Untitled1shruti.ipynb

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              "   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>Country</th>\n",
              "      <th>GDP per capita (USD)</th>\n",
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              "      <td>Russia</td>\n",
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              "      <td>5.8</td>\n",
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              "      <th>1</th>\n",
              "      <td>Greece</td>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
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              "      <th>3</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>29932.493910</td>\n",
              "      <td>5.9</td>\n",
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              "      <th>4</th>\n",
              "      <td>Hungary</td>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
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              "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": 8
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(kind='scatter', grid=True, color='red',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": "njQcn_eedqDg",
        "outputId": "ecf67c55-029c-44a4-a0c2-c0a9352ee6cc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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='blue', 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='pink', 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": "WmRbbxkidyTl",
        "outputId": "fdeeaa70-bf35-420c-a2fa-23dcdad6895d"
      },
      "execution_count": 12,
      "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": "rZQS21CYeLAV",
        "outputId": "9b73d596-1d06-4363-f57d-30ab769adde6"
      },
      "execution_count": 13,
      "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": "46ieyd1keRnY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SmsEwfyLeYe3",
        "outputId": "98962935-76fa-44d1-ab79-55f3b88efd1a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "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": "tcTb66UsejhV",
        "outputId": "3f3db722-ac05-48cf-9efe-6bf3e414409d"
      },
      "execution_count": 15,
      "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": "24bAWCwEnkdi"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gdp_per_capita.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "MUPneJXlnwQd",
        "outputId": "485c8a69-23bf-4297-a448-c18a121a99bd"
      },
      "execution_count": 18,
      "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",
              "20  Africa Eastern and Southern  NaN  1991   \n",
              "21  Africa Eastern and Southern  NaN  1992   \n",
              "22  Africa Eastern and Southern  NaN  1993   \n",
              "23  Africa Eastern and Southern  NaN  1994   \n",
              "24  Africa Eastern and Southern  NaN  1995   \n",
              "25  Africa Eastern and Southern  NaN  1996   \n",
              "26  Africa Eastern and Southern  NaN  1997   \n",
              "27  Africa Eastern and Southern  NaN  1998   \n",
              "28  Africa Eastern and Southern  NaN  1999   \n",
              "29  Africa Eastern and Southern  NaN  2000   \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    \n",
              "20                                        2783.028528    \n",
              "21                                        2642.479937    \n",
              "22                                        2551.342966    \n",
              "23                                        2528.416435    \n",
              "24                                        2570.462183    \n",
              "25                                        2642.467351    \n",
              "26                                        2659.714031    \n",
              "27                                        2637.930638    \n",
              "28                                        2640.336494    \n",
              "29                                        2654.767900    "
            ],
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              "      <th>GDP per capita, PPP (constant 2017 international $)</th>\n",
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              "      <td>2005</td>\n",
              "      <td>1286.793659</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2006</td>\n",
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              "      <th>5</th>\n",
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              "      <td>AFG</td>\n",
              "      <td>2007</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2008</td>\n",
              "      <td>1484.114461</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2009</td>\n",
              "      <td>1758.904477</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <th>9</th>\n",
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              "      <td>2011</td>\n",
              "      <td>1904.559926</td>\n",
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              "      <th>10</th>\n",
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              "      <td>AFG</td>\n",
              "      <td>2012</td>\n",
              "      <td>2075.491614</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2013</td>\n",
              "      <td>2116.465258</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>2016</td>\n",
              "      <td>2057.067978</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
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              "      <td>2017</td>\n",
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              "      <th>16</th>\n",
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              "      <td>2033.804389</td>\n",
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              "      <th>17</th>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>2019</td>\n",
              "      <td>2065.036235</td>\n",
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              "      <th>18</th>\n",
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              "      <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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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
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              "      <td>2783.028528</td>\n",
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              "      <th>21</th>\n",
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              "      <th>22</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
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              "      <td>2551.342966</td>\n",
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              "      <th>23</th>\n",
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              "      <td>2654.767900</td>\n",
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              "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": 18
        }
      ]
    },
    {
      "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()"
      ],
      "metadata": {
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        "id": "aidb6Z3Gn7Mo",
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      },
      "execution_count": 19,
      "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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              "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
        "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
        "\n",
        "oecd_bli.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "Ue0b-vrqoAf0",
        "outputId": "c5814010-d806-4229-e1ef-f55d0aca8cb9"
      },
      "execution_count": 20,
      "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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              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>73.0</td>\n",
              "      <td>63.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>32759.0</td>\n",
              "      <td>427064.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49126.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>502.0</td>\n",
              "      <td>14.35</td>\n",
              "      <td>91.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.66</td>\n",
              "      <td>72.0</td>\n",
              "      <td>80.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>33541.0</td>\n",
              "      <td>308325.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50349.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>492.0</td>\n",
              "      <td>14.55</td>\n",
              "      <td>80.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>15.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>77.0</td>\n",
              "      <td>4.75</td>\n",
              "      <td>63.0</td>\n",
              "      <td>70.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30364.0</td>\n",
              "      <td>386006.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49675.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>15.70</td>\n",
              "      <td>89.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>3.69</td>\n",
              "      <td>73.0</td>\n",
              "      <td>82.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>30854.0</td>\n",
              "      <td>423849.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47622.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>523.0</td>\n",
              "      <td>14.56</td>\n",
              "      <td>68.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-284537e2-0ce4-4ee4-aa78-c74eeb64168f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-284537e2-0ce4-4ee4-aa78-c74eeb64168f 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-284537e2-0ce4-4ee4-aa78-c74eeb64168f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c9d0b629-ca4d-4242-af8a-f10444cfe964\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c9d0b629-ca4d-4242-af8a-f10444cfe964')\"\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",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\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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              "    40% {\n",
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              "      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-c9d0b629-ca4d-4242-af8a-f10444cfe964 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": 20
        }
      ]
    },
    {
      "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": 237
        },
        "id": "jYBaKtVcoF2f",
        "outputId": "e33d99d8-60cb-4a02-c358-19d559735d33"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
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              "      <th>Country</th>\n",
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              "      <th>South Africa</th>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } 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-802a6e92-ed4c-4585-8a44-686b48bce55d 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": 21
        }
      ]
    },
    {
      "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": 237
        },
        "id": "zkyJ1FDaoMNS",
        "outputId": "3ae1e222-df37-4d59-b75f-2e7e9b826ab2"
      },
      "execution_count": 22,
      "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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              "      <th>Russia</th>\n",
              "      <td>26456.387938</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
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              "      <th>Greece</th>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
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              "      <th>Latvia</th>\n",
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              "      <td>5.9</td>\n",
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              "      <td>5.6</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      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": "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(country_stats.index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3V_GIJxoQjO",
        "outputId": "77103d80-00bf-4359-a45d-7849d3b7e9f0"
      },
      "execution_count": 23,
      "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= 'orange')\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='green', width=0.7,\n",
        "                                 shrink=0.20, 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": "5szCc84hoVZc",
        "outputId": "6a2535b0-fed0-483a-8b9c-dd6494d109c0"
      },
      "execution_count": 26,
      "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}$\", color=\"r\")\n",
        "\n",
        "w1, w2 = 10, -9\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
        "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
        "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "w1, w2 = 3, 8\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
        "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
        "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"r\")\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": "fUaf4-PXoroM",
        "outputId": "6ddf395b-3bf3-4339-957b-1e2aff0aaa00"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
        "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "2fsdO4D2o_GE",
        "outputId": "8126147f-7d57-4c7e-dc5f-56800a0aa3d4"
      },
      "execution_count": 29,
      "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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              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
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              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>42025.617373</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
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              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
              "      <td>7.3</td>\n",
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              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>48697.837028</td>\n",
              "      <td>7.3</td>\n",
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              "      <th>Denmark</th>\n",
              "      <td>55938.212809</td>\n",
              "      <td>7.6</td>\n",
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              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>60235.728492</td>\n",
              "      <td>6.9</td>\n",
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              "        const element = document.querySelector('#df-e980497d-9f65-448a-bd94-30852102ac9b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-58ba9701-ac1b-470f-8479-1c331ecb8a66 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",
              "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": 29
        }
      ]
    },
    {
      "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=\"black\")\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}$\", color=\"y\")\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"y\")\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": "JEAxdZtRpFyS",
        "outputId": "c118ac56-60b9-4c20-e4df-3a93beb9472f"
      },
      "execution_count": 32,
      "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": "POEp8EUUpUte",
        "outputId": "f3c02413-55bd-401a-cf1d-430f224daf21"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "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": "CR4GK-P5pkcy",
        "outputId": "013c0c35-a882-4ff6-87e7-3ead2df89333"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.301656332738056"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "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}$\", color=\"g\")\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"g\")\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": "Mz65XsEYpni3",
        "outputId": "d57d0c53-ef01-4dd7-bf53-2fe6d64a11ba"
      },
      "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": [
        "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": 363
        },
        "id": "a2IE-VoApx__",
        "outputId": "d3d7f88e-4b96-4f62-c576-93756aa689ca"
      },
      "execution_count": 36,
      "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-2e70df34-9d61-463c-957e-a9e30fb60533\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>11466.189672</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>13441.492952</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>14063.982505</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>17887.750736</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>63585.903514</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>68393.306004</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>89688.956958</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>110261.157353</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      spin 1s steps(1) infinite;\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        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",
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              "        document.querySelector('#id_977835b5-e966-4fa4-85d8-f51cdd8564f6 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing_data');\n",
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              "\n",
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              "  </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": 36
        }
      ]
    },
    {
      "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": "llnBZ2qJp2X_"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.plot(kind='scatter', figsize=(10, 4),\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='red', width=0.5,\n",
        "                                 shrink=0.08, headwidth=7))\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": 393
        },
        "id": "I3xgNsrjp8vM",
        "outputId": "521500e9-6987-4993-fbc7-2d369e431e94"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 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=\"b\", 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": "jNKrtIrQqNy_",
        "outputId": "4ab5e0ce-3164-48ff-9c74-3cf9f9c4984e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fq3RNTraqaUn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}