01_01_BetterLifeIndex_GPD.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMNasXiwDFk56d17vwk7LT1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lafalletes/AMLSLKT/blob/master/01_01_BetterLifeIndex_GPD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.linear_model"
      ],
      "metadata": {
        "id": "34UbeGj9RQyk"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "uS2mTEdxB2l_"
      },
      "outputs": [],
      "source": [
        "# Load the data\n",
        "oecd_bli = pd.read_csv(\"oecd_bli_2015.csv\", thousands=',')\n",
        "\n",
        "gdp_per_capita = pd.read_csv(\"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
        "encoding='latin1', na_values=\"n/a\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare prepare_country_stats function\n",
        "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
        "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
        "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
        "    gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
        "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
        "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
        "                                  left_index=True, right_index=True)\n",
        "    full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
        "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
        "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
        "    return full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]"
      ],
      "metadata": {
        "id": "rYiZdpnhSOPb"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)                                      "
      ],
      "metadata": {
        "id": "J_BUIkezU0IT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data\n",
        "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
        "X = np.c_[country_stats[\"GDP per capita\"]]\n",
        "y = np.c_[country_stats[\"Life satisfaction\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "YGWx7ilQUFye",
        "outputId": "96a6f9be-39c6-48e3-9a95-99ddcd0d5df8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-523124ca47df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Prepare the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcountry_stats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_country_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moecd_bli\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgdp_per_capita\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcountry_stats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"GDP per capita\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcountry_stats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Life satisfaction\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-3-73f9c330c95e>\u001b[0m in \u001b[0;36mprepare_country_stats\u001b[0;34m(oecd_bli, gdp_per_capita)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0moecd_bli\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moecd_bli\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Country\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Indicator\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Value\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mgdp_per_capita\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"2015\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"GDP per capita\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mgdp_per_capita\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Country\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n\u001b[1;32m      8\u001b[0m                                   left_index=True, right_index=True)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   5449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5450\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5451\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5453\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of ['Country'] are in the columns\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the data\n",
        "country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "oj-0R-yRUKVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a linear model\n",
        "model = sklearn.linear_model.LinearRegression()"
      ],
      "metadata": {
        "id": "_VNGiRK6UN_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(X, y)"
      ],
      "metadata": {
        "id": "zKgGVsJOUPUa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a prediction for Cyprus\n",
        "X_new = [[22587]] # Cyprus's GDP per capita\n",
        "Y_new = model.predict(X_new)\n",
        "print(Y_new) # outputs [[ 5.96242338]]"
      ],
      "metadata": {
        "id": "rC-eX8dMUUpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add new values\n",
        "df_cyprus = pd.DataFrame([[X_new, Y_new]], columns=list('GDP per capita','Life satisfaction'), index=['Cyprus'])"
      ],
      "metadata": {
        "id": "UlvSaHl_UnmW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}