-b26d-464d-8c33-cd38df42407f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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2tnDtWsb28ehR0j8PcXHKJWL3PH6ayKCXQYHPAjE3NCdgYECy8larW1G7eG0A2q9rz8E7B2ns3JiNnTamup+CpgUpaFrwo+ry7b/fMvXY1OSFyavFVe+rlLUpm+K9K9qvoM/WPthPs0dfpU/VolXpWrErZx6cSfPzHPI7sKL9CuovrU+JAiVY3GYxKpXqo9rwsUoUKMH5gec1WgcpewQGgrk5BAQkL2/VCmorpxrt28PBg9C4MWxM/VTD0jIGF5eU99kyYv165f7h6tVKzyggAIYNU4Kbl1fq73FwgBUrlEEjJUoo2dc1fJpQooTSS5WSaDToxcXFMW7cOFauXMnDhw+xs7OjV69e/Pjjjxr/o/q+8KhwbMxskvWI7r68y41nN/is/GcADK05lD5V+rDs/LI09zPpyCQmHZmU7mdd8b6CQ36HNF8f4TGCXlV6ARATE8Phw4epV69esv9oSxQokep7SxYsyaFeh4iMjiQ8KpyilkXpvLFzmtsDPIp4xIBtA2hdujX+9/0Zvns4s1rOSrcN6bExs0Ffpc+jyEfJyh9HPqaIRZFM71fKHcLDwcYGXN65+HD3Lty4AZ8ppxpDhyoDQJalfaqxYUMpundP/0/clStKsHrfyJFKb69LF+W5q6tSh8mT0w56jx7BgAHKiEx/fxg+HGZl/jTBxgb09QUvXxqn+Jwi8jTJNI0GvalTpzJ37lyWLVtGhQoVOH36NL179yZ//vx8ndoQKQ2yMbMhLCoMIURiQJ54ZCItS7WkfKHyADRwasDBOwfT3U9WXN4sZF6IQuaFACXo3Ta5TVmbsulexnmfuZE55kbmvHjzgj039/Brk19T3e7p66c0Xt6YcoXKsaHjBgKfBdJgaQOMDYz5venvGf68dxnpG1HNrhr7bu+jXdl2AMSLeA7cOcCQGkMytU8p97CxUebjCZHUU5o4EVq2hPLKqUaDBkpPLz0fc3nz9Wvl/tm79PWV+3WpefpU6XWWKwcbNii91QYNwNgYfs/caYKREVStKrhwoVBiWXy8cq9xiDxNMk2jQe+///6jbdu2tGrVCgAnJyfWrFnDKXUnouSARs6NeBv7lilHp9ClYhdWXVzFtsBtnOqnXl2z4vLmx9hzcw8CQRnrMtx8fpORfiMpa1OW3lV6p9g2XsTTYlULHK0cWff5Ogz0DChfqDx+PfxotLwR9pb2DPcYnuJ9EdER3Hx+M/F50IsgAh4GUNC0YGIP1ucTH7w2e1Hdrjputm7MC5lHZExkqvWQ8pZGjZRBK1OmKD2tVatg2zb156d9zOXN1q2VQOvgoFzePHdOmULRp0/KbePjlQEujo6wbh0YGCjB2c9PaYu9vdLre19EBNxMOk0IClIuoxYsmNT7HDo0nt69HVm+XFCrFsyYoQxO6S1PkxRu387YdhoNerVq1WLBggUEBgZSunRpzp8/z9GjR5k2bVqq20dFRREVlXRTNzw8HFB6OzExMWp9dsL2GX1fQeOCLPp0Ed/t/45fDv9CQ6eGHOhxgCJmRZLtIzY2lvj4eLXrk1nqtuNZ5DPGHBxDyKsQCpoUpH3Z9vxc/2eIVwblvO/n+j9Tp3gdVPGqxNfLW5dnV9ddFDIrlOrnngg+QZNVTRKf++z1AaCHaw8Wt14MQIcyHXjY+CE/HfiJh5EPcTR2ZPNnmyloXDDHvruspu6x0FaabkfBgrBokYrvvtPnl1+gYUPBgQNxFCmiDGZJEBurIj5ej5iYuBT7+Ng2TJsG48bpMXiwHo8fKz3Cfv3i+fHHeFLb5c8/q6hTR6BSJdWxfHnYtQsKFSLV95w4oaJJk6Q/wT7KaUKPHvEsXqy0qX37GA4dusn48RV59EhQubJg+/Z4ChYUqe5TW2XX71RYGPz9t4oVK/Q4dixj71EJkZVrDKgnPj6e77//nl9//RV9fX3i4uKYOHEi3333Xarbjxs3jvHjx6coX716NWZmZtld3Qy5+OoiO5/uZLTzaE1XRZJytYsXrdm5swSjR2ftikKSdouLgwsXCrF/vwMnTxYlOlofAJUqDCGsCAsLI1++fGm+X6M9vfXr17Nq1SpWr15NhQoVCAgIYNiwYdjZ2eGVyt3i7777Dp+Ef4dQenrFixenadOm6TYyNTExMfj5+dGkSRO17oV9iPldc86ePkvLli2zbJ/pya525KTc0AaQ7chp5uYqzp7VS/Vc05U2fIhsR5KrV2HFCj3WrNEjNDRpoGPZsoIePeJp3To2QyvNaDTojRw5km+//ZYu/x8i5erqyt27d5k8eXKqQc/Y2BhjY+MU5YaGhpn+Ij/mve/zXO7J+UfniYyOxHmWMxs6bsCjuEeW7PtDsrIdmpIb2gCyHTnB01MZih8ZCc7OemzYAB6pnGra3AZ15NV2PH8Oa9cqo3TfvadboIAyH9LLC6pXV6FS6RMenrH9ajTovX79Gr33hkjp6+sTn9YQKS33b89/NV0FScoT/pWnWq4VEwN79sDSpcoApuhopVxfXxnB6+UFn36qjIzNDI0GvdatWzNx4kQcHByoUKEC586dY9q0afRJbYhUOjZd3US36t1SLIosSZIk6Ybz55Ue3apV8PhxUnnlytCrl9KzS1hk+2NoNOjNmjWLMWPGMHjwYB4/foydnR1ffvklP/30k1r76bW5F98f+54BVQcwoNoAiloWzaYaS5IkSVnl8WNl1ZulS5OvHFO4sLLkm5eXEvSykkaDnqWlJTNmzGDGjBkftZ9C5oW4/+o+4w6NY8KRCXxW7jO83b2p41BH61Z2kSRJysuiomD7dqVXt2tXUlokIyNlfqSXFzRvnrn5lRmRK9bevOJ9Bb8QP3z9fTl27xjrLq9j3eV1uBZ2xdvdm+6VumNhZKHpakqSJOVJQsCNG1YMHarHunXKAJUE7u7K5csuXZQ5mtktVwQ9I30jurp2patrVwIeBjDHfw6rLq7i4uOLDNwxkFH/jqJX5V4Mdh9MGZsymq6uJElSnnD/PqxcCUuWGHDtWv3Ecjs76NFD6dWVK5ezdcp1+fSqFKnCgtYLCBkewrSm03Ap6EJ4VDh/nvqTsr5labKiCZuvbU41I7ckSZL0cd68gTVrlEuUxYvD6NFw7ZoKI6M4unSJZ88eJY/hlCk5H/Agl/T0UlPAtADDPYYz9JOh+N1SLn1uD9zOv7f/5d/b/+KQz4F65vVwj3THzip78tdJkiTlBULAf/8pA1LWr1cyZSSoUwe++CIWS8s9dOzYFENDzfa1cl1P7316Kj2auTRja9et3B56m9G1R2Ntak1weDArH6zEebYzPTb14ETICTS4IpskSZLOuXsXfvkFSpdWgtuiRUrAc3SEMWOUdFBHjkCfPgJzc+24upbrg967nKycmOI5hRCfEBa3Xkwps1JEx0Wz8sJKPBZ7UH1hdf469xdvYt5ouqqSJElaKSJCGXnZqBE4OcFPPynZIszNlQEpBw4oGQ9+/jl5TkRtkWsvb6bHxMCEHq49sL5nTeEqhZl/bj5rL63l7IOz9N3al5F+I+lTpQ+D3Aelm1xVkiQpL4iPh0OHlGC3caOy/Bso+Q4bNlQGpHToABY6MEg+T/X0UlPdrjpL2y0lxCeEqZ5TcbJy4vmb5/x+/Hdc/nSh1epW7Lyxk3ihm0ujSZK2uP0kggPXHxP0NFLTVclVsvN7vXlTuUxZooTSs1u2TAl4Li4wYQLcuaMkte3ZUzcCHuTRnl5qbMxsGFV7FCM8RrDzxk58/X3Zc2sPO2/sZOeNnZQoUIJB1QfRx62PRpPASpKuefk6mq/XBHD4xpPEsnqlCjGrqxv5zeTSgZmVXd9rWJgyGGXpUmVwSoL8+aFzZ6VX5+GRlNVe1+T5nt779PX0aV2mNbu/2E3gkECGfzIcKxMrbr+4zUi/kdhPs6fvlr6cfXBW01WVJJ3w9ZoAjt18mqzs2M2nfLXmnIZqlDtk5fcaFwe7d0PXrlCkCAwYoAQ8PT1l6sGaNfDgAcyfD7Vq6W7Ag0z29G7cuMGBAwd4/PhxiowI6q6bqc1KWZdiWrNpTGg0gdUXV+Pr70vAwwD+CviLvwL+4pNin+Dt7k3H8h0xNsjkkt+SlIvdfhKRrCeSIE4IDt94QtDTSJxtzDVQM92WVd/rlSvKJcuVK5WJ5AkqVFB6dN27KxPJcxO1g97ChQsZNGgQNjY2FClSJNnaliqVKlcFvQRmhmb0q9qPvm59+e/ef/j6+7LxykZOhJzgRMgJfPb40K9qPwZWH4hDfgdNV1eStMbd56/Tff3OMxn0MuNjvtdnz5QcdUuXwunTSeUFCyblqKtWTbd7c+lRO+hNmDCBiRMnMnr06Oyoj1ZTqVTUdqhNbYfaTG82nYVnFzL/zHxCwkOYfHQyU49NpU2ZNni7e9PYubFc7FrK8xwLmqX7upO17gW8208iuPv8NU7W5hoL2Op+rzExyuLOy5YpOepiYpRyAwMlR12vXtCqlbLoc26ndtB78eIFHTt2zI666BRbC1t+rPcj39b5lq3Xt+Lr78v+oP1svraZzdc2U8a6DIPdB+NV2Yv8Jvk1XV1J0ogShSyoV6oQx24+Je6dxR/0VSpqu9joVC9PmwbkZPR7DQhIylH35J2roW5uSo+ua9esyVGnS9QeyNKxY0f27t2bHXXRSQZ6BnQo14F9PfdxZfAVhrgPwdLIkuvPrjN091Dsp9kzcPtALj66qOmqSpJGzOrqRm0Xm2RltV1smNXVTUM1yhxtG5CT1vc6ppEb06Ypeejc3GDGDCXg2dqCj4+St+7sWRg6NO8FPMhET8/FxYUxY8Zw4sQJXF1dMXwv6dHXX3+dZZXTNeUKlWNWy1lMajyJFRdW4Ovvy5UnV5h/Zj7zz8ynnmM9vN29aV+2vczyLuUZ+c0MWd63BkFPI7nzLFKjlwUzSxsH5Lz7vQY+iOSmfz52bzKh3JfKaExQLle2bav06po1Uy5n5nVqfwULFizAwsKCQ4cOcejQoWSvqVSqPB30ElgaWzLYfTCDqg/i0N1D+Pr7sunqJg7fPczhu4cpalGUAdWULO92lrlsaJQkpcHZRveCXQJtHJAjBPj7w7Jl5qxZY86LF0mv1aypBLrOnXMmR50uUTvoBQUFZUc9ciWVSkUDpwY0cGpAaHgoC84sYP6Z+TyIeMD4Q+OZeGQi7cu2x9vdm3qO9eTAF0knaMNAjpyWXQNyMvNdhobCihXKvbpr15LK7e2TctSVLZup6uQJH9XZTchKIP9Yf5h9PnvGNxzPD/V+4J+r/+Dr78vR4KNsuLKBDVc2ULFwRbzdvfmi0hcyy7uklbRpIEdOy+oBOep+l69fw44dSqDz81N6eQCmpsqal15eyjJh+vqZa19ekqkVWZYvX46rqyumpqaYmppSqVIlVqxYkdV1y5WM9I3oUrELR3ofIeDLAAZUHYCZoRmXHl9i0I5B2E+z5+tdX3Pt6bUP70yScpC2DeTIaVk5ICcj36UQcOyYitmzq1C8uAHdu8PevUp53bpKGp+HD5WJ5U2ayICXUWr39KZNm8aYMWMYMmQItWvXBuDo0aMMHDiQp0+fMnz48CyvZG5VuUhl5reez9QmU1kWsIw5p+cQ+CyQWadmMevULBo7N8bb3ZvWZVpjoCfvQEuao40DOXJaVg3I+dB3eeTsaw5sN2P5crh1ywBwBJQ0Pl5eyiXMkiU/sjF5mNp/SWfNmsXcuXPp2bNnYlmbNm2oUKEC48aNk0EvE6xMrBj6yVC+qvkV+27vY7b/bLYHbmdf0D72Be2jeL7ifFntS/pX609h8zw4xlgCNHsvTdMDObKy7R+7r48dkJPadxkfpc/rwKJEXCxGvalJ9w8tLAQ1awbz3Xf2NGxogJ5cLfmjqR30Hjx4QK1atVKU16pViwcPHmRJpfIqPZUeTUo2oUnJJtx9eZd5p+ex6Nwi7oXf48cDPzL+0Hg6VeiEt7s3nxT7RN5LzSO04V6aplZWycq2a8P3CEnfpRDw9q41kZeK8TqwCCJG+XOsUgkaNVLh5QWtW8dy6FAA9erZyYCXRdT+Gl1cXFi/fn2K8nXr1lGqVKksqZQEjlaOTPaczL3h91jebjk17WsSEx/DqourqPVXLaotqMbis4t5HZP+f+CS7tOGe2kJAzn03/tHS1+lol6pQtnWy8vKtmvD9wgQ99ICy0tVuD+vEY/XfULk5WKIGAMMC0RSpf097txR8e+/ymVM89x9xVgj1O7pjR8/ns6dO3P48OHEe3rHjh1j3759qQZD6eOYGJjQo3IPelTuwZn7Z/D192XNpTWce3iOftv6MdJvJHUt61L6eWnK2ZbTdHWlLKZN99JmdXXjqzXnktUnO1dWycq2a/p7fPkyKUfd8eMA9gCojGMwL3cfi4oheNY3ZHY3N/Kn36mWPpLaQe+zzz7j5MmTTJ8+nc2bNwNQrlw5Tp06hZubbi0rpGuq2VXjr7Z/8VuT3/jr3F/MPT2XoJdBbH27la3zttLCpQXe7t40d2mOvp4cypUbaPpe2rtyemWVrGy7Jr7H2FhlesGyZbB5M0RFKeUJOeq8vKBSrWgeRJriZF0l1w8E0haZGhJYrVo1Vq5cmdV1kTLI2syakbVH4uPhw/br2/l518+cfXWWXTd3sevmLpytnBOzvFubWWu6ujpDGydda2OWgpxaWSUr256T3+Ply0k56t4d5lCxYlKOuqJFE0rNKYt2/K7lFRkKeuHh4eTLly/x5/QkbCdlP309fVq6tISSUPqT0iwKWMRfAX8R9DKIUf+O4qeDP9GlYhe83b2pbldd09XVWtoywCE1uSlLgbqysu3Z/T0+fapkF1+2DM6cSSq3tlZy1PXqpSz+LMeeaV6GBrIUKFCAx48fA2BlZUWBAgVSPBLKJc1wKejCH83+INQnlEWtF+FWxI23sW9ZGrAU94XufLLoE1acX8Hb2LearqrW0ZYBDmnJLVkKMiMr257V32NMDGzZoqyIYmcHX3+tBDwDA2WR502blGzkf/4JVavKgKctMtTT279/PwX/v2rpgQMHsrVC0scxMzSjb9W+9HHrw4mQE/j6+7L+8npOhp7kZOhJfPb60M9NyfLuaOWo6epqnKYHOGREbshSkFlZ2fas2JcQyXPUPX3nf6WqVZNy1BUqlKkqSjkgQ0Gvfv36iT87OztTvHjxFHPEhBDcu3cva2snZZpKpcKjuAcexT34o+kfLDq7iHln5hESHsKUY1P49b9f+bT0p3i7e+NZwhM9Vd6cBKRNA0U+RJezFHysrGx7Zvb18KES5JYtg4vvpMa0tU1a5LlixSypnpTN1P5L5+zszJMnKf8zfv78Oc7OzllSKSlr2VrY8kO9HwgaGsQ/nf6hsXNj4kU8W69vpdnKZpTzLcfMEzN5+falpqua47RxoIikHd6+hQ0b4NNPoVgxwTffKAHP2Bg6dVIWgA4Jgd9+kwFPl6gd9IQQqa4EEhERgYmJSZZUSsoeBnoGtC/Xnn97/stV76t8VeMrLI0sCXwWyLA9w7CfZs+X277kwqMLmq5qjtHUpGtJOwkBJ07AoEHKCMuE4BYXp8LI7gUFm16k7a9nWLAkhpYtZVJWXZThQ+bj4wMol83GjBmDmVnSf8hxcXGcPHmSKlWqZHkFpexR1qYsf7b4k4mNJrLywkp8/X25/OQyC84uYMHZBdRxqIO3uzcdynXASN9I09XNVjk96VrSPiEhSTnqrl9PKjcrEIVR2XuYVQjB0DoSgNMPVHy15hzL+9bQUG2lj5HhoHfunDKSTQjBxYsXMTJK+kNoZGRE5cqV+eabb7K+hlK2sjS2ZJD7IAZWH8jhu4eVLO/XNnE0+ChHg49SxKIIA6oqWd7t89lrurrZIi8PFMnLXr9WRlguXQr79iXPUffZZ9C07Rt+PLWf9293a9MgJ0l9GQ56CaM2e/fuzcyZM7NkPp6TkxN3795NUT548GB8fX0/ev9SxqlUKuo71ae+U33uv7rPgjMLWHBmAQ8iHvDz4Z+VLO/llCzv9R3r58rFrvPyQJG8Qgg4ckTp0W3YAK9eJb1Wr54yn+7zz8HSEg5cf4XqdNr70qZBTlLGqX1FesaMGcTGxqYof/78OQYGBmoFQ39/f+Li4hKfX7p0iSZNmtCxY0d1qyVlITtLO8Y1GMcPdX9g07VN+Pr7cvjuYTZe2cjGKxupUKgCg90H06NSDyyNLTVdXUn6oKAgWL5cedy+nVRukP815hVDMK8YgqO7BR06u2H5/wUJ5CCn3EntgSxdunRh7dq1KcrXr19Ply5d1NpXoUKFKFKkSOJj+/btlCxZMtkUCUlzDPUN6VShE4d6HeLCwAt8We1LzA3NufzkMt47vbGfZs+QnUO4+uSqpqsqSSm8eWPA0qUq6teHEiVg3Dgl4FlaQsk6Tyja7Th2Xx7Aqs4NDK3epFiQQA5yyp3U7umdPHmSadOmpShv0KABP/zwQ6YrEh0dzcqVK/Hx8Unz0llUVBRRCau2krQkWkxMDDExMWp9XsL26r5P2+RUO8oWLMusZrOYUH8CKy6uYN6ZeQQ+D8TX3xdff18aOjZkYLWBtC6tfpZ3eSy0iy63Iy4ODh5UsWyZik2bmhEV9W6OOkGPHvG41Ymg418nSXmNQnDy9mNuPgzD0Vrp5U3vWJGRGy9w7FbSLPTaJa357fOKOfL96PKxeFdOtCOj+1YJ8c5CdBlgbm7OiRMncHV1TVZ+8eJFatasyevXmcvvtn79erp160ZwcDB2dnapbjNu3DjGjx+fonz16tXJRpNK2U8IwYWIC+x8uhP/MH/iiQfA2tCaZtbNaGrdFCtDK81WUsozQkPNOXDAgQMHivPsmWliub39Kxo2vEf9+vcoVEguwZebvX79mm7duhEWFpbubTa1g17Dhg2pWLEis2bNSlbu7e3NhQsXOHLkSKYq3KxZM4yMjNi2bVua26TW0ytevDhPnz5Ve2BNTEwMfn5+NGnSBENDzS4q/DG0oR3BYcEsOLeAJQFLePJaGfZvqGdIh7IdGFRtEB7FPNId+KINbcgKsh1Z487TSO69eI1DQfPEHldqXryADRv0WLFCxcmTSXdqrKwEn38eS+nSxxk8uDpGRsnbcOdpJJ/OPprmfnd8VTfdz81Jmj4WWSWhHWWq1eZ+ePQHj21mhIeHY2Nj88Ggp/blzQkTJuDp6cn58+dp3LgxAPv27cPf35+9e/dmqrJ3797l33//5Z9//kl3O2NjY4yNjVOUGxoaZvoX4mPeq0002Y6SNiWZ2mQqPzf8mQ1XNuDr78uJkBOsu7KOdVfWUaVIFbzdvenm2g0zw7R/0eWx0C453Y6MZLuIjYW9e5XRl1u2JOWo09dPylHXurUKfX3YufMFRkYp21CqqBU1SxROM+OCS5H82d9YNen671TYm2gA2s49QVSc8g9wVmcyyej3o/ZAltq1a3P8+HGKFy/O+vXr2bZtGy4uLly4cIG6deuqXVGAJUuWULhwYVq1apWp90vawdjAmC8qfcHxvsc5M+AMfar0wcTAhICHAfTf1h/7afb47PHh5vObmq6qpIXSy3Zx8SJ88w0ULw6tWilZyKOiwNUVfv9dmVy+fTt07AgZWRgqL2eu0IRRGy+mKNNUJpNMLaJTpUoVVq1alSUViI+PZ8mSJXh5eWEg1/TJNaoWrcritov5relvLDm3hDmn53D7xW2mn5jO9BPTaVayGd7u3rQs1VLTVZW0QGrZLuJeGxJ+xZ51S4qx4lFSuY1NUo66KlUyl7JHLkiQc24/ieDYrae0fS+ftaYm+X9UlHn79i3R0dHJytS9t/bvv/8SHBxMnz59PqYqkpYqaFqQEbVGMNxjOLtv7sbX35ddN3ax59Ye9tzag5OVEwPcBlAstpimq6px2pi5PackZLsQcSre3CpMxKVivLlVGOKVi1EGhoLWn6rw8oIWLcAoi1bGkwsSZD9ty2SidtB7/fo1o0aNYv369Tx79izF6+9ONs+Ipk2bouZYGkkH6an0aFmqJS1LteTW81vMOz2PxecWc+flHb4/8D2GKkP8VH58VfMr3O3dNV3dHKXNmdtzghAQfs+C5/+WJ/KKHfFvku7bGxV5iXnFUPxmO1GtjAxOukjbJvmrfU9v5MiR7N+/n7lz52JsbMyiRYsYP348dnZ2LF++PDvqKOUyJQuW5LemvxHqE8pfbf7CrYgbMSKGFRdXUGNRDWosrMGygGV5Jsu7tmduzy4PHij34ypVgnZNzHh1xpn4N8bom78lX41bFO1ziGK9/uPTLpEy4OmwEoUsqF3SJkW5pib5q93T27ZtG8uXL6dBgwb07t2bunXr4uLigqOjI6tWraJ79+7ZUU8pFzI1NKW3W2+6V+jOjI0zOG98no1XN+J/359eW3oxYu8I+lVVsrw7WTlpurrZQhcyt2elt29h61Zlkec9eyBemd6JsTG0ah3PS/sb3DS6hUpPufpT26WQHFySC/z2eSWOHvBLVqapgUNqB73nz59TokQJQLl/9/z5cwDq1KnDoEGDsrZ2Up6gUqkoY16G4S2HM735dBafXcy8M/MIDgtm6rGp/HosKct7k5JNclWWd22735EdEnLULVsG69bBy5dJr3l4KANSOnUCKys9oAxBT4vJwSW5TD5T5TL9jq/qci8sSqPHVu2gV6JECYKCgnBwcKBs2bKsX7+eGjVqsG3bNqysrLKhilJeUti8MN/V/Y6RtUeyI3AHs/1n8+/tf9kWuI1tgdsoVbAUg6oPoleVXhQwLaDp6n40bbvfkZXu3UvKURcYmFRevDj07Kk8SpdO+T45uCT3crQ20/g8SLX/Ze7duzfnz58H4Ntvv8XX1xcTExOGDx/OyJEjs7yCUt5koGdA27Jt8evhxzXva3xd42vyGefjxvMb+Oz1wX6aPQO2DeD8w/OarupHyW2LGkdGKoHO0xMcHeGHH5SAZ2YGPXooeevu3IEJE1IPeJKU3dTu6Q0fPjzxZ09PT65du8aZM2dwcXGhUqVKWVo5SQIoY1OGmS1mMrHxRFZdWIWvvy8XH19k4dmFLDy7kNrFa+Pt7s1n5T/TySzvup65PT4+eY66iIik1+rXVy5ffvaZkt1AkjQtQ0GvYMGCBAYGYmNjQ58+fZg5cyaW//8NdnR0xNHRMVsrKUkAFkYWfFn9SwZUG8DR4KPM9p/NP1f/4di9Yxy7dwzbPbb0r9qfL6t/SbF8ujPvT1cnSt++reSnW7ZM6b0lKFFCWQ6sRw9wdtZY9SQpVRm6vBkdHZ2YxmfZsmW8fZs3hpJL2kmlUlHXsS7rPl9H8LBgxtUfR1GLojyKfMSEIxNwmuHE5+s/50DQAZ2aA+psY07DMoW1OuCFh8PixUqW8ZIlYfx4JeBZWkLfvnD4MNy8CT/9JAOepJ0y1NPz8PCgXbt2VKtWDSEEX3/9Naampqlu+9dff2VpBSUpPUUtizK2wVi+r/s9m69txtffl0N3D/H31b/5++rflC9UnsHVB9Ojcg/yGau3WpCkiIuD/fuVHt0//8CbN0q5SgVNmii9unbtlPt2kqTtMhT0Vq5cyfTp07l16xYqlYqwsDDZ25O0iqG+IR0rdKRjhY5cenyJOf5zWH5+OVeeXGHIriF8u+9belbqiXcNb8oXKq/p6uqEa9dg9WpYuVJZ0DlB2bJKoPviCyimO1eRJQnIYNCztbVlypQpADg7O7NixQqsra0/8C5J0oyKhSsyp9UcJjeezPLzy5lzeg7Xnl5jzuk5zDk9hwZODRjiPoS2ZduqneU9t3vxAlat0mPWrLoEBiYtgVagAHTtqgQ7d/fMLfIsSdpA7TM+KCgoRdnLly/lHD1J6+Q3yc9XNb9iSI0h7A/aj6+/L1uub+HgnYMcvHMQe0t7vqz2Jf2r9aeIRRFNV1djYmOV1VGWLlVWS4mO1gcKoq8vaNFC9f8cdcqqKZKk69Sepzd16lTWrVuX+LxTp04ULFgQe3v7xPl7kqRNVCoVjUs05p/O/3Bn6B1+qPsDhc0LE/oqlJ8O/oTDdAe6/t2Vo8FHtWrgy+0nERy4/pigp5HZsv8LF2DECOUS5aefwsaNEB0Nrq6C3r0vcedOLNu2weefy4An5R5qB7158+ZRvHhxAPz8/PDz82P37t20aNFCTk6XtF7x/MWZ0GgCwcOCWdVhFR7FPIiJj2HtpbXUXVKXKvOrsPDMQiKjsyfQZMTL19H0XHyKRn8covcSfxr+fpCei08R9jrmo/f95AnMnAlVq0LlyjBtGjx6BIUKwbBhcO4cnDkTS9u2t7C1/fi2SJK2UTvoPXz4MDHobd++nU6dOtG0aVNGjRqFv79/lldQkrKDsYEx3Vy78V/f/zg74Cx93fpiamDKhUcXGLB9APbT7Bm+ezg3nt3I8bplddaF6Ghl1GXbtmBnlxTcDA2hQwfYsgVCQ2H6dCUpqyTlZmoHvQIFCnDv3j0Adu/ejaenJwBCCLVz6UmSNnAr6saiNosI9Qnlj6Z/ULJAScKiwphxcgalZ5em2cpmbL2+lbj47P/9Tsi6EPfeZdZ3sy5khBBw+jR89ZUS6D77TLlfFxsL1avD7NlKap+//4Y2bZQAKEl5gdoDWTp06EC3bt0oVaoUz549o0WLFgCcO3cOFxeXLK+gJOWUAqYF8PHwYdgnw9h7ay++/r7sCNzB3lt72XtrL475HRlYfSB93fpSyLxQttThY7MuPHigTDFYuhSuXEkqL1pUWSHFywvKyxkbUh6mdtCbPn06Tk5O3Lt3j19//RULCwsAHjx4wODBg7O8gpKU0/RUejR3aU5zl+YEvQhi7um5LD63mLthd/lu33eMOziOzhU74+3uTQ37Gln62ZnJuvDmjXKJctky2Ls3KUediYkyadzLS1kA2kDOzpAk9YOeoaEh33zzTYrydxeilqTcwrmAM782+ZXxDcaz7vI6fP19OX3/NMvPL2f5+eVUt6uOt7s3nSt0xkD90ymFhKwLx24+TXaJU1+loraLTWIvTwg4fjwpR11YWNI+atdWAl2nTpBfs1lcJEnrZOgs3bp1Ky1atMDQ0JCtW7emu22bNm2ypGKSpE1MDU3pVaUXvar04lToKXz9fVl7aS2n75+m95bejNg7gl6Ve1EmqsxHf1Z6WReCg5VFnpcvhxvvjLFxcEjKUVeq1EdXQZJyrQwFvXbt2vHw4UMKFy5Mu3bt0txOpVLJwSxSrlfDvgY17Gvwe5PfWXxuMXNPzyU4LJhpJ6ahQsWWmC18VfMrmpZsmqks7+9nXShsYs6ZQ+Z0aA0HDii9PABzc2WASq9eSgofvdyTUF6Ssk2Ggl58wk2C936WJE24/SSCu89fazwFTyHzQnxb51tG1hrJjhs7mH1yNn5Bfuy8uZOdN3fiUtCFQdUH0btKb7WzvMfHw91L5ixfZs6GDUpy1gQNGyqXLz/7DP5/S12SpAxS+ybE8uXL6dy5M8bvLdEQHR3N2rVr6dmzZ5ZVTpLe9fJ1NF+vCUh22a9eqULM6upGfjPNjbnX19OnTZk2tCjRgoX/LOS65XWWXVjGzec3GbF3BD/u/5Furt3wdvfGrWj6iWFv3VLu061YkTxHXcmSSTnqnJyytTmSlKupfUGkd+/ehL171/z/Xr16Re/evbOkUpKUmqyetJ0d7E3s+b3J74T6hDL/0/lUsq3Em9g3LD63mKoLqlJrcS1WX1xNVGxU4nvCwmDRIqhbF1xc4JdflICXLx/07w9Hjyr378aMkQFPkj6W2j09IQSqVJZYDwkJIb8cKiZlk4RJ2+97d9K2NiVfNTcyZ0C1AfSv2p9j947h6+/LxisbOR5ynOMhxxlmOgJPJvPavyN7tpuTkKlLTy95jro00lZKkpRJGQ56bm5uqFQqZfHexo0xeGfST1xcHEFBQTRv3jxbKilJHztpW1NUKhV1HOpQx6EO05tNZ9I/m1iyNI4np9ux5lVSMjoHl0gG9TWjRw8V9vYarLAk5XIZDnoJozYDAgJo1qxZ4qR0ACMjI5ycnPjss8+yvIKSBJmbtK0tnj+HtWth2bIinDo1KLHcwDyM2PIroMoygu1Os8yyLBah3vS06SmzvEtSNslw0Bs7diwATk5OdO7cGRMTk2yrlCS9L6OTtrVFTExSjrpt25RFnwH09aFlS2WaQatW+bkZ1pA5/ldZfuEa155e46tdX/Hdvu/oUakH3u7eVChcQZPNkKRcR+2BLF5eXjLgSRoxq6sbtV1skpUlTNrWFkFB+Rg5Uo9ixZTEq3//rQS8ypWVLAb37ysLP3fooOSoq1C4Ar6tfAn1CWV2i9mUsylHRHQEc0/PpeLcijRY2oANlzcQE/fxaYUkScrEQJa4uDimT5/O+vXrCQ4OJjrhX9j/e/78eZZVTpLe9f6kbU3P00vw+DGsXg1Llhhw4ULDxPLChaF7d2VQSuXK6e8jn3E+vGt4M9h9MAfuHFCyvF/bwqG7hzh09xB2lnYMqDqAAdUGUNSyaDa3SJJyL7V7euPHj2fatGl07tyZsLAwfHx86NChA3p6eowbNy4bqihJyTnbmNOwTGGNBryoqKS0PPb2MHw4XLigwsAgjvbt49m6FUJClCStHwp471KpVDRybsTfnf7mzrA7/Fj3R2zNbbn/6j7jDo3DYYYDXTZ24cjdI1qV5V2SdIXaQW/VqlUsXLiQESNGYGBgQNeuXVm0aBE//fQTJ06cyI46Shpw+0kEB64/znD+trxACPD3hyFDlBx1n3+u3K+LjYUaNeDPP+NYsmQP69bF0br1x+eoK5avGL80+oXg4cGs7rCa2sVrExsfy7rL66i3tB6V51Vm/un5RERHZE0DJSkPUPvy5sOHD3F1dQXAwsIicaL6p59+ypgxY7K2dlKO09ZVTzTp/v2kHHVXryaV29kl5agrVw5iYuLZuTPr770Z6RvR1bUrXV27EvAwgDn+c1h5YSUXH19k4I6BjPp3FL0q92Kw+2DK2Hz8gteSlJup3dMrVqwYDx48AKBkyZLs3bsXAH9//xRLk0m6RxdWPckJb97AmjXQvDkULw6jRysBz8QEunVTRmYGB8OUKUrAyylVilRhQesFhPqEMq3pNFwKuhAeFc6fp/6krG9Zmq5oypZrW3Iky7sk6SK1g1779u3Zt28fAF999RVjxoyhVKlS9OzZkz59+mR5BaWck7DqSdx794reXfUkNxMCjh1Tlv4qUiQpuMXHQ506sHAhPHwIq1ZB06bK9ANNKWBagOEew7k+5Dq7u++mdenWqFDhd9uPduvaUeLPEkw+MpknkSlXsZGkvEzty5tTpkxJ/Llz5844ODhw/PhxSpUqRevWrbO0clLO0tVVTz7W3btJOepu3kwqd3RU8tM1aBWJykoZLZo/v3a1X0+lRzOXZjRzacadl3eYd3oei84uIjgsmO/3f8+4Q+PoVKET3u7e1LSvmeoSgpKUl3x0Bi4PDw98fHwyHfBCQ0P54osvsLa2xtTUFFdXV06fPv2x1ZIyQZdXPVFXRISSzaBRI2UR559+UgKeubkycfzAATh7KZo7jqfos+kgvZf40/D3g/RcfIqw19o5Z87JyokpnlMI8QlhaduluNu5Ex0XzcoLK/FY7IH7QneWnFvCm5g3mq6qJGmM2kFv2bJl7NixI/H5qFGjsLKyolatWty9e1etfb148YLatWtjaGjIrl27uHLlCn/88QcFCqiXe0zKGgmrnui/1xvQV6moV6qQzvfy4uNh/35l4EmRIknBTaVSgt+yZcrlyyVLoEEDGLZON+9vmhiY4FXFi1P9T3Gq3ym8KnthrG/MmQdn6LO1D8WmF2Pk3pHcfnFb01WVpByn9uXNSZMmMXfuXACOHz/O7NmzmTFjBtu3b2f48OH8888/Gd7X1KlTKV68OEuWLEksc3Z2TnP7qKgooqKSUrKEh4cDEBMTQ0yMev99J2yv7vu0TVa3Y3rHiozceIFjt5L+2Ncuac1vn1fMtu8qu4/FzZuwYoUeq1bpERycFNBdXAQ9esTTvXs8Dg7v1gfuPI3k5O3HGOi9f5IITt5+zM2HYThaJ+8Za+PvVJXCVVjYaiGTG05m6fmlLDi7gDthd/j9+O/8cfwPmpdszsBqA2lWsllilndtbIe6ckMbQLYjM5/xISqh5gxXMzMzrl27hoODA6NHj+bBgwcsX76cy5cv06BBA548yfiN8/Lly9OsWTNCQkI4dOgQ9vb2DB48mP79+6e6/bhx4xg/fnyK8tWrV2Nmlv6lOSlviYw04Ngxe/bvL861a9aJ5WZmMdSpE0qjRsGUKfOCvHaLK07EcSb8DLue7uLcq6Qeq62RLS1sWtC4YGMsDSw1WENJypzXr1/TrVs3wsLCyJcv7QXb1Q56hQsXZs+ePbi5ueHm5oaPjw89evTg1q1bVK5cmYiIjE+UTVjD08fHh44dO+Lv78/QoUOZN28eXl5eKbZPradXvHhxnj59mm4jUxMTE4Ofnx9NmjTB8GNnEWvInaeRBD99RcTtszrdjo89FneeRnLvxWuIV3HqmBFHd1uyb7chb98qEU1PT9CkieCLL+Jp00ZkKEfdnaeRfDr7aJqv7/iqbqo9PV36nbrx/AYLzi5g2YVlvHz7ElAujXYq14nKUZUZ2HagTrQjNbp2LNIi25Fx4eHh2NjYfDDoqX15s0mTJvTr1w83NzcCAwNp2bIlAJcvX8ZJzbTO8fHxVK9enUmTJgFKzr5Lly6lGfSMjY1TnQtoaGiY6S/yY96rKe9OIDfWF/xaA4asvcD0LtV0egK5usci4Xv49/gbIi8VI/KyPXERSYuhlysn6N1bRffuKuzsVKhzC7tUUStqliicZlYHlyJpJ0zWld+p8rblmdFiBhMbT2TNpTX4+vsS8DCA5ReXA7B+9XqG1BhCx/IdMTbQzTm4unIsPkS2I2P7zgi1B7L4+vri4eHBkydP+Pvvv7G2Vi4dnTlzhq5du6q1r6JFi1K+fPlkZeXKlSM4OFjdauUpqU0gP3H7mdYPsMhKz55B0/73Wf9jaR4srk/4yZLERZigZxKNZdU72Hsdo5qPPyNHKiunZIYuZHXICuZG5vSr2o+zA85ytPdROpfvjIHKgJOhJ+mxqQfFpxfn+33fExwmz0tJ96nd07OysmL27NkpylO71/YhtWvX5vr168nKAgMDcXR0VHtfeUXCBPL3vTuBXNdHWaYlJgZ27VJGWW7bJoiJcVJe0IvHtMRjLFxDMC35GJW+0jM7cpOP+j60NatDdlGpVNR2qE2NojVooWrBHes7LDy3kNBXoUw+Opmpx6bSpkwbvN29aezcWM75k3SS2kEvKw0fPpxatWoxadIkOnXqxKlTp1iwYAELFizQZLW0Wl6cQB4QoAS6VasgaZyUCiPbMMwrhmBe7j765tGpvjcrvg9nm9wd7FJjZWjF93W+54f6P7D1+lZ8/X3ZH7Sfzdc2s/naZspYl2Gw+2C8KnuR3yTtS72SpG0+enL6x3B3d2fTpk2sWbOGihUr8ssvvzBjxgy6d++uyWp9kCYzEOSVCeSPHiWl5XFzgxkzlIBnaws+PrDjwGuK9jpKvup30gx4kHu+D00x0DOgQ7kO7Ou5j8uDL+Pt7o2lkSXXn11n6O6h2E+zZ+D2gVx8dFHTVZWkDNFoTw+U7AyffvqppquRIdqQgSBhAnlqAyx0fQJ5VBRs2aL06nbtgrj/r5lsZARt2yqTyps1AwMDADPq3Ur5PSRIGHCiy9+HtilfqDyzW85mcuPJrLiwAl9/X648ucL8M/OZf2Y+9Rzr4e3uTfuy7THU1/1BF1LupNGenq7RlgwEqQ2w+KSEtU4OsFBy1KmYN68SDg4GdOwI27crAa9mTZgzBx48gPXroVWrhICnSO17SJAbB5xoC0tjSwa7D+bSoEsc8DrA5+U/R1+lz+G7h+m8sTOOMxwZd3Ac91/d13RVJSmFTPX0YmNjOXjwILdu3aJbt25YWlpy//598uXLh4WFRVbXUSto0wCSdwdYBD0O49UNf+b3qKZTQ5pDQ2HFCqVXd+2aAaCsxGNvn5SjrmzZ9Pfx/kATAz0VsfEi1w840RYqlYoGTg1o4NSAkPAQFpxZwIIzC3gQ8YDxh8Yz8chE2pdtz5AaQ6jrUFcOfJG0gtpB7+7duzRv3pzg4GCioqJo0qQJlpaWTJ06laioKObNm5cd9dQ4bRxA4mxjTrH8Ruy8kaMfm2mvX8PmzUqg8/NTenkApqaCGjVCGD26KE2bGiRL2XP7SQR3n79ON5DlxYEm2qZYvmL83PBnfqz3I/9c/Qdff1+OBh9lw5UNbLiygYqFK+Lt7s0Xlb7Awih3/mMs6Qa1L28OHTqU6tWr8+LFC0zfWdri3Tx7uVFeGUCS1YSAo0ehXz9lkefu3WHvXqW8bl1YtAju3Ytl+PCzeHqKxID38nU0PRefotEfh3Qiw4GkMNI3okvFLhzpfYSALwMYUHUAZoZmXHp8iUE7BmE/zZ6vd33NtafXNF1VKY9SO+gdOXKEH3/8ESMjo2TlTk5OhIaGZlnFtE1uz0CQ1e7cgZ9/hlKllOC2eDG8eqWk8Rk7Fm7dgsOHoW9fSG3FIG25fyplXuUilZnfej6hPqFMbzadUgVLER4VzqxTsyjnWw7P5Z5svraZ2PhYTVdVykPUDnrx8fHEJQyre0dISAiWlrl7odq8skJHZr16BUuXKml5nJ2TgpuFBfTuDQcPKs/HjYMSJdLeT17P4J7bWJlYMeyTYVwbco09X+yhTZk26Kn02Be0j/br2lNiZgkmHZnE48jHmq6qlAeofU+vadOmzJgxI3ECuUqlIiIigrFjxyauw5lb5bUVOjIiPl7JSbdsGfz9t3LfDpJy1Hl5QYcOSnLWjNLG+6fSx9NT6dG0ZFOalmzK3Zd3lSzv5xZxL/weP+z/gfGHxtOxfEe83b35pNgncuCLlC3UDnp//PEHzZo1o3z58rx9+5Zu3bpx48YNbGxsWLNmTXbUUevo4sCJjAwIUceNG0qgW7EC3l0qtVQpJTnrF1+QLEedOuT909zP0cqRyZ6TGdtgLBsub2C2/2xOhZ5i1cVVrLq4Crcibni7e9PVtStmhjJtmJR11A56xYoV4/z586xbt47z588TERFB37596d69e7KBLZJ2yMoJ9S9fKvPlli6F48eTyvPnhy5dlF7dJ5/w0Tnq0puALyec5y4mBib0qNyDHpV7cPr+aXz9fVlzcQ3nHp6j37Z+jPQbSR+3PgyqPoiSBUtqurpSLpChe3pVq1blxYsXAPz8889ER0fTvXt3fv31V+bMmUO/fv1kwNNSHzsgJDZWWR2lSxdl9OWXXyoBT08PWraEdevg4UOYNw88PD4+4CWQ90/znup21VnSdgmhPqH86vkrzlbOvHj7gj+O/0GpWaVouaolOwJ3EBefckyBJGVUhnp6V69eJTIykgIFCjB+/HgGDhwoM5XrgI+ZUH/pknL5cuVKJaglqFhR6dF17w5Fi2ZXzeX907zM2syakbVH4uPhw+6bu/H192XXzV2JD2crZwZVH0Qftz5Ym1lrurqSjslQ0KtSpQq9e/emTp06CCH4/fff01x55aeffsrSCuZ2WX2v7V3qDgh5+hTWrFGC3ZkzSdtZW0O3bsq9Oje3rOvNZYQu3j+Vsoa+nj6tSreiVelW3Hx+k7n+c/kr4C+CXgYx6t9R/HTwJ7pU7IK3uzfV7aprurqSjshQ0Fu6dCljx45l+/btqFQqdu3ahYFByreqVCoZ9DIoJxavzsiAkJgY2LlTCXTbtys560BZ47JVKyXQtWypLPosSZriUtCFP5r9wS+NfmHNRSXL+7mH51gasJSlAUupaV8Tb3dvOlboiImBiaarK2mxDAW9MmXKsHbtWgD09PTYt28fhQsXztaK5Xbp3Wtb3rdGlnxGWgNC9FBR3tiBmRPMWbVK6eElqFpVuXzZtSsUKpQl1ZCkLGNmaEbfqn3p49aHEyEn8PX3Zf3l9ZwMPcnJ0JP47PWhn1s/+lbpq+mqSlpK7dGb8fHx2VGPPCUnF6+e1dWNr9acUyZ7RxgTecUOEejEjtCkXqCtbdIizxUrZsnHSlK2UqlUeBT3wKO4B380/YNFZxcx78w8QsJDmHJsCr/+9yvVLatjeNuQ5qWbo6eSCWUkRYaC3tatW2nRogWGhoZs3bo13W3btGmTJRXLzXJy8rWxniGt89Xg3plYDh/QJz5OuSFnbJyUo65p0+QpeyRJl9ha2PJDvR8YXWc0265vw9ffl31B+zgVfopWa1tR2ro0g6sPxquKF1YmVpqurqRhGfpT165dOx4+fEjhwoVp165dmtupVKpUlyjLaz40OCW7J18LASdPKvfp1q5V5tclHOpPPlECXefOUKBA5uovSdrIQM+A9uXa075cey4+uMh3/3zH4fDDBD4LZNieYXy//3u+cP0C7xreVLKtpOnqShqSoaD37iVNeXkzbRkdnJJdk69DQpJy1F2/nlRerBj07Kk8ypT5+PpLkrYra1OW/sX6s7zxctZdXYevvy+Xn1xmwdkFLDi7gDoOdfB296ZDuQ4Y6ctRWnlJll3oDgkJYcCAAVm1O52kzkTwrJp8/fo1HDpUjBYt9HFwgO+/VwKeqamyFJifn5LxYOLE9AOeuvWXJF1gaWzJIPdBXBx0kYNeB+lYviMGegYcDT5K17+74jjDkbEHxhIannszxEjJZdmdnGfPnrF48eLEhajzGnUHp3zM5Gsh4MgRpUe3YYMBr15VS3ytXj1lmsHnn4M6SS+0KTO8JGU1lUpFfaf61Heqz/1X91lwZgHzz8znYcRDfj78s5LlvVx7vN29qe9YXy52nYvJIU1ZJCODU1LjbGNOwzKFMxRQgoJg/HgoWRLq14e//oJXr1TY2kYyZkwct27BoUNKGh91szxltv6SpGvsLO0Y12Acd4fdZe1na6nrUJc4EcfGKxtpuKwhrnNdmeM/h1dRrzRdVSkbyKCXRbJrcMqrV0pwq19fyUE3bpwS/CwtoU8f2Lcvlrlz/2XMmPh0c9R9iMxsIOU1RvpGdK7YmcO9D3Nh4AW+rPYl5obmXH5yGe+d3thPs+ernV9x9clVTVdVykIy6GWRrMysHhcH//6rzJ0rUkTJLn74sLL8V5MmSethLl4MdesK9LLgKMrM8FJe5mrryrxP5xHqE8rM5jMpbV2aV9GvmO0/m/JzytN4eWP+ufqPzPKeC2T4nl6HDh3Sff2lMi4+T3t3IngCdQanBAYq9+mWL1dGYiYoU0aZZvDFF1C8eFbXOsnH1l+SdF1+k/x8XfNrhtQYwr7b+/D192Vb4Db2B+1nf9B+iuUrxpfVvqR/1f7YWthqurpSJmQ46OXPn/+Dr/fs2fOjK6TLMjM45cULJT3PsmVw4kRSuZWVks6nVy+oUSNnFnmWmQ0kSaGn0qNJySY0KdmE4LBgJcv72UWEhIcw5sAYfj70Mx0rKFnePYp5yIEvOiTDQW/JkiXZWY9c5UOZAWJjYe9eJdBt2QJRUUq5vj40b6706lq3BhMNrZsrMxtIUhKH/A5MajyJsfXHsuHKBnz9fTkRcoLVF1ez+uJqqhSpgre7N91cu8ks7zpA3tPLQRcvwjffKJcoW7VSspBHRYGrK/z+u3JJc/t26NhRcwFPkqTUGRsY80WlLzje9zhnBpyhT5U+mBiYEPAwgP7b+mM/zR6fPT7cfH5T01WV0iGDXjZ7+hT+/BOqVYNKleCPP5RBKDY2MHQonD0L58/DiBHKoBVJkrRf1aJVWdx2MSHDQ/ityW+UKFCCl29fMv3EdErNKkXzlc3ZHrhdZnnXQnKZ4WwQHa3kqFu6FHbsUC5nAhgawqefKpcvW7SQOeokSddZm1nzTa1vkmd5v7GLPbf2sOfWHpysnBhUfRB93frKLO9aQga9LCKE0mtbtgxWr4Znz5Jeq1YtKUedjU3a+5AkSTfpqfRoWaolLUu15NbzW8w7PY/F5xZz5+UdRv87mp8OJGV5d7d313R18zR5efMjPXig3I+rVAmqV4dZs5SAV6QIjBwJly7B6dPw1Vcy4ElSXlCyYEl+a/oboT6h/NXmL6oWrUpUXBTLzi+jxqIa1FhYg+Xnl/M29q2mq5onyaCXCW/fKoNQWrZUMhgkBDdjYyVlz86dcO8e/PorVKig6dpKkqQJpoam9Hbrzen+pzne9zhfVPoCI30j/O/747XZi+LTi/Ptv99y5+UdTVc1T5FBL4OEgOPHYeBAKFpUCW67dkF8PHh4wPz5ygCVtWuV+3UyKauEEDBtGjg7g5kZtGsHYWGarpWUw1QqFZ8U+4QV7Vdwb/g9JjWahEN+B56+fsrUY1MpMbMEbda0Yc/NPcQLmbotu8mg9wH37sGkSVC2LNSqpQS3ly+VaQc//KCk8fnvPxgwQJlQLkmJRo6EuXOVG71HjsCZM8riqTltyhRldYNhw9LeZvJkcHcHS0sM7O2pMWlS8qSMUpYobF6Y7+p+x62vb7G582Y8S3giEGwL3EbzVc0pO7ss049P58WbF5quaq4lg14qIiOVZKyenuDoqAS3wEDln/UePWDfPiVH3YQJULq0pmsraaWTJ5Ve3rp1Sr6natWgf3/l2ndO8vdX/lOr9IFM4YcOgbc3nDhB7M6dqOLiMGjVSjkZpCxnoGdA27Jt8evhxzXva3xd42vyGefjxvMb+Oz1odj0YgzYNoDzj85ruqq5jkaD3rhx41CpVMkeZcuW1Uhd4uOV875PH2UQSs+eSnATQslwsGSJcvly+XJo1IgsWeRZysV+/x0aN4aqVZPKbG2ViZs5JSICuneHhQuhQIH0t929W1nzrkIFqFyZc19/jSo4WOmdStmqjE0ZZraYSahPKPNazcO1sCuvY16z8OxC3Be7892N71h7eS3RcdGarmquoPE/3RUqVODBgweJj6NHj+bo59++rVxxKlkSGjRQgltEhJLGZ/x45fWDB5W/B+rmqJPyqKgoZYJm+/bJy9++hQ+sYZvMpElgYZH+Izg47fd7eytL/3h6qt0Ew9f/z69YsKDa75Uyx8LIgi+rf8n5gec53OswnSp0wkDPgKuRV+m5pScO0x0Ys38MIeEhH96ZlCaND7cwMDCgSA4vRRIeDmvXqpg5szZXrhgmlltaKgNUvLygdu2cWeRZyn1U587BmzfKMjujRiW9EBMDDRsqP2/frrweHw+jR0O/fil3NHAgdOqU/ofZ2aVevnatMnHU31/9BsTHU3HxYuJr1UKvYkX13y99FJVKRV3HutR1rEvwi2BGrx/NoYhDPIh4wIQjE5h8dDLtyrbD292bBk4N5GLXatJ40Ltx4wZ2dnaYmJjg4eHB5MmTcXBwSHXbqKgoohJWZwbCw8MBiImJISYmJt3PiYuDAwdUrFihx+bNKt68MQBsUKkEnp6CL76Ip21bgdn/14uN1ZG0WQnt/lD7tVluaAMk1T/u6lX0zc2JfS/gGLRtS/wnnxD/5g0Gw4cT6+cH+fNjULMmsZ9+CtbvrdhhafnhywtCKMH0XffuYTB0KLE7dyqrmMfEoC8EIj6e+Ix8x0OGkO/uXaKOHcNAR49JbvmdsjG2oUuRLsxrNI+dQTuZd2Yeh4MP8/fVv/n76t+UsynHwKoD+cL1CyyNtfdSVE4cj4zuWyWEENlWiw/YtWsXERERlClThgcPHjB+/HhCQ0O5dOkSlqmc7OPGjWP8+PEpylevXo2ZWeqrm4eEWHDgQHEOHizOs2emieXFir2iYcN71K9/DxsbOUlUyjrO27fjsmULfgsXJpaZPn5Mky+/5MCff2IQGUmpTZs49d13AFRctIgXpUsTWq9esv2U2rCB0n//ne5n7Z81izeFCiUrK3LiBDWnTCH+nRvPevHxCJUKoVKxbcMGJRimwnXBAoqePMnRSZN4bSvzxWmju2/usuvpLg6+OMjbeOVvl4meCQ0LNqSlTUuKm2Rj0k0t9vr1a7p160ZYWBj58uVLczuNBr33vXz5EkdHR6ZNm0bfvn1TvJ5aT6948eI8ffo0WSNfvID16/VYsULFqVNJJ36BAoLOnePp0UNQuXI0//7rR5MmTTA0NERXxcTE4Oen2+3IDW2ApHY0f/ECo+HDiX30KPEauf6gQfDgAXGbN6P6+29Uhw8TP3MmAHp//AEqFfE+Psl3+Py58kiPk1PKSaGvXsHdu8mK9Pv3hzJliPvmG0jtkqUQ6A0bht6WLbzdtYu9QUE6fTxy2+9Uau0IexvGyosrmXd2HtefJU0vaeDYgIHVBtKmdBsM9DR+MQ/ImeMRHh6OjY3NB4Oednwj/2dlZUXp0qW5eTP11BzGxsYYGxunKDc0NESlMmTPHmWR561blUWfQfmHtkWLhBx1KoyNlf9wY2JE4nt1+aRIkBvakRvaAKDn6Ynq7VsM//hDyQS8apUysOXUKfQMDZUgpaeHfkJb9fVBpUp6nsDWVnmoq2DBlANQLCygUCH03NyU57Nnw6ZNyhBlgMGDlUVjt2zBoEABjM+exfDZMwxtbMDUFF2VW36nUmuHjaENw2oNY6jHUPYH7cfX35ct17dw8O5BDt49iL2lvZLlvVp/ilhoRwqX7DweGd2vxkdvvisiIoJbt25RtGhRtd73/ffKcmCffgobNyoBr1IlZZpUaChs2waff64sEyZJ2c7WVvnva+5cZQrAiRNw9KiyogEog09CQ5O2Dw1Ne0BKdnn6FG7dSno+d66yWkyDBhg6ONC8d28MHRyUeYaSVlOpVDQu0Zh/Ov9D0NAgvq/zPYXMChH6KpSfDv6Ew3QHuv7dlaPBR9GiC3sao9Gg980333Do0CHu3LnDf//9R/v27dHX16dr165q7cfXFx49gkKFlEUnzp1TctQNH565f5Ql6aN17qxMJ3j9WhmpWbJk0ms1aiiLtYaGKvNjdu2CZs2ytz4HD8KMGUnPx41TVlhIIETiIyY6mi2bNxMTHa3M1ZF0hkN+ByY2nsi94fdY2X4lHsU8iImPYe2ltdRdUhe3+W4sPLOQyOi8u+iARoNeSEgIXbt2pUyZMnTq1Alra2tOnDhBofduzH9I69awZYvyN2T6dKhSJXvqK0lZwsBAySbcsKHyyzpiRMqRm5L0EYwNjOleqTv/9f2PswPO0tetL6YGppx/dJ4B2wdgP82e4buHc+PZDU1XNcdp9J7e2rVrs2Q/K1dCOvctJUn7tGmjPCQpm7kVdWNRm0X81uQ3lgQsYY7/HG69uMWMkzOYcXIGTUs2xdvdm1alWqGvl/qo3txEq+7pSZIkSdmjgGkBfDx8CPwqkJ3ddtKqVCtUqNh7ay9t17al5J8lmXp0Kk9f5+BSeRogg54kSVIeoqfSo0WpFmzvtp2bX99kZK2RFDQtyN2wu3y771uKTSuG12YvToWe0nRVs4UMepIkSXlUiQIl+LXJr4QMD2FJ2yVUt6tOVFwUy88vp+aimrgvdGdpwFLexLzRdFWzjAx6kiRJeZypoSm9qvTCv78/J/udpEelHhjpG3H6/ml6b+lNsenFGO03mqAXQZqu6keTQU+SJElKVMO+BsvbLydkeAiTG0/GIb8Dz98859f/fqXknyVpvaY1u2/u1tks7zLoSZIkSSkUMi/Et3W+5fbXt9nSZQtNSzZFINgeuJ0Wq1pQZnYZph2fpnNZ3mXQkyRJktKkr6dPmzJt2PPFHq4Puc7QmkPJb5yfm89vMmLvCOyn2dNvaz/OPTin6apmiAx6kiRJUoaUti7NjOYzCPUJZf6n86lkW4k3sW9YfG4xVRdUpfZftVl9cbVWZ3mXQU+SJElSi7mROQOqDSDgywCO9D5Cl4pdMNAz4L97/9H9n+4Un16cH/f/yL2we5quagoy6ElSdhFCWfXc2RnMzKBdO2VRZ0nKJVQqFXUc6rDmszXcG36Pnxv8jJ2lHY8jHzPxyEScZzrTYV0H9gft15rFrmXQk6TsMnKkkr1g2TI4cgTOnFEWes4pvr5Kvj0TE6hZE07lzsnGknYoYlGEMfXHcGfoHTZ03EADpwbEiTg2XdtE8zXN+eraV8w5PYfwqHCN1lMGPUnKDidPKr28deugXj2oVg3694edO3Pm89etAx8fGDsWzp6FypWVTA6PH+fM50t5lqG+IZ+X/5wDXge4NOgSg6sPxsLIgpCoEIbtHYb9NHsG7xjM5ceXNVI/GfQkKTv8/js0bgxVqyaV2doqeexywrRpSpDt3RvKl4d585RLrH/9lTOfL0lAhcIV8G3ly52v7jDAfgBlrcsSER3B3NNzqTi3Ig2WNmDD5Q3ExMXkWJ1k0JOkrBYVpWRKb98+efnbt5A/f8b3M2mSkvE8vUdwcMr3RUcrl1I9PZPK9PSU58ePZ65NkvQR8hnno2WhlpwfcJ59PffRoVwH9FX6HLp7iE4bO+E004mfD/3Mg1cPsr0uGk0tJEm5kercOXjzRsmTN2pU0gsxMUoOPVAC4sGDSm9w48bUdzRwIHTqlP6HpZZx/elTiItLmUHZ1hauXctwOyQpq6lUKho5N6KRcyNCwkOYf3o+C84u4P6r+4w9OJZfDv/CZ+U+w9vdmzoOdVCpVFleBxn0JCmrBQaCuTkEBCQvb9UKatdWfh46FPr0UQa5pKVgQeUhSblQsXzF+KXRL4ypP4a/r/zNbP/Z/HfvP9ZdXse6y+twLeyKt7s33St1x8LIIss+V17elKQspnr1CmxswMUl6WFoCDduwGefKRs1aACWlunvKLOXN21sQF8fHj1KXv7oERQpkiVtlKSsYqRvRFfXrhzrc4xzX56jn1s/TA1Mufj4IgN3DMR+mj3Ddg8j8FlglnyeDHqSlMWEtbUyH+/deUkTJ0LLlsqgkowaOFDpLab3SO3yppGRMlp0376ksvh45bmHh/oNkqQcUqVIFRa2WUioTyjTmk7DpaAL4VHhzDw5kzKzy9B0RVO2XNtCXHxcpj9DXt6UpCwmGjZUBq1MmQJdusCqVbBtm/rz5D7m8qaPD3h5QfXqUKMGzJgBkZHKaE5J0nIFTAsw3GM4Qz8Zit8tP3z9fdkeuB2/23743fbDIb8DA6sNpF/VfhQyL6TWvmVPT5Kymq0tLF2qTEyvUAFOnICjR6F48ZyrQ+fOyrSJn36CKlWUXuHu3SkHt0iSFtNT6dHMpRlbu27l1te3GFVrFNam1gSHBfP9/u8pNr0YPTf15GTIyQyv+CKDniRlh86dlfttr1/D9u1QsmTO12HIELh7V5lCcfKksiqLJOko5wLOTG0ylRCfEJa2XYq7nTvRcdGsuLCCTxZ/QoOlDTK0Hxn0JEkTPD2hY0dlhZZixeT8OUnKIBMDE7yqeHGq/ylO9TuFV2UvjPWNCXgYkKH3y6AnSZrw77/w5InSEwwJkQNMJCkT3O3dWdpuKSE+IYxrMC5D75FBT5IkSdJpNmY2DPcYnqFtZdCTJEmS8gwZ9CRJkqQ8QwY9SZIkKc/IHZPTIyOVZZfUEROD/tu3ynsNDbOnXjkhN7QjN7QBZDu0SW5oA8h2qCMyMkObqYS25HDPhPDwcPLnz08YkE/TlZEkSZI0JhzID4SFhZEvX9oRQV7elCRJkvKM3HF58/59SCeypyYmJoY9e/bQrFkzDHX4skFuaEduaAPIdmiT3NAGkO1QS3h46guwvyd3BD1zc+WhjpgY4kxMlPfp8C9TrmhHbmgDyHZok9zQBpDtUEdcxjIvyMubkiRJUp4hg54kSZKUZ8igJ0mSJOUZWhP0pkyZgkqlYtiwYZquiiRJkpRLaUXQ8/f3Z/78+VSqVEnTVZEkSZJyMY0HvYiICLp3787ChQspUKCApqsjSZIk5WIan7Lg7e1Nq1at8PT0ZMKECeluGxUVRVRUVOLzsLAwAJ4/f05MTIxanxsTE8Pr16959uyZzs9/0fV25IY2gGyHNskNbQDZDnW8evUKgA8tMqbRoLd27VrOnj2Lv79/hrafPHky48ePT1Hu7Oyc1VWTJEmSdNCrV6/Inz9/mq9rbO3Ne/fuUb16dfz8/BLv5TVo0IAqVaowY8aMVN/zfk8vPj6e58+fY21tjUqlUuvzw8PDKV68OPfu3Ut3nTZtlxvakRvaALId2iQ3tAFkO9QhhODVq1fY2dmhp5f2nTuN9fTOnDnD48ePqVq1amJZXFwchw8fZvbs2URFRaH/XuYEY2NjjI2Nk5VZWVl9VD3y5cun079MCXJDO3JDG0C2Q5vkhjaAbEdGpdfDS6CxoNe4cWMuXryYrKx3796ULVuW0aNHpwh4kiRJkvSxNBb0LC0tqVixYrIyc3NzrK2tU5RLkiRJUlbQ+JQFTTE2Nmbs2LEpLpfqmtzQjtzQBpDt0Ca5oQ0g25EddDqJrCRJkiSpI8/29CRJkqS8RwY9SZIkKc+QQU+SJEnKM2TQkyRJkvIMnQl6kydPxt3dHUtLSwoXLky7du24fv16sm0aNGiASqVK9hg4cGCybYKDg2nVqhVmZmYULlyYkSNHEhsbm2ybgwcPUrVqVYyNjXFxcWHp0qUp6uPr64uTkxMmJibUrFmTU6dOZagdc+fOpVKlSomTND08PNi1a1fi62/fvsXb2xtra2ssLCz47LPPePTokVa1ISPt0IVj8b7U0lvpyvH4UDt04XiMGzcuRR3Lli2b+LouHIsPtUEXjkOC0NBQvvjiC6ytrTE1NcXV1ZXTp08nvi6E4KeffqJo0aKYmpri6enJjRs3ku3j+fPndO/enXz58mFlZUXfvn2JiIhIts2FCxeoW7cuJiYmFC9enF9//TVFXTZs2EDZsmUxMTHB1dWVnTt3qtWWZISOaNasmViyZIm4dOmSCAgIEC1bthQODg4iIiIicZv69euL/v37iwcPHiQ+wsLCEl+PjY0VFStWFJ6enuLcuXNi586dwsbGRnz33XeJ29y+fVuYmZkJHx8fceXKFTFr1iyhr68vdu/enbjN2rVrhZGRkfjrr7/E5cuXRf/+/YWVlZV49OjRB9uxdetWsWPHDhEYGCiuX78uvv/+e2FoaCguXbokhBBi4MCBonjx4mLfvn3i9OnT4pNPPhG1atXSqjZkpB26cCzederUKeHk5CQqVaokhg4dmliuK8fjQ+3QheMxduxYUaFChWR1fPLkSeLrunAsPtQGXTgOQgjx/Plz4ejoKHr16iVOnjwpbt++Lfbs2SNu3ryZuM2UKVNE/vz5xebNm8X58+dFmzZthLOzs3jz5k3iNs2bNxeVK1cWJ06cEEeOHBEuLi6ia9euia+HhYUJW1tb0b17d3Hp0iWxZs0aYWpqKubPn5+4zbFjx4S+vr749ddfxZUrV8SPP/4oDA0NxcWLFzPUlvfpTNB73+PHjwUgDh06lFhWv379ZCf6+3bu3Cn09PTEw4cPE8vmzp0r8uXLJ6KiooQQQowaNUpUqFAh2fs6d+4smjVrlvi8Ro0awtvbO/F5XFycsLOzE5MnT85UWwoUKCAWLVokXr58KQwNDcWGDRsSX7t69aoAxPHjx7W6De+2QwjdOhavXr0SpUqVEn5+fsnqrWvHI612CKEbx2Ps2LGicuXKqb6mK8civTYIoRvHQQghRo8eLerUqZPm6/Hx8aJIkSLit99+Syx7+fKlMDY2FmvWrBFCCHHlyhUBCH9//8Rtdu3aJVQqlQgNDRVCCDFnzhxRoECBxLYlfHaZMmUSn3fq1Em0atUq2efXrFlTfPnllxlqy/t05vLm+xLSChUsWDBZ+apVq7CxsaFixYp89913vH79OvG148eP4+rqiq2tbWJZs2bNCA8P5/Lly4nbeHp6Jttns2bNOH78OADR0dGcOXMm2TZ6enp4enombpNRcXFxrF27lsjISDw8PDhz5gwxMTHJ9l22bFkcHBwS961tbUitHQl05Vi8m97qXbp2PNJqRwJdOB43btzAzs6OEiVK0L17d4KDgwHdOhZptSGBLhyHrVu3Ur16dTp27EjhwoVxc3Nj4cKFia8HBQXx8OHDZJ+RP39+atasmex4WFlZUb169cRtPD090dPT4+TJk4nb1KtXDyMjo2RtuX79Oi9evMhQe9Wl8Xx6mREfH8+wYcOoXbt2siXLunXrhqOjI3Z2dly4cIHRo0dz/fp1/vnnHwAePnyY7JcJSHz+8OHDdLcJDw/nzZs3vHjxgri4uFS3uXbtWobqf/HiRTw8PHj79i0WFhZs2rSJ8uXLExAQgJGRUYpFtG1tbT9Yv5xuQ3rtAN05Fumlt3r48KHOHI8PpenSheNRs2ZNli5dSpkyZXjw4AHjx4+nbt26XLp0SWeORXptsLS01InjAHD79m3mzp2Lj48P33//Pf7+/nz99dcYGRnh5eWVWJfUPuPdehYuXDjZ6wYGBhQsWDDZNu+nhnu3vQUKFEizvQn7UJdOBj1vb28uXbrE0aNHk5UPGDAg8WdXV1eKFi1K48aNuXXrFiVLlszpaqapTJkyBAQEEBYWxsaNG/Hy8uLQoUOarpba0mpH+fLldeJY3Lt3j6FDh+Ln54eJiYmmq5NpGWmHLhyPFi1aJP5cqVIlatasiaOjI+vXr8fU1FSDNcu49NrQt29fnTgOoHQsqlevzqRJkwBwc3Pj0qVLzJs3Dy8vLw3X7uPo3OXNIUOGsH37dg4cOECxYsXS3bZmzZoA3Lx5E4AiRYqkGO2V8LxIkSLpbpMvXz5MTU2xsbFBX18/1W0S9vEhRkZGuLi4UK1aNSZPnkzlypWZOXMmRYoUITo6mpcvX6a5b21pQ3rtSI02Hot301sZGBhgYGDAoUOH+PPPPzEwMMDW1lYnjseH2hEXF5fiPdp4PN5nZWVF6dKluXnzps6dG6m1ITXaehyKFi2aeNUmQbly5RIv1SbsJ73PKFKkCI8fP072emxsLM+fP8+SY5aZ4wE6FPSEEAwZMoRNmzaxf//+DGVLDwgIAJQDCODh4cHFixeTHQg/Pz/y5cuXeIA9PDzYt29fsv34+fkl3qsyMjKiWrVqybaJj49n3759ye5nqSM+Pp6oqCiqVauGoaFhsn1fv36d4ODgxH1raxvebUdqtPFYJKS3CggISHxUr16d7t27J/6sC8fjQ+1ILU2XNh6P90VERHDr1i2KFi2qs+fGu21IjbYeh9q1a6eYEhYYGIijoyMAzs7OFClSJNlnhIeHc/LkyWTH4+XLl5w5cyZxm/379xMfH58Y7D08PDh8+DAxMTHJ2lKmTBkKFCiQofaqLVPDXzRg0KBBIn/+/OLgwYPJhvu+fv1aCCHEzZs3xc8//yxOnz4tgoKCxJYtW0SJEiVEvXr1EveRMBy4adOmIiAgQOzevVsUKlQo1eHAI0eOFFevXhW+vr6pDgc2NjYWS5cuFVeuXBEDBgwQVlZWyUZcpeXbb78Vhw4dEkFBQeLChQvi22+/FSqVSuzdu1cIoQzLdnBwEPv37xenT58WHh4ewsPDQ6va8KF26MqxSM37o+t05Xik1w5dOR4jRowQBw8eFEFBQeLYsWPC09NT2NjYiMePHwshdONYpNcGXTkOQihTXwwMDMTEiRPFjRs3xKpVq4SZmZlYuXJl4jZTpkwRVlZWYsuWLeLChQuibdu2qU5ZcHNzEydPnhRHjx4VpUqVSjZl4eXLl8LW1lb06NFDXLp0Saxdu1aYmZmlmLJgYGAgfv/9d3H16lUxduzYvDFlAUj1sWTJEiGEEMHBwaJevXqiYMGCwtjYWLi4uIiRI0cmmwMjhBB37twRLVq0EKampsLGxkaMGDFCxMTEJNvmwIEDokqVKsLIyEiUKFEi8TPeNWvWLOHg4CCMjIxEjRo1xIkTJzLUjj59+ghHR0dhZGQkChUqJBo3bpwY8IQQ4s2bN2Lw4MGiQIECwszMTLRv3148ePBAq9rwoXboyrFIzftBT1eOR3rt0JXj0blzZ1G0aFFhZGQk7O3tRefOnZPNC9OFY5FeG3TlOCTYtm2bqFixojA2NhZly5YVCxYsSPZ6fHy8GDNmjLC1tRXGxsaicePG4vr168m2efbsmejatauwsLAQ+fLlE7179xavXr1Kts358+dFnTp1hLGxsbC3txdTpkxJUZf169eL0qVLCyMjI1GhQgWxY8cOtdryLplaSJIkScozdOaeniRJkiR9LBn0JEmSpDxDBj1JkiQpz5BBT5IkScozZNCTJEmS8gwZ9CRJkqQ8QwY9SZIkKc+QQU+SJEnKM2TQk6Q8ysnJiRkzZmTb/uvVq8fq1auzbf8ZsXv3bqpUqUJ8fLxG6yFpDxn0JK338OFDhg4diouLCyYmJtja2lK7dm3mzp2bLAGnk5MTKpUKlUqFqakpTk5OdOrUif379yfb3507dxK3U6lUWFtb07RpU86dO5fTTdMof3//ZKluVCoVmzdvzpJ9b926lUePHtGlS5cP7r9Xr160a9cu8XlQUBDdunXDzs4OExMTihUrRtu2bZPlgnv3+Jmbm1OqVCl69eqVbHFjgObNm2NoaMiqVauypF2S7pNBT9Jqt2/fxs3Njb179zJp0iTOnTvH8ePHGTVqFNu3b+fff/9Ntv3PP//MgwcPuH79OsuXL8fKygpPT08mTpyYYt///vsvDx48YM+ePURERNCiRYsUqWuy27ury+e0QoUKYWZmli37/vPPP+nduzd6eur9iYmJiaFJkyaEhYXxzz//cP36ddatW4erq2uKY7NkyRIePHjA5cuX8fX1JSIigpo1a7J8+fJk2/Xq1Ys///zzY5sk5RaZXrVTknJAs2bNRLFixURERESqr8fHxyf+7OjoKKZPn55im59++kno6emJa9euCSGECAoKEoA4d+5c4jbHjh0TQLKV6t81duxYUblyZTFv3jxRrFgxYWpqKjp27ChevnyZbLuFCxeKsmXLCmNjY1GmTBnh6+ub+FrC565du1bUq1dPGBsbp7pQsBBCvHjxQgwYMEAULlxYGBsbiwoVKoht27YJIYR4+vSp6NKli7CzsxOmpqaiYsWKYvXq1cneX79+feHt7S28vb1Fvnz5hLW1tfjxxx/T/L4cHR2TLeTu6OgohFAyNLRp00YULlxYmJubi+rVqws/P79U65zg8ePHQqVSiUuXLiUrB8SmTZtSbO/l5SXatm0rhBDi3LlzAhB37txJ9zPS2lfPnj2FpaWleP78eWLZ3bt3BZBs8Wop75I9PUlrPXv2jL179+Lt7Y25uXmq26hUqg/uZ+jQoQgh2LJlS5rbJGTmjo6OTnObmzdvsn79erZt28bu3bs5d+4cgwcPTnx91apV/PTTT0ycOJGrV68yadIkxowZw7Jly5Lt59tvv2Xo0KFcvXqVZs2apfic+Ph4WrRowbFjx1i5ciVXrlxhypQpiXnx3r59S7Vq1dixYweXLl1iwIAB9OjRg1OnTiXbz7JlyzAwMODUqVPMnDmTadOmsWjRolTb5u/vDyT1nhKeR0RE0LJlS/bt28e5c+do3rw5rVu3TkwmmpqjR49iZmZGuXLl0twmLYUKFUJPT4+NGzemmvz2Q4YPH86rV6/w8/NLLHNwcMDW1pYjR46ovT8pF9J01JWktJw4cUIA4p9//klWbm1tLczNzYW5ubkYNWpUYnlaPT0hhLC1tRWDBg0SQqTs6b148UK0b99eWFhYpJlvbOzYsUJfX1+EhIQklu3atUvo6eklprcpWbJkih7XL7/8kpjzLeFzZ8yYkW679+zZI/T09FKkaUlPq1atxIgRIxKf169fX5QrVy5Zz2706NGiXLlyic/f/75Io/f0vgoVKohZs2al+fr06dNFiRIlUpSntf93e3pCCDF79mxhZmYmLC0tRcOGDcXPP/8sbt26laF9vXnzRgBi6tSpycrd3NzEuHHj0m+YlCfInp6kc06dOkVAQAAVKlRIM1P7+4QQKXqFtWrVwsLCggIFCnD+/HnWrVuHra1tmvtwcHDA3t4+8bmHhwfx8fFcv36dyMhIbt26Rd++fbGwsEh8TJgwgVu3biXbT/Xq1dOta0BAAMWKFaN06dKpvh4XF8cvv/yCq6srBQsWxMLCgj179qTofX3yySfJ2uzh4cGNGzfU6kFFRETwzTffUK5cOaysrLCwsODq1avp9vTevHmDiYlJhj/jfd7e3jx8+JBVq1bh4eHBhg0bqFChQrLeW1rE/zOlvX+sTU1Nkw16kvIuA01XQJLS4uLigkql4vr168nKS5QoASRdkvyQZ8+e8eTJE5ydnZOVr1u3jvLly2NtbY2VldVH1TUiIgKAhQsXUrNmzWSvJVyWTJDWpdoEH2rXb7/9xsyZM5kxYwaurq6Ym5szbNiwdC/NZtY333yDn58fv//+Oy4uLpiamvL555+n+1k2Nja8ePEiRbmlpSVhYWEpyl++fEn+/PlTbNu6dWtat27NhAkTaNasGRMmTKBJkybp1vfq1asAKY718+fPKVSoULrvlfIG2dOTtJa1tTVNmjRh9uzZREZGZno/M2fORE9PL9mweIDixYtTsmTJDAe84OBg7t+/n/j8xIkT6OnpUaZMGWxtbbGzs+P27du4uLgke7z/B/hDKlWqREhICIGBgam+fuzYMdq2bcsXX3xB5cqVKVGiRKrbnjx5MtnzEydOUKpUqRRBOIGhoWGKXuCxY8fo1asX7du3x9XVlSJFinDnzp106+/m5sbDhw9TBL4yZcqkmFIQFxfH+fPn0+zVgtJrK1u2bIZ+B2bMmEG+fPnw9PRMLHv79i23bt3Czc3tg++Xcj8Z9CStNmfOHGJjY6levTrr1q3j6tWrXL9+nZUrV3Lt2rUUf8BfvXrFw4cPuXfvHocPH2bAgAFMmDCBiRMn4uLi8lF1MTExwcvLi/Pnz3PkyBG+/vprOnXqRJEiRQAYP348kydP5s8//yQwMJCLFy+yZMkSpk2bptbn1K9fn3r16vHZZ5/h5+dHUFAQu3btYvfu3QCUKlUKPz8//vvvP65evcqXX37Jo0ePUuwnODgYHx8frl+/zpo1a5g1axZDhw5N83OdnJzYt29fsoBVqlQp/vnnHwICAjh//jzdunX74ERvNzc3bGxsOHbsWLJyHx8fFi1axJw5c7hx4wYBAQEMGDCAFy9e0K9fP0C5tNu2bVs2btzIlStXuHnzJosXL+avv/6ibdu2yfb38uVLHj58yN27d/Hz8+Pzzz9n9erVzJ07N9k/MidOnMDY2BgPD4906y3lEZq+qShJH3L//n0xZMgQ4ezsLAwNDYWFhYWoUaOG+O2330RkZGTidu8OuzcyMhIODg6iU6dOYv/+/cn2l9qUhQ9JmLIwZ84cYWdnJ0xMTMTnn3+ebGi8EEKsWrVKVKlSRRgZGYkCBQqIevXqJQ7EUedznz17Jnr37i2sra2FiYmJqFixoti+fXvia23bthUWFhaicOHC4scffxQ9e/ZMNhikfv36YvDgwWLgwIEiX758okCBAuL7779Pd4rH1q1bhYuLizAwMEicshAUFCQaNmwoTE1NRfHixcXs2bNF/fr1xdChQ9Ot/6hRo0SXLl1SlK9atUpUq1ZNWFpaCltbW9GyZUtx/vz5xNefPHkivv76a1GxYkVhYWEhLC0thaurq/j9999FXFxc4na8M73CxMRElCxZUnh5eYkzZ86k+MwBAwaIL7/8Mt36SnmHSoj/3/mVJClN48aNY/PmzQQEBGi6KhnSoEEDqlSpkq3LjKXn4cOHVKhQgbNnz+Lo6KiROgA8ffqUMmXKcPr0abUvM0u5k7y8KUlSlitSpAiLFy9Od5RnTrhz5w5z5syRAU9KJEdvSpKULd4fOKQJ1atX/+AUESlvkZc3JUmSpDxDXt6UJEmS8gwZ9CRJkqQ8QwY9SZIkKc+QQU+SJEnKM2TQkyRJkvIMGfQkSZKkPEMGPUmSJCnPkEFPkiRJyjP+Bx++i8fgK4aaAAAAAElFTkSuQmCC\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": 297
        },
        "id": "sZt-omkoiR7N",
        "outputId": "524c5ce5-1604-4a7a-b79f-ee2a2c426819"
      },
      "execution_count": 32,
      "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"
            ],
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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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col, color=\"orange\")\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",
        "\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}$\", color=\"b\")\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "DcPtgXsZiaY6",
        "outputId": "bf201afe-ea07-49ec-efe1-41d803ec5f4f"
      },
      "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": "N6m7jbzDisa9",
        "outputId": "0cad5f20-4f75-4a18-d05e-331ecd50c873"
      },
      "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": "OMeeW15XivTH",
        "outputId": "294069a9-6ee8-40ab-a4a2-4be91a8b75cc"
      },
      "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": 316
        },
        "id": "nFRysZLmizC5",
        "outputId": "5deb4d40-1629-4ab7-8ca3-f5262b7eb639"
      },
      "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 = final_df[(final_df[gdppc_col] < min_gdp) |\n",
        "                                  (final_df[gdppc_col] > max_gdp)]\n",
        "missing_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "WHAX-jF3i3Q6",
        "outputId": "64155538-ba1c-4b99-bd4f-38e4fca4c40e"
      },
      "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-3425c34a-28ea-467a-bf91-cd2064e83ffc\" 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-3425c34a-28ea-467a-bf91-cd2064e83ffc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3425c34a-28ea-467a-bf91-cd2064e83ffc 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-3425c34a-28ea-467a-bf91-cd2064e83ffc');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --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",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    20% {\n",
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              "      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-e5cb54d5-c52c-40cb-8309-f868b0ea8cd2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_60717a2e-a058-4e8e-935e-a7f4ad02f653\">\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_60717a2e-a058-4e8e-935e-a7f4ad02f653 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing_data');\n",
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              "  </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": "3eBnYMTVjFC7"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.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_[final_df[gdppc_col]]\n",
        "yfull = np.c_[final_df[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",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "O0fadAkvjHs3",
        "outputId": "1044f0ec-7049-439b-89ef-25ae5ea5a394"
      },
      "execution_count": 43,
      "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": [
        "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=\"m\", 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",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "v_sVqX-PjN0o",
        "outputId": "9ae5abf1-bffa-49cb-8779-62b5dd9d230d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}