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yosukehara/01_the_machine_learning_landscape.ipynb

Created December 3, 2018 13:19
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01_the_machine_learning_landscape.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chapter 1 – The Machine Learning landscape**\n",
"\n",
"_This is the code used to generate some of the figures in chapter 1._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"# To support both python 2 and python 3\n",
"from __future__ import division, print_function, unicode_literals\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"np.random.seed(42)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n",
"plt.rcParams['xtick.labelsize'] = 12\n",
"plt.rcParams['ytick.labelsize'] = 12\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"fundamentals\"\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format='png', dpi=300)\n",
"\n",
"# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n",
"warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code example 1-1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function just merges the OECD's life satisfaction data and the IMF's GDP per capita data. It's a bit too long and boring and it's not specific to Machine Learning, which is why I left it out of the book."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"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]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code in the book expects the data files to be located in the current directory. I just tweaked it here to fetch the files in datasets/lifesat."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"datapath = os.path.join(\"datasets\", \"lifesat\", \"\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[5.96242338]]\n"
]
}
],
"source": [
"# Code example\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn.linear_model\n",
"\n",
"# Load the data\n",
"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
"gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
" encoding='latin1', na_values=\"n/a\")\n",
"\n",
"# 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\"]]\n",
"\n",
"# Visualize the data\n",
"country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
"plt.show()\n",
"\n",
"# Select a linear model\n",
"model = sklearn.linear_model.LinearRegression()\n",
"\n",
"# Train the model\n",
"model.fit(X, y)\n",
"\n",
"# Make a prediction for Cyprus\n",
"X_new = [[22587]] # Cyprus' GDP per capita\n",
"print(model.predict(X_new)) # outputs [[ 5.96242338]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Note: you can ignore the rest of this notebook, it just generates many of the figures in chapter 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load and prepare Life satisfaction data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want, you can get fresh data from the OECD's website.\n",
"Download the CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
"and save it to `datasets/lifesat/`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>Indicator</th>\n",
" <th>Air pollution</th>\n",
" <th>Assault rate</th>\n",
" <th>Consultation on rule-making</th>\n",
" <th>Dwellings without basic facilities</th>\n",
" <th>Educational attainment</th>\n",
" <th>Employees working very long hours</th>\n",
" <th>Employment rate</th>\n",
" <th>Homicide rate</th>\n",
" <th>Household net adjusted disposable income</th>\n",
" <th>Household net financial wealth</th>\n",
" <th>...</th>\n",
" <th>Long-term unemployment rate</th>\n",
" <th>Personal earnings</th>\n",
" <th>Quality of support network</th>\n",
" <th>Rooms per person</th>\n",
" <th>Self-reported health</th>\n",
" <th>Student skills</th>\n",
" <th>Time devoted to leisure and personal care</th>\n",
" <th>Voter turnout</th>\n",
" <th>Water quality</th>\n",
" <th>Years in education</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>13.0</td>\n",
" <td>2.1</td>\n",
" <td>10.5</td>\n",
" <td>1.1</td>\n",
" <td>76.0</td>\n",
" <td>14.02</td>\n",
" <td>72.0</td>\n",
" <td>0.8</td>\n",
" <td>31588.0</td>\n",
" <td>47657.0</td>\n",
" <td>...</td>\n",
" <td>1.08</td>\n",
" <td>50449.0</td>\n",
" <td>92.0</td>\n",
" <td>2.3</td>\n",
" <td>85.0</td>\n",
" <td>512.0</td>\n",
" <td>14.41</td>\n",
" <td>93.0</td>\n",
" <td>91.0</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>27.0</td>\n",
" <td>3.4</td>\n",
" <td>7.1</td>\n",
" <td>1.0</td>\n",
" <td>83.0</td>\n",
" <td>7.61</td>\n",
" <td>72.0</td>\n",
" <td>0.4</td>\n",
" <td>31173.0</td>\n",
" <td>49887.0</td>\n",
" <td>...</td>\n",
" <td>1.19</td>\n",
" <td>45199.0</td>\n",
" <td>89.0</td>\n",
" <td>1.6</td>\n",
" <td>69.0</td>\n",
" <td>500.0</td>\n",
" <td>14.46</td>\n",
" <td>75.0</td>\n",
" <td>94.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
"Indicator Air pollution Assault rate Consultation on rule-making \\\n",
"Country \n",
"Australia 13.0 2.1 10.5 \n",
"Austria 27.0 3.4 7.1 \n",
"\n",
"Indicator Dwellings without basic facilities Educational attainment \\\n",
"Country \n",
"Australia 1.1 76.0 \n",
"Austria 1.0 83.0 \n",
"\n",
"Indicator Employees working very long hours Employment rate Homicide rate \\\n",
"Country \n",
"Australia 14.02 72.0 0.8 \n",
"Austria 7.61 72.0 0.4 \n",
"\n",
"Indicator Household net adjusted disposable income \\\n",
"Country \n",
"Australia 31588.0 \n",
"Austria 31173.0 \n",
"\n",
"Indicator Household net financial wealth ... \\\n",
"Country ... \n",
"Australia 47657.0 ... \n",
"Austria 49887.0 ... \n",
"\n",
"Indicator Long-term unemployment rate Personal earnings \\\n",
"Country \n",
"Australia 1.08 50449.0 \n",
"Austria 1.19 45199.0 \n",
"\n",
"Indicator Quality of support network Rooms per person Self-reported health \\\n",
"Country \n",
"Australia 92.0 2.3 85.0 \n",
"Austria 89.0 1.6 69.0 \n",
"\n",
"Indicator Student skills Time devoted to leisure and personal care \\\n",
"Country \n",
"Australia 512.0 14.41 \n",
"Austria 500.0 14.46 \n",
"\n",
"Indicator Voter turnout Water quality Years in education \n",
"Country \n",
"Australia 93.0 91.0 19.4 \n",
"Austria 75.0 94.0 17.0 \n",
"\n",
"[2 rows x 24 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
"oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
"oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
"oecd_bli.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Country\n",
"Australia 7.3\n",
"Austria 6.9\n",
"Belgium 6.9\n",
"Brazil 7.0\n",
"Canada 7.3\n",
"Name: Life satisfaction, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oecd_bli[\"Life satisfaction\"].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load and prepare GDP per capita data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like above, you can update the GDP per capita data if you want. Just download data from http://goo.gl/j1MSKe (=> imf.org) and save it to `datasets/lifesat/`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>Subject Descriptor</th>\n",
" <th>Units</th>\n",
" <th>Scale</th>\n",
" <th>Country/Series-specific Notes</th>\n",
" <th>GDP per capita</th>\n",
" <th>Estimates Start After</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>599.994</td>\n",
" <td>2013.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>3995.383</td>\n",
" <td>2010.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Subject Descriptor Units \\\n",
"Country \n",
"Afghanistan Gross domestic product per capita, current prices U.S. dollars \n",
"Albania Gross domestic product per capita, current prices U.S. dollars \n",
"\n",
" Scale Country/Series-specific Notes \\\n",
"Country \n",
"Afghanistan Units See notes for: Gross domestic product, curren... \n",
"Albania Units See notes for: Gross domestic product, curren... \n",
"\n",
" GDP per capita Estimates Start After \n",
"Country \n",
"Afghanistan 599.994 2013.0 \n",
"Albania 3995.383 2010.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',', delimiter='\\t',\n",
" encoding='latin1', na_values=\"n/a\")\n",
"gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
"gdp_per_capita.set_index(\"Country\", inplace=True)\n",
"gdp_per_capita.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>Air pollution</th>\n",
" <th>Assault rate</th>\n",
" <th>Consultation on rule-making</th>\n",
" <th>Dwellings without basic facilities</th>\n",
" <th>Educational attainment</th>\n",
" <th>Employees working very long hours</th>\n",
" <th>Employment rate</th>\n",
" <th>Homicide rate</th>\n",
" <th>Household net adjusted disposable income</th>\n",
" <th>Household net financial wealth</th>\n",
" <th>...</th>\n",
" <th>Time devoted to leisure and personal care</th>\n",
" <th>Voter turnout</th>\n",
" <th>Water quality</th>\n",
" <th>Years in education</th>\n",
" <th>Subject Descriptor</th>\n",
" <th>Units</th>\n",
" <th>Scale</th>\n",
" <th>Country/Series-specific Notes</th>\n",
" <th>GDP per capita</th>\n",
" <th>Estimates Start After</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Brazil</th>\n",
" <td>18.0</td>\n",
" <td>7.9</td>\n",
" <td>4.0</td>\n",
" <td>6.7</td>\n",
" <td>45.0</td>\n",
" <td>10.41</td>\n",
" <td>67.0</td>\n",
" <td>25.5</td>\n",
" <td>11664.0</td>\n",
" <td>6844.0</td>\n",
" <td>...</td>\n",
" <td>14.97</td>\n",
" <td>79.0</td>\n",
" <td>72.0</td>\n",
" <td>16.3</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>8669.998</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mexico</th>\n",
" <td>30.0</td>\n",
" <td>12.8</td>\n",
" <td>9.0</td>\n",
" <td>4.2</td>\n",
" <td>37.0</td>\n",
" <td>28.83</td>\n",
" <td>61.0</td>\n",
" <td>23.4</td>\n",
" <td>13085.0</td>\n",
" <td>9056.0</td>\n",
" <td>...</td>\n",
" <td>13.89</td>\n",
" <td>63.0</td>\n",
" <td>67.0</td>\n",
" <td>14.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>9009.280</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Russia</th>\n",
" <td>15.0</td>\n",
" <td>3.8</td>\n",
" <td>2.5</td>\n",
" <td>15.1</td>\n",
" <td>94.0</td>\n",
" <td>0.16</td>\n",
" <td>69.0</td>\n",
" <td>12.8</td>\n",
" <td>19292.0</td>\n",
" <td>3412.0</td>\n",
" <td>...</td>\n",
" <td>14.97</td>\n",
" <td>65.0</td>\n",
" <td>56.0</td>\n",
" <td>16.0</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>9054.914</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>35.0</td>\n",
" <td>5.0</td>\n",
" <td>5.5</td>\n",
" <td>12.7</td>\n",
" <td>34.0</td>\n",
" <td>40.86</td>\n",
" <td>50.0</td>\n",
" <td>1.2</td>\n",
" <td>14095.0</td>\n",
" <td>3251.0</td>\n",
" <td>...</td>\n",
" <td>13.42</td>\n",
" <td>88.0</td>\n",
" <td>62.0</td>\n",
" <td>16.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>9437.372</td>\n",
" <td>2013.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hungary</th>\n",
" <td>15.0</td>\n",
" <td>3.6</td>\n",
" <td>7.9</td>\n",
" <td>4.8</td>\n",
" <td>82.0</td>\n",
" <td>3.19</td>\n",
" <td>58.0</td>\n",
" <td>1.3</td>\n",
" <td>15442.0</td>\n",
" <td>13277.0</td>\n",
" <td>...</td>\n",
" <td>15.04</td>\n",
" <td>62.0</td>\n",
" <td>77.0</td>\n",
" <td>17.6</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>12239.894</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>33.0</td>\n",
" <td>1.4</td>\n",
" <td>10.8</td>\n",
" <td>3.2</td>\n",
" <td>90.0</td>\n",
" <td>7.41</td>\n",
" <td>60.0</td>\n",
" <td>0.9</td>\n",
" <td>17852.0</td>\n",
" <td>10919.0</td>\n",
" <td>...</td>\n",
" <td>14.20</td>\n",
" <td>55.0</td>\n",
" <td>79.0</td>\n",
" <td>18.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>12495.334</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chile</th>\n",
" <td>46.0</td>\n",
" <td>6.9</td>\n",
" <td>2.0</td>\n",
" <td>9.4</td>\n",
" <td>57.0</td>\n",
" <td>15.42</td>\n",
" <td>62.0</td>\n",
" <td>4.4</td>\n",
" <td>14533.0</td>\n",
" <td>17733.0</td>\n",
" <td>...</td>\n",
" <td>14.41</td>\n",
" <td>49.0</td>\n",
" <td>73.0</td>\n",
" <td>16.5</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>13340.905</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovak Republic</th>\n",
" <td>13.0</td>\n",
" <td>3.0</td>\n",
" <td>6.6</td>\n",
" <td>0.6</td>\n",
" <td>92.0</td>\n",
" <td>7.02</td>\n",
" <td>60.0</td>\n",
" <td>1.2</td>\n",
" <td>17503.0</td>\n",
" <td>8663.0</td>\n",
" <td>...</td>\n",
" <td>14.99</td>\n",
" <td>59.0</td>\n",
" <td>81.0</td>\n",
" <td>16.3</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>15991.736</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czech Republic</th>\n",
" <td>16.0</td>\n",
" <td>2.8</td>\n",
" <td>6.8</td>\n",
" <td>0.9</td>\n",
" <td>92.0</td>\n",
" <td>6.98</td>\n",
" <td>68.0</td>\n",
" <td>0.8</td>\n",
" <td>18404.0</td>\n",
" <td>17299.0</td>\n",
" <td>...</td>\n",
" <td>14.98</td>\n",
" <td>59.0</td>\n",
" <td>85.0</td>\n",
" <td>18.1</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>17256.918</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Estonia</th>\n",
" <td>9.0</td>\n",
" <td>5.5</td>\n",
" <td>3.3</td>\n",
" <td>8.1</td>\n",
" <td>90.0</td>\n",
" <td>3.30</td>\n",
" <td>68.0</td>\n",
" <td>4.8</td>\n",
" <td>15167.0</td>\n",
" <td>7680.0</td>\n",
" <td>...</td>\n",
" <td>14.90</td>\n",
" <td>64.0</td>\n",
" <td>79.0</td>\n",
" <td>17.5</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>17288.083</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greece</th>\n",
" <td>27.0</td>\n",
" <td>3.7</td>\n",
" <td>6.5</td>\n",
" <td>0.7</td>\n",
" <td>68.0</td>\n",
" <td>6.16</td>\n",
" <td>49.0</td>\n",
" <td>1.6</td>\n",
" <td>18575.0</td>\n",
" <td>14579.0</td>\n",
" <td>...</td>\n",
" <td>14.91</td>\n",
" <td>64.0</td>\n",
" <td>69.0</td>\n",
" <td>18.6</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>18064.288</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Portugal</th>\n",
" <td>18.0</td>\n",
" <td>5.7</td>\n",
" <td>6.5</td>\n",
" <td>0.9</td>\n",
" <td>38.0</td>\n",
" <td>9.62</td>\n",
" <td>61.0</td>\n",
" <td>1.1</td>\n",
" <td>20086.0</td>\n",
" <td>31245.0</td>\n",
" <td>...</td>\n",
" <td>14.95</td>\n",
" <td>58.0</td>\n",
" <td>86.0</td>\n",
" <td>17.6</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>19121.592</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovenia</th>\n",
" <td>26.0</td>\n",
" <td>3.9</td>\n",
" <td>10.3</td>\n",
" <td>0.5</td>\n",
" <td>85.0</td>\n",
" <td>5.63</td>\n",
" <td>63.0</td>\n",
" <td>0.4</td>\n",
" <td>19326.0</td>\n",
" <td>18465.0</td>\n",
" <td>...</td>\n",
" <td>14.62</td>\n",
" <td>52.0</td>\n",
" <td>88.0</td>\n",
" <td>18.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>20732.482</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spain</th>\n",
" <td>24.0</td>\n",
" <td>4.2</td>\n",
" <td>7.3</td>\n",
" <td>0.1</td>\n",
" <td>55.0</td>\n",
" <td>5.89</td>\n",
" <td>56.0</td>\n",
" <td>0.6</td>\n",
" <td>22477.0</td>\n",
" <td>24774.0</td>\n",
" <td>...</td>\n",
" <td>16.06</td>\n",
" <td>69.0</td>\n",
" <td>71.0</td>\n",
" <td>17.6</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>25864.721</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Korea</th>\n",
" <td>30.0</td>\n",
" <td>2.1</td>\n",
" <td>10.4</td>\n",
" <td>4.2</td>\n",
" <td>82.0</td>\n",
" <td>18.72</td>\n",
" <td>64.0</td>\n",
" <td>1.1</td>\n",
" <td>19510.0</td>\n",
" <td>29091.0</td>\n",
" <td>...</td>\n",
" <td>14.63</td>\n",
" <td>76.0</td>\n",
" <td>78.0</td>\n",
" <td>17.5</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>27195.197</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>21.0</td>\n",
" <td>4.7</td>\n",
" <td>5.0</td>\n",
" <td>1.1</td>\n",
" <td>57.0</td>\n",
" <td>3.66</td>\n",
" <td>56.0</td>\n",
" <td>0.7</td>\n",
" <td>25166.0</td>\n",
" <td>54987.0</td>\n",
" <td>...</td>\n",
" <td>14.98</td>\n",
" <td>75.0</td>\n",
" <td>71.0</td>\n",
" <td>16.8</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>29866.581</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>24.0</td>\n",
" <td>1.4</td>\n",
" <td>7.3</td>\n",
" <td>6.4</td>\n",
" <td>94.0</td>\n",
" <td>22.26</td>\n",
" <td>72.0</td>\n",
" <td>0.3</td>\n",
" <td>26111.0</td>\n",
" <td>86764.0</td>\n",
" <td>...</td>\n",
" <td>14.93</td>\n",
" <td>53.0</td>\n",
" <td>85.0</td>\n",
" <td>16.3</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>32485.545</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>21.0</td>\n",
" <td>6.4</td>\n",
" <td>2.5</td>\n",
" <td>3.7</td>\n",
" <td>85.0</td>\n",
" <td>16.03</td>\n",
" <td>67.0</td>\n",
" <td>2.3</td>\n",
" <td>22104.0</td>\n",
" <td>52933.0</td>\n",
" <td>...</td>\n",
" <td>14.48</td>\n",
" <td>68.0</td>\n",
" <td>68.0</td>\n",
" <td>15.8</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>35343.336</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>11.0</td>\n",
" <td>2.2</td>\n",
" <td>10.3</td>\n",
" <td>0.2</td>\n",
" <td>74.0</td>\n",
" <td>13.87</td>\n",
" <td>73.0</td>\n",
" <td>1.2</td>\n",
" <td>23815.0</td>\n",
" <td>28290.0</td>\n",
" <td>...</td>\n",
" <td>14.87</td>\n",
" <td>77.0</td>\n",
" <td>89.0</td>\n",
" <td>18.1</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>37044.891</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>3.5</td>\n",
" <td>0.5</td>\n",
" <td>73.0</td>\n",
" <td>8.15</td>\n",
" <td>64.0</td>\n",
" <td>0.6</td>\n",
" <td>28799.0</td>\n",
" <td>48741.0</td>\n",
" <td>...</td>\n",
" <td>15.33</td>\n",
" <td>80.0</td>\n",
" <td>82.0</td>\n",
" <td>16.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>37675.006</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>21.0</td>\n",
" <td>6.6</td>\n",
" <td>4.5</td>\n",
" <td>2.0</td>\n",
" <td>72.0</td>\n",
" <td>4.57</td>\n",
" <td>62.0</td>\n",
" <td>1.1</td>\n",
" <td>28307.0</td>\n",
" <td>83876.0</td>\n",
" <td>...</td>\n",
" <td>15.71</td>\n",
" <td>89.0</td>\n",
" <td>87.0</td>\n",
" <td>18.9</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>40106.632</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>16.0</td>\n",
" <td>3.6</td>\n",
" <td>4.5</td>\n",
" <td>0.1</td>\n",
" <td>86.0</td>\n",
" <td>5.25</td>\n",
" <td>73.0</td>\n",
" <td>0.5</td>\n",
" <td>31252.0</td>\n",
" <td>50394.0</td>\n",
" <td>...</td>\n",
" <td>15.31</td>\n",
" <td>72.0</td>\n",
" <td>95.0</td>\n",
" <td>18.2</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>40996.511</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finland</th>\n",
" <td>15.0</td>\n",
" <td>2.4</td>\n",
" <td>9.0</td>\n",
" <td>0.6</td>\n",
" <td>85.0</td>\n",
" <td>3.58</td>\n",
" <td>69.0</td>\n",
" <td>1.4</td>\n",
" <td>27927.0</td>\n",
" <td>18761.0</td>\n",
" <td>...</td>\n",
" <td>14.89</td>\n",
" <td>69.0</td>\n",
" <td>94.0</td>\n",
" <td>19.7</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>41973.988</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Canada</th>\n",
" <td>15.0</td>\n",
" <td>1.3</td>\n",
" <td>10.5</td>\n",
" <td>0.2</td>\n",
" <td>89.0</td>\n",
" <td>3.94</td>\n",
" <td>72.0</td>\n",
" <td>1.5</td>\n",
" <td>29365.0</td>\n",
" <td>67913.0</td>\n",
" <td>...</td>\n",
" <td>14.25</td>\n",
" <td>61.0</td>\n",
" <td>91.0</td>\n",
" <td>17.2</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>43331.961</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>30.0</td>\n",
" <td>4.9</td>\n",
" <td>6.1</td>\n",
" <td>0.0</td>\n",
" <td>73.0</td>\n",
" <td>0.45</td>\n",
" <td>74.0</td>\n",
" <td>0.9</td>\n",
" <td>27888.0</td>\n",
" <td>77961.0</td>\n",
" <td>...</td>\n",
" <td>15.44</td>\n",
" <td>75.0</td>\n",
" <td>92.0</td>\n",
" <td>18.7</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>43603.115</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>27.0</td>\n",
" <td>3.4</td>\n",
" <td>7.1</td>\n",
" <td>1.0</td>\n",
" <td>83.0</td>\n",
" <td>7.61</td>\n",
" <td>72.0</td>\n",
" <td>0.4</td>\n",
" <td>31173.0</td>\n",
" <td>49887.0</td>\n",
" <td>...</td>\n",
" <td>14.46</td>\n",
" <td>75.0</td>\n",
" <td>94.0</td>\n",
" <td>17.0</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>43724.031</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom</th>\n",
" <td>13.0</td>\n",
" <td>1.9</td>\n",
" <td>11.5</td>\n",
" <td>0.2</td>\n",
" <td>78.0</td>\n",
" <td>12.70</td>\n",
" <td>71.0</td>\n",
" <td>0.3</td>\n",
" <td>27029.0</td>\n",
" <td>60778.0</td>\n",
" <td>...</td>\n",
" <td>14.83</td>\n",
" <td>66.0</td>\n",
" <td>88.0</td>\n",
" <td>16.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>43770.688</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sweden</th>\n",
" <td>10.0</td>\n",
" <td>5.1</td>\n",
" <td>10.9</td>\n",
" <td>0.0</td>\n",
" <td>88.0</td>\n",
" <td>1.13</td>\n",
" <td>74.0</td>\n",
" <td>0.7</td>\n",
" <td>29185.0</td>\n",
" <td>60328.0</td>\n",
" <td>...</td>\n",
" <td>15.11</td>\n",
" <td>86.0</td>\n",
" <td>95.0</td>\n",
" <td>19.3</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>49866.266</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iceland</th>\n",
" <td>18.0</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>0.4</td>\n",
" <td>71.0</td>\n",
" <td>12.25</td>\n",
" <td>82.0</td>\n",
" <td>0.3</td>\n",
" <td>23965.0</td>\n",
" <td>43045.0</td>\n",
" <td>...</td>\n",
" <td>14.61</td>\n",
" <td>81.0</td>\n",
" <td>97.0</td>\n",
" <td>19.8</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>50854.583</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>13.0</td>\n",
" <td>2.1</td>\n",
" <td>10.5</td>\n",
" <td>1.1</td>\n",
" <td>76.0</td>\n",
" <td>14.02</td>\n",
" <td>72.0</td>\n",
" <td>0.8</td>\n",
" <td>31588.0</td>\n",
" <td>47657.0</td>\n",
" <td>...</td>\n",
" <td>14.41</td>\n",
" <td>93.0</td>\n",
" <td>91.0</td>\n",
" <td>19.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>50961.865</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ireland</th>\n",
" <td>13.0</td>\n",
" <td>2.6</td>\n",
" <td>9.0</td>\n",
" <td>0.2</td>\n",
" <td>75.0</td>\n",
" <td>4.20</td>\n",
" <td>60.0</td>\n",
" <td>0.8</td>\n",
" <td>23917.0</td>\n",
" <td>31580.0</td>\n",
" <td>...</td>\n",
" <td>15.19</td>\n",
" <td>70.0</td>\n",
" <td>80.0</td>\n",
" <td>17.6</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>51350.744</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>15.0</td>\n",
" <td>3.9</td>\n",
" <td>7.0</td>\n",
" <td>0.9</td>\n",
" <td>78.0</td>\n",
" <td>2.03</td>\n",
" <td>73.0</td>\n",
" <td>0.3</td>\n",
" <td>26491.0</td>\n",
" <td>44488.0</td>\n",
" <td>...</td>\n",
" <td>16.06</td>\n",
" <td>88.0</td>\n",
" <td>94.0</td>\n",
" <td>19.4</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>52114.165</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States</th>\n",
" <td>18.0</td>\n",
" <td>1.5</td>\n",
" <td>8.3</td>\n",
" <td>0.1</td>\n",
" <td>89.0</td>\n",
" <td>11.30</td>\n",
" <td>67.0</td>\n",
" <td>5.2</td>\n",
" <td>41355.0</td>\n",
" <td>145769.0</td>\n",
" <td>...</td>\n",
" <td>14.27</td>\n",
" <td>68.0</td>\n",
" <td>85.0</td>\n",
" <td>17.2</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>55805.204</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>16.0</td>\n",
" <td>3.3</td>\n",
" <td>8.1</td>\n",
" <td>0.3</td>\n",
" <td>82.0</td>\n",
" <td>2.82</td>\n",
" <td>75.0</td>\n",
" <td>0.6</td>\n",
" <td>33492.0</td>\n",
" <td>8797.0</td>\n",
" <td>...</td>\n",
" <td>15.56</td>\n",
" <td>78.0</td>\n",
" <td>94.0</td>\n",
" <td>17.9</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>74822.106</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Switzerland</th>\n",
" <td>20.0</td>\n",
" <td>4.2</td>\n",
" <td>8.4</td>\n",
" <td>0.0</td>\n",
" <td>86.0</td>\n",
" <td>6.72</td>\n",
" <td>80.0</td>\n",
" <td>0.5</td>\n",
" <td>33491.0</td>\n",
" <td>108823.0</td>\n",
" <td>...</td>\n",
" <td>14.98</td>\n",
" <td>49.0</td>\n",
" <td>96.0</td>\n",
" <td>17.3</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>80675.308</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Luxembourg</th>\n",
" <td>12.0</td>\n",
" <td>4.3</td>\n",
" <td>6.0</td>\n",
" <td>0.1</td>\n",
" <td>78.0</td>\n",
" <td>3.47</td>\n",
" <td>66.0</td>\n",
" <td>0.4</td>\n",
" <td>38951.0</td>\n",
" <td>61765.0</td>\n",
" <td>...</td>\n",
" <td>15.12</td>\n",
" <td>91.0</td>\n",
" <td>86.0</td>\n",
" <td>15.1</td>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>101994.093</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>36 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" Air pollution Assault rate Consultation on rule-making \\\n",
"Country \n",
"Brazil 18.0 7.9 4.0 \n",
"Mexico 30.0 12.8 9.0 \n",
"Russia 15.0 3.8 2.5 \n",
"Turkey 35.0 5.0 5.5 \n",
"Hungary 15.0 3.6 7.9 \n",
"Poland 33.0 1.4 10.8 \n",
"Chile 46.0 6.9 2.0 \n",
"Slovak Republic 13.0 3.0 6.6 \n",
"Czech Republic 16.0 2.8 6.8 \n",
"Estonia 9.0 5.5 3.3 \n",
"Greece 27.0 3.7 6.5 \n",
"Portugal 18.0 5.7 6.5 \n",
"Slovenia 26.0 3.9 10.3 \n",
"Spain 24.0 4.2 7.3 \n",
"Korea 30.0 2.1 10.4 \n",
"Italy 21.0 4.7 5.0 \n",
"Japan 24.0 1.4 7.3 \n",
"Israel 21.0 6.4 2.5 \n",
"New Zealand 11.0 2.2 10.3 \n",
"France 12.0 5.0 3.5 \n",
"Belgium 21.0 6.6 4.5 \n",
"Germany 16.0 3.6 4.5 \n",
"Finland 15.0 2.4 9.0 \n",
"Canada 15.0 1.3 10.5 \n",
"Netherlands 30.0 4.9 6.1 \n",
"Austria 27.0 3.4 7.1 \n",
"United Kingdom 13.0 1.9 11.5 \n",
"Sweden 10.0 5.1 10.9 \n",
"Iceland 18.0 2.7 5.1 \n",
"Australia 13.0 2.1 10.5 \n",
"Ireland 13.0 2.6 9.0 \n",
"Denmark 15.0 3.9 7.0 \n",
"United States 18.0 1.5 8.3 \n",
"Norway 16.0 3.3 8.1 \n",
"Switzerland 20.0 4.2 8.4 \n",
"Luxembourg 12.0 4.3 6.0 \n",
"\n",
" Dwellings without basic facilities Educational attainment \\\n",
"Country \n",
"Brazil 6.7 45.0 \n",
"Mexico 4.2 37.0 \n",
"Russia 15.1 94.0 \n",
"Turkey 12.7 34.0 \n",
"Hungary 4.8 82.0 \n",
"Poland 3.2 90.0 \n",
"Chile 9.4 57.0 \n",
"Slovak Republic 0.6 92.0 \n",
"Czech Republic 0.9 92.0 \n",
"Estonia 8.1 90.0 \n",
"Greece 0.7 68.0 \n",
"Portugal 0.9 38.0 \n",
"Slovenia 0.5 85.0 \n",
"Spain 0.1 55.0 \n",
"Korea 4.2 82.0 \n",
"Italy 1.1 57.0 \n",
"Japan 6.4 94.0 \n",
"Israel 3.7 85.0 \n",
"New Zealand 0.2 74.0 \n",
"France 0.5 73.0 \n",
"Belgium 2.0 72.0 \n",
"Germany 0.1 86.0 \n",
"Finland 0.6 85.0 \n",
"Canada 0.2 89.0 \n",
"Netherlands 0.0 73.0 \n",
"Austria 1.0 83.0 \n",
"United Kingdom 0.2 78.0 \n",
"Sweden 0.0 88.0 \n",
"Iceland 0.4 71.0 \n",
"Australia 1.1 76.0 \n",
"Ireland 0.2 75.0 \n",
"Denmark 0.9 78.0 \n",
"United States 0.1 89.0 \n",
"Norway 0.3 82.0 \n",
"Switzerland 0.0 86.0 \n",
"Luxembourg 0.1 78.0 \n",
"\n",
" Employees working very long hours Employment rate \\\n",
"Country \n",
"Brazil 10.41 67.0 \n",
"Mexico 28.83 61.0 \n",
"Russia 0.16 69.0 \n",
"Turkey 40.86 50.0 \n",
"Hungary 3.19 58.0 \n",
"Poland 7.41 60.0 \n",
"Chile 15.42 62.0 \n",
"Slovak Republic 7.02 60.0 \n",
"Czech Republic 6.98 68.0 \n",
"Estonia 3.30 68.0 \n",
"Greece 6.16 49.0 \n",
"Portugal 9.62 61.0 \n",
"Slovenia 5.63 63.0 \n",
"Spain 5.89 56.0 \n",
"Korea 18.72 64.0 \n",
"Italy 3.66 56.0 \n",
"Japan 22.26 72.0 \n",
"Israel 16.03 67.0 \n",
"New Zealand 13.87 73.0 \n",
"France 8.15 64.0 \n",
"Belgium 4.57 62.0 \n",
"Germany 5.25 73.0 \n",
"Finland 3.58 69.0 \n",
"Canada 3.94 72.0 \n",
"Netherlands 0.45 74.0 \n",
"Austria 7.61 72.0 \n",
"United Kingdom 12.70 71.0 \n",
"Sweden 1.13 74.0 \n",
"Iceland 12.25 82.0 \n",
"Australia 14.02 72.0 \n",
"Ireland 4.20 60.0 \n",
"Denmark 2.03 73.0 \n",
"United States 11.30 67.0 \n",
"Norway 2.82 75.0 \n",
"Switzerland 6.72 80.0 \n",
"Luxembourg 3.47 66.0 \n",
"\n",
" Homicide rate Household net adjusted disposable income \\\n",
"Country \n",
"Brazil 25.5 11664.0 \n",
"Mexico 23.4 13085.0 \n",
"Russia 12.8 19292.0 \n",
"Turkey 1.2 14095.0 \n",
"Hungary 1.3 15442.0 \n",
"Poland 0.9 17852.0 \n",
"Chile 4.4 14533.0 \n",
"Slovak Republic 1.2 17503.0 \n",
"Czech Republic 0.8 18404.0 \n",
"Estonia 4.8 15167.0 \n",
"Greece 1.6 18575.0 \n",
"Portugal 1.1 20086.0 \n",
"Slovenia 0.4 19326.0 \n",
"Spain 0.6 22477.0 \n",
"Korea 1.1 19510.0 \n",
"Italy 0.7 25166.0 \n",
"Japan 0.3 26111.0 \n",
"Israel 2.3 22104.0 \n",
"New Zealand 1.2 23815.0 \n",
"France 0.6 28799.0 \n",
"Belgium 1.1 28307.0 \n",
"Germany 0.5 31252.0 \n",
"Finland 1.4 27927.0 \n",
"Canada 1.5 29365.0 \n",
"Netherlands 0.9 27888.0 \n",
"Austria 0.4 31173.0 \n",
"United Kingdom 0.3 27029.0 \n",
"Sweden 0.7 29185.0 \n",
"Iceland 0.3 23965.0 \n",
"Australia 0.8 31588.0 \n",
"Ireland 0.8 23917.0 \n",
"Denmark 0.3 26491.0 \n",
"United States 5.2 41355.0 \n",
"Norway 0.6 33492.0 \n",
"Switzerland 0.5 33491.0 \n",
"Luxembourg 0.4 38951.0 \n",
"\n",
" Household net financial wealth ... \\\n",
"Country ... \n",
"Brazil 6844.0 ... \n",
"Mexico 9056.0 ... \n",
"Russia 3412.0 ... \n",
"Turkey 3251.0 ... \n",
"Hungary 13277.0 ... \n",
"Poland 10919.0 ... \n",
"Chile 17733.0 ... \n",
"Slovak Republic 8663.0 ... \n",
"Czech Republic 17299.0 ... \n",
"Estonia 7680.0 ... \n",
"Greece 14579.0 ... \n",
"Portugal 31245.0 ... \n",
"Slovenia 18465.0 ... \n",
"Spain 24774.0 ... \n",
"Korea 29091.0 ... \n",
"Italy 54987.0 ... \n",
"Japan 86764.0 ... \n",
"Israel 52933.0 ... \n",
"New Zealand 28290.0 ... \n",
"France 48741.0 ... \n",
"Belgium 83876.0 ... \n",
"Germany 50394.0 ... \n",
"Finland 18761.0 ... \n",
"Canada 67913.0 ... \n",
"Netherlands 77961.0 ... \n",
"Austria 49887.0 ... \n",
"United Kingdom 60778.0 ... \n",
"Sweden 60328.0 ... \n",
"Iceland 43045.0 ... \n",
"Australia 47657.0 ... \n",
"Ireland 31580.0 ... \n",
"Denmark 44488.0 ... \n",
"United States 145769.0 ... \n",
"Norway 8797.0 ... \n",
"Switzerland 108823.0 ... \n",
"Luxembourg 61765.0 ... \n",
"\n",
" Time devoted to leisure and personal care Voter turnout \\\n",
"Country \n",
"Brazil 14.97 79.0 \n",
"Mexico 13.89 63.0 \n",
"Russia 14.97 65.0 \n",
"Turkey 13.42 88.0 \n",
"Hungary 15.04 62.0 \n",
"Poland 14.20 55.0 \n",
"Chile 14.41 49.0 \n",
"Slovak Republic 14.99 59.0 \n",
"Czech Republic 14.98 59.0 \n",
"Estonia 14.90 64.0 \n",
"Greece 14.91 64.0 \n",
"Portugal 14.95 58.0 \n",
"Slovenia 14.62 52.0 \n",
"Spain 16.06 69.0 \n",
"Korea 14.63 76.0 \n",
"Italy 14.98 75.0 \n",
"Japan 14.93 53.0 \n",
"Israel 14.48 68.0 \n",
"New Zealand 14.87 77.0 \n",
"France 15.33 80.0 \n",
"Belgium 15.71 89.0 \n",
"Germany 15.31 72.0 \n",
"Finland 14.89 69.0 \n",
"Canada 14.25 61.0 \n",
"Netherlands 15.44 75.0 \n",
"Austria 14.46 75.0 \n",
"United Kingdom 14.83 66.0 \n",
"Sweden 15.11 86.0 \n",
"Iceland 14.61 81.0 \n",
"Australia 14.41 93.0 \n",
"Ireland 15.19 70.0 \n",
"Denmark 16.06 88.0 \n",
"United States 14.27 68.0 \n",
"Norway 15.56 78.0 \n",
"Switzerland 14.98 49.0 \n",
"Luxembourg 15.12 91.0 \n",
"\n",
" Water quality Years in education \\\n",
"Country \n",
"Brazil 72.0 16.3 \n",
"Mexico 67.0 14.4 \n",
"Russia 56.0 16.0 \n",
"Turkey 62.0 16.4 \n",
"Hungary 77.0 17.6 \n",
"Poland 79.0 18.4 \n",
"Chile 73.0 16.5 \n",
"Slovak Republic 81.0 16.3 \n",
"Czech Republic 85.0 18.1 \n",
"Estonia 79.0 17.5 \n",
"Greece 69.0 18.6 \n",
"Portugal 86.0 17.6 \n",
"Slovenia 88.0 18.4 \n",
"Spain 71.0 17.6 \n",
"Korea 78.0 17.5 \n",
"Italy 71.0 16.8 \n",
"Japan 85.0 16.3 \n",
"Israel 68.0 15.8 \n",
"New Zealand 89.0 18.1 \n",
"France 82.0 16.4 \n",
"Belgium 87.0 18.9 \n",
"Germany 95.0 18.2 \n",
"Finland 94.0 19.7 \n",
"Canada 91.0 17.2 \n",
"Netherlands 92.0 18.7 \n",
"Austria 94.0 17.0 \n",
"United Kingdom 88.0 16.4 \n",
"Sweden 95.0 19.3 \n",
"Iceland 97.0 19.8 \n",
"Australia 91.0 19.4 \n",
"Ireland 80.0 17.6 \n",
"Denmark 94.0 19.4 \n",
"United States 85.0 17.2 \n",
"Norway 94.0 17.9 \n",
"Switzerland 96.0 17.3 \n",
"Luxembourg 86.0 15.1 \n",
"\n",
" Subject Descriptor \\\n",
"Country \n",
"Brazil Gross domestic product per capita, current prices \n",
"Mexico Gross domestic product per capita, current prices \n",
"Russia Gross domestic product per capita, current prices \n",
"Turkey Gross domestic product per capita, current prices \n",
"Hungary Gross domestic product per capita, current prices \n",
"Poland Gross domestic product per capita, current prices \n",
"Chile Gross domestic product per capita, current prices \n",
"Slovak Republic Gross domestic product per capita, current prices \n",
"Czech Republic Gross domestic product per capita, current prices \n",
"Estonia Gross domestic product per capita, current prices \n",
"Greece Gross domestic product per capita, current prices \n",
"Portugal Gross domestic product per capita, current prices \n",
"Slovenia Gross domestic product per capita, current prices \n",
"Spain Gross domestic product per capita, current prices \n",
"Korea Gross domestic product per capita, current prices \n",
"Italy Gross domestic product per capita, current prices \n",
"Japan Gross domestic product per capita, current prices \n",
"Israel Gross domestic product per capita, current prices \n",
"New Zealand Gross domestic product per capita, current prices \n",
"France Gross domestic product per capita, current prices \n",
"Belgium Gross domestic product per capita, current prices \n",
"Germany Gross domestic product per capita, current prices \n",
"Finland Gross domestic product per capita, current prices \n",
"Canada Gross domestic product per capita, current prices \n",
"Netherlands Gross domestic product per capita, current prices \n",
"Austria Gross domestic product per capita, current prices \n",
"United Kingdom Gross domestic product per capita, current prices \n",
"Sweden Gross domestic product per capita, current prices \n",
"Iceland Gross domestic product per capita, current prices \n",
"Australia Gross domestic product per capita, current prices \n",
"Ireland Gross domestic product per capita, current prices \n",
"Denmark Gross domestic product per capita, current prices \n",
"United States Gross domestic product per capita, current prices \n",
"Norway Gross domestic product per capita, current prices \n",
"Switzerland Gross domestic product per capita, current prices \n",
"Luxembourg Gross domestic product per capita, current prices \n",
"\n",
" Units Scale \\\n",
"Country \n",
"Brazil U.S. dollars Units \n",
"Mexico U.S. dollars Units \n",
"Russia U.S. dollars Units \n",
"Turkey U.S. dollars Units \n",
"Hungary U.S. dollars Units \n",
"Poland U.S. dollars Units \n",
"Chile U.S. dollars Units \n",
"Slovak Republic U.S. dollars Units \n",
"Czech Republic U.S. dollars Units \n",
"Estonia U.S. dollars Units \n",
"Greece U.S. dollars Units \n",
"Portugal U.S. dollars Units \n",
"Slovenia U.S. dollars Units \n",
"Spain U.S. dollars Units \n",
"Korea U.S. dollars Units \n",
"Italy U.S. dollars Units \n",
"Japan U.S. dollars Units \n",
"Israel U.S. dollars Units \n",
"New Zealand U.S. dollars Units \n",
"France U.S. dollars Units \n",
"Belgium U.S. dollars Units \n",
"Germany U.S. dollars Units \n",
"Finland U.S. dollars Units \n",
"Canada U.S. dollars Units \n",
"Netherlands U.S. dollars Units \n",
"Austria U.S. dollars Units \n",
"United Kingdom U.S. dollars Units \n",
"Sweden U.S. dollars Units \n",
"Iceland U.S. dollars Units \n",
"Australia U.S. dollars Units \n",
"Ireland U.S. dollars Units \n",
"Denmark U.S. dollars Units \n",
"United States U.S. dollars Units \n",
"Norway U.S. dollars Units \n",
"Switzerland U.S. dollars Units \n",
"Luxembourg U.S. dollars Units \n",
"\n",
" Country/Series-specific Notes \\\n",
"Country \n",
"Brazil See notes for: Gross domestic product, curren... \n",
"Mexico See notes for: Gross domestic product, curren... \n",
"Russia See notes for: Gross domestic product, curren... \n",
"Turkey See notes for: Gross domestic product, curren... \n",
"Hungary See notes for: Gross domestic product, curren... \n",
"Poland See notes for: Gross domestic product, curren... \n",
"Chile See notes for: Gross domestic product, curren... \n",
"Slovak Republic See notes for: Gross domestic product, curren... \n",
"Czech Republic See notes for: Gross domestic product, curren... \n",
"Estonia See notes for: Gross domestic product, curren... \n",
"Greece See notes for: Gross domestic product, curren... \n",
"Portugal See notes for: Gross domestic product, curren... \n",
"Slovenia See notes for: Gross domestic product, curren... \n",
"Spain See notes for: Gross domestic product, curren... \n",
"Korea See notes for: Gross domestic product, curren... \n",
"Italy See notes for: Gross domestic product, curren... \n",
"Japan See notes for: Gross domestic product, curren... \n",
"Israel See notes for: Gross domestic product, curren... \n",
"New Zealand See notes for: Gross domestic product, curren... \n",
"France See notes for: Gross domestic product, curren... \n",
"Belgium See notes for: Gross domestic product, curren... \n",
"Germany See notes for: Gross domestic product, curren... \n",
"Finland See notes for: Gross domestic product, curren... \n",
"Canada See notes for: Gross domestic product, curren... \n",
"Netherlands See notes for: Gross domestic product, curren... \n",
"Austria See notes for: Gross domestic product, curren... \n",
"United Kingdom See notes for: Gross domestic product, curren... \n",
"Sweden See notes for: Gross domestic product, curren... \n",
"Iceland See notes for: Gross domestic product, curren... \n",
"Australia See notes for: Gross domestic product, curren... \n",
"Ireland See notes for: Gross domestic product, curren... \n",
"Denmark See notes for: Gross domestic product, curren... \n",
"United States See notes for: Gross domestic product, curren... \n",
"Norway See notes for: Gross domestic product, curren... \n",
"Switzerland See notes for: Gross domestic product, curren... \n",
"Luxembourg See notes for: Gross domestic product, curren... \n",
"\n",
" GDP per capita Estimates Start After \n",
"Country \n",
"Brazil 8669.998 2014.0 \n",
"Mexico 9009.280 2015.0 \n",
"Russia 9054.914 2015.0 \n",
"Turkey 9437.372 2013.0 \n",
"Hungary 12239.894 2015.0 \n",
"Poland 12495.334 2014.0 \n",
"Chile 13340.905 2014.0 \n",
"Slovak Republic 15991.736 2015.0 \n",
"Czech Republic 17256.918 2015.0 \n",
"Estonia 17288.083 2014.0 \n",
"Greece 18064.288 2014.0 \n",
"Portugal 19121.592 2014.0 \n",
"Slovenia 20732.482 2015.0 \n",
"Spain 25864.721 2014.0 \n",
"Korea 27195.197 2014.0 \n",
"Italy 29866.581 2015.0 \n",
"Japan 32485.545 2015.0 \n",
"Israel 35343.336 2015.0 \n",
"New Zealand 37044.891 2015.0 \n",
"France 37675.006 2015.0 \n",
"Belgium 40106.632 2014.0 \n",
"Germany 40996.511 2014.0 \n",
"Finland 41973.988 2014.0 \n",
"Canada 43331.961 2015.0 \n",
"Netherlands 43603.115 2014.0 \n",
"Austria 43724.031 2015.0 \n",
"United Kingdom 43770.688 2015.0 \n",
"Sweden 49866.266 2014.0 \n",
"Iceland 50854.583 2014.0 \n",
"Australia 50961.865 2014.0 \n",
"Ireland 51350.744 2014.0 \n",
"Denmark 52114.165 2015.0 \n",
"United States 55805.204 2015.0 \n",
"Norway 74822.106 2015.0 \n",
"Switzerland 80675.308 2015.0 \n",
"Luxembourg 101994.093 2014.0 \n",
"\n",
"[36 rows x 30 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
"full_country_stats.sort_values(by=\"GDP per capita\", inplace=True)\n",
"full_country_stats"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GDP per capita 55805.204\n",
"Life satisfaction 7.200\n",
"Name: United States, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_country_stats[[\"GDP per capita\", 'Life satisfaction']].loc[\"United States\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
"keep_indices = list(set(range(36)) - set(remove_indices))\n",
"\n",
"sample_data = full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[keep_indices]\n",
"missing_data = full_country_stats[[\"GDP per capita\", 'Life satisfaction']].iloc[remove_indices]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure money_happy_scatterplot\n"
]
},
{
"data": {
"image/png": 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gwQL/iLdatWrs3r2bBg0alHFW7BOpdZpKZYADC3GKyCvAbcaYI+D3+3XD93cycKuIzMaagrgd+MiFfiJKeQsp+nBzNBaOAfUqWU5Fw3jL60/s1rRNYFBCXFwce/bsoU2bNo5cO5rJycmhdevWrvfjM8AZGRmu9+UUpU1BZGEV3QQYhpX5zG0ewkru8yPWXPMq4J8R6Nc1wplGcGsRLdypDa+S5UQyjNfpaZu8vDw+/PBD//vCwkLHQ3KjEZ+XVSSi0irVCBhYBswUkZVYNeEm2IEYJ2CMucEJMcaYPOBme6sUhDuN4MZoLFxNniXLieEw3vnz5xcJRsjNzWXPnj0eKooM+/fvByAtLa2MI8Onshng64A7gTOw5mUbALHj3xElOGGsnA7JDVeTm/OkZZE7aBDVYjCMd8qUKUUMQ05ODr/++quHiiLDtm3W/LzbLmhQyQywXeftLgAR2YwViLE/UsIqC14aKzc1eeHeduDAAc444ww2b97sdzmKBXJzc4tMP/jw+cdWZnyfsUWLFq73FeiGFiuE6gXRMrhNRBLtKQOlDKLRF9cJTZFOljN+/HgOHjxIQkK5c0h5im+kW61aNXJzc6lTpw5ZWVls377dY2XuE8kRsC/0OJaeLEK6k0XkL8AOY8x79vtJwDAR2QRcaYzZ4KLGSkE0ZvaKRk0lcejQIZ599lkAqlePrcTsKSkpZGZmsn79etq0acOQIUM455xzaNnyhHFNpcNXn61Ro0YR63PLli0R6ytcQh1K/AUrAQ8ichFwDXAtVmL2J4H+rqhTPCWa8gg/+eSTFBYWkpSUFJPRYyLifzS+4ooruPzyyz1WFBl8QRiR/Jv5Rt2xQKgGuBmwxX59BfCOMeZtEVmLVRlZqWREUx7hjIwMnn76aXJycvwLLbGILw1jJOZDo4V169YV8f6IBE7nG3aTUH+WDgO+Z4i+HK8Ll0dk/IOVCBJteYSfeeYZCm2vjVibfgjE92hclQwwQKdOnSLaXyy594VqgOcCL9lzv2cAn9rtbXE4IbtiU46sX04TTVWJjxw5whNPPEFWltV3LBvgr7+2CojHkgdHOPiKZF500UUR6zOSc81OEKoBvgVYCjQEBhpjfBnQOgHT3RBWpbGzfsVv+wUxxp/1y00jHJhYJ5qqEk+YMIGCggL/+1q1apVydHTz+eefey0hovimAiIRhuyjSZPYquEQqhvaYeDWYtofcFyRUmrWr3gXor6Km++NBt/lo0eP8thjj/lHvxDbBnjXrl00bNjQaxkRI5IuaD5atGjBmjVrItZfuJSWjKe+b6QrIvVLu0jAiFhxgIpk/aooJSXWWTqmF0vH9PLUC+L5558vMvoFYnoRDqwyPVUFnwGO5Jz3aaedFrG+nKC0EfBeEWlijNkD7KP4NJFit8dSVGjUU9GsXxWhtLwQbuQQDpVjx47xyCOPcOzYsSLtsTp/mpmZCcB5553nsZLI4fP6iOQI2DcFkZOTExM14UozwL04Xu2iF5UsT280E//oI0UqP4CV9SvBhaxf0TTfG8iGDRvIysqiTp06ZGZm+r0gTjrpJE91VRRfSG5KSoq3QiLIypUrgcg+tQwdOpTmzZtTrVq1iPUZDiUuwhljFhlj8u3XC+33xW6Rk1tFGDqUhJdfouDUFhgRCk5tQcLLL7mS9Sta68Z17NiRw4cPM2bMGAoLCzn99NOJi4ujcePGnuqqKD4DHGuPyOGwdOnSiPeZkpLCsGHDHCnKGQlCDUX2J2cPam8A7DHG6BSE0wwdSvzQoWzcuJFJkybxLxdTLkZjrgqwYvu/++47AL7//nvy8/NdL2vjFlUxCGPHjh3Uq1fPaxlRTahuaCX9nCRh1YZTXGLjxo2MHz+eHTt2uNqPW3XjwmX69OnUqFGDpKQkatasGTOPlsGsX78eiD03qXDp0qWL1xKimlJHwCJyh/3SADeJSGbA7nis6sXrXdKmANnZ2RQWFjJx4kQefPBBr+VEFF9E09133+2xkvBZtMiaqYt0WK5X+FwHq5LXR0UoawrC5/srwAgg0CcoFys/xE3Oy1J85OTkUK1aNZ5//nnuv//+KvMfGGDhwoUAXHLJJd4KcYBVq1Z5LSGi+FJtVqU574pQqgH25QEWkc+Aq40xVaOOdhSRnZ1NYmIiubm5zJ07l0svvdRrSRFj6tSpAJx77rkeK3GGSOdE8JJIJmKPZUKaAzbG9FTj6w2+KYgjR47w1FNPeS0nonzyySc0adIk5hKwB+MLJunRo4e3QiJIeaPgtmzZwjnnnFOkbdy4cTzxxBNF2nbv3k3//v1p3749bdq04bLLLnNGsEeEfGeLSGtgINACKLIS4lRRTrufM4G1wLvGmD84dd1YJTs72/8feMmSJezYsYNmzbxJCxlJfP+B//a3v3msJHx8FRrOOussj5VEjp9//hmA5s2bO3rd+++/n759+3LbbbcBxFTYcXGENAIWkcuBNVi5gG8AzgIuA36HlaDHSZ7HKk2vYM0BB4bjvvTSSx6qiRyfffYZAH379vVYSfhUxcfxZcuWATgejbZz584iRj0S1ZbdJFQ3tH8ADxpjfoNVGfk6IAWYByx0SoyIDAYOcTzfcJUnKyvLb4BzcnKKzY9QGfnvf/8LcMJjaSxSFYMwdu3a5cp1b7nlFv70pz/Rs2dP/vnPf8ZU/bfiCNUAnwW8Zb/OA2oYY7KxDPPtTggRkTr29Up95hSRG0VkhYis2Lt3rxNdRzVHjx4t8j4nJ4e5c+d6pCYyGGNYtmwZqampMVl+KJjNm62U2ZHMieA1S5YsKZdxLClyLbi9X79+/Pzzz4wcOZL169fTsWNHYtkOhHp3H+F45YudWEnZwZpDdirU5SFgkjGm1JRfxpiJxph0Y0x6rCVfrgjBBrgqLMb99NNPANx66wkZUGOS5cuXA7GdSrO81KtXr1xBJw0aNODgwaLr/AcOHCg2fWf9+vW59tpree211+jcuTOLFy8OW69XhGqAvwS62a8/Bp4UkQeAycAX4YoQkQ5AH+DpcK9V2QjOBgbW/GisP3qVxrx58wDo06ePx0qcwYucCLFGrVq1aNKkCfPnW7OPBw4cYPbs2XTr1q3IcQsWLPD/nzhy5AibNm2K6bn1UL0g7gB8P9/jgNpYFZF/tPeFSw+sOeVf7EeOWkC8iLQxxlQd58liyMrKolq1aiQnJ3P48GGaNGlS6f1Jn3/+eQDOPPNMj5U4w4EDB2jatKnXMqKeV199lVtuucXv+fLAAw/QqlUrXnzxRQBuuukmVq5cyejRo0lISKCwsJARI0bQuXNnADp06MC3337rmf6KIMZ4n2VSRGoAgTnr7sQyyH82xpQ4wZOenm5WrFjhsjpvWb58OT/++CNHjx7l5ptvZv78+fTq1ctrWa5RWFhIfHw8Xbp04csvv/RajiOICNdccw1vvfVW2QcrUYOIrDTGpLvZR6huaI1EpFHA+3Yi8rCIDHFChDHmmDFml28DMoHs0oxvVeH888/n+uuv5+qrrwZg9erVHityl3Xr1gHWaKcykJGRAUB6uqv/j5UYJdQ54LexfIARkYbAYiwf4BdFxHFPeWPMOA3CKIovD+4HH3zgsRJ3+d///gdQaUb5vjSUVSkRuxI6oRrgNGC5/Xog8JMxpi1wPTDKDWHKiSQlJfkT1FRWfB4elcVntioGYSihE6oBro41LQCWt4JvGPYNUHWcGz1m0KBBAJU2ECM/P58dO3bQr18/r6U4htNBGO+//z4i4s8vXF5mzpzJ999/X+7zpkyZwujRowF48cUXefXVVyvUv1KUUA3wRuBqETkVuBjwRQI0xopcUyJAz549geNx9pUNX8rG4cOHeyvEQbp168aYMWM4+eSTHbne9OnT6datG2+++WaFzi/NAOfn54d0jZtuuonrr7++Qv0rRQnVAD8IPIaV/3e5Mca3PN0PqFqJTj2kXbt2AKxdu9ZjJe4we/Zs4PgPTWUgLS2Nf/3rX45E9GVmZrJ06VImTZrkN8ALFy6kf//+/mNGjx7NlClTAPj73/9OmzZtSEtL484772TZsmV88MEH3HXXXXTo0IFNmzbRo0cPxo4dS/fu3Xn22Wf58MMPOe+88+jYsSN9+vRh9+7dJ+gIzFL20ksv0blzZ9q3b8+AAQOK9VtXSiYkP2BjzAwRaQE0BQKX4ecB77khTDmRtm3bAvD111/7vSIqE77/1LFaeNNtZs6cySWXXELr1q2pX78+33zzTYnHHjhwgPfff5/169cjIhw6dIiTTjqJK6+8kv79+zNw4ED/sYcOHfJX7Dh48CDLly9HRHj55Zd5/PHHefLJJ0vs5+qrr2bkyJEA3HvvvUyaNKnSRDBGgpB/lo0xu40xq4wxhQFtXxpjtCRRhKhRowZApfQnzc3N5fDhw1xzzTVeS4lapk+fzuDBgwEYPHgw06dPL/HYOnXqkJyczIgRI5gxY4b/3imO3//+9/7X27dvp1+/frRr147x48f73QJL4rvvvuPCCy+kXbt2TJs2rczjlaLEfqaTKkbLli39yV0qE76giyFDHHEtr3Ts37+fBQsWMGLECFJSUhg/fjxvvfUW8fHxFBb6x0RkZ2cDkJCQwFdffcWAAQP8I+eSqFmzpv/1rbfeyujRo1m7di3//e9//dcrieHDh/Pcc8+xdu1aHnjggTKPV4qiBjjG8I1WKttc28cffwxA9+7dPVYSnbz77rtcf/31bN26lS1btrBt2zZatmwJwPfff09OTg4ZGRn+XAqZmZlkZGRw2WWX8cwzz/hDdGvXrs2RI0dK7CcjI8Of8N9XEqo0jhw5QpMmTcjLy2PatGnhfswqhxrgGMMX917ZHvUef/xxwMqipZzI9OnT+d3vflekbcCAAbzxxhtcc801pKWlMXToUDp27AhYhrF///6kpaXRvXt3nn7aynM1ePBgxo8fT8eOHdm0adMJ/YwbN45BgwZx4YUXFpuJLJiHHnqI8847j759+5KamurAJ61aREUuiIpSFXJBBLNx40Zat27NpEmTuOEGxypBecqxY8eoWbMmI0eOZOLEiV7LURQginJB2GIai8idIvIfOxwZEekqIi3dk6cE06pVK8BKy1dZ8KVrdNqzIz4+ng4dOvi3LVu2OHp9RQmXkNzQRORcrDJBm4G2wHhgH9AXaA1c65ZApSg+f9J33nmH119/3WM1zvD+++8DnJD7NVyqV69eanrC/Pz8mK+4rMQ2oY6AnwCeNcZ0xKoJ52MO0NVxVUqp9OjRg9zcXK9lOMacOXNISEiISMWIKVOmMGjQIK644gouvvhiMjMz6d27N506daJdu3bMmjULsMqkn3322YwcOZK2bdty8cUXk5WVBVgVO/r06UP79u3p1KmTfy51/PjxdO7cmbS0NB544AHXP4tSCTDGlLkBh4HT7ddHAl6nYKWNDOk6Tm/nnnuuqYo89dRTBjC7du3yWoojHDx40JXPEhcXZ9q3b2/at29vfvvb3xpjjJk8ebJp1qyZ2b9/vzHGmLy8PJORkWGMMWbv3r2mVatWprCw0GzevNnEx8ebVatWGWOMGTRokHnttdeMMcZ06dLFzJgxwxhjTFZWljl69KiZM2eOGTlypCksLDQFBQXm8ssvN4sWLXL8MymRA1hhXLZhoT5/ZVF87bdUYE94PwFKeWnfvj1ghSRXhqixk046yZXrljQF0bdvX+rXrw9YA5CxY8eyePFi4uLi2LFjhz/8tmXLlnTo0AGAc889ly1btnDkyBF27Njh90hITrZKJc6dO5e5c+f6vRAyMzPZuHEjF110kSufTakchGqAZwEPiMgg+70RkRSs/BAaihxhfDkhVq9eXWnqpkWSwMCDadOmsXfvXlauXEliYiIpKSn+YIKkpCT/cfHx8WRlZfmeCE/AGMM999zDqFGanVUJnVDngO8E6gN7gRrA58BPWJnQ7nVHmlISvmrQM2fO9FhJ7JORkcHJJ59MYmIin332mT+BeknUqVOH5s2b+7/7nJwcjh07Rr9+/XjllVfIzLSytu7YsYM9e/ThUCmdUJPxHAa6iUgvoBOW4f7GGDPPTXFKydSoUYPPP//caxkxz9ChQ7niiitIT0+nQ4cOIQUTvPbaa4waNYr777+fxMRE3nnnHS6++GKOEwz4AAAQm0lEQVR++OEHfvOb3wBWld/XX3/dsTSUSuWkxEAMESkAmhhj9ojIK8BtxpiSYxg9oCoGYvgYPnw4U6dOJT8/n/j4eK/lKEqlw+tAjCyOl6IfBiS7KUQpHz169AAslyhFUWKT0qYglgEzRWQlIMAEEckq7kBjTNgxsSKSBLyAVfKoPtYc81hjzKfhXrsyEpic/ayzzvJYjaIoFaG0EfB1WIEWJwEGaAA0KmFzggRgG9AdqAvcB7xte1soQbRp0waAr776ytHrBgZDfPLJJ5x55pn+umaKojhLSMl4RGQzkG6M2e++pCL9rgEeNMYU6+pWleeAAUSEFi1alLlyXx5q1apFZmYm8+fP58Ybb2Tu3Ln+/BNloaG9SmXC6zlgP8aYlh4Y38ZYeSYqV95FB+nUqROHDjlfE3XJkiWMHDmSjz/+2G98t27dSu/evUlLS6N3797+UfHw4cO544476NmzJ2PGjOHo0aPccMMNdO7cmY4dOxYJ7b3wwgvp1KkTnTp1YtmyZY7rVpRYozQviDuAF4wx2fbrEjHGPOWoKJFE4FNgkzFmVNC+G4EbAVq0aHGuk6O/WCMnJwcRoVq1ao5dMzExkdq1a7Nw4ULS0tL87VdccQUDBw5k2LBhvPLKK3zwwQfMnDmT4cOHs2/fPmbNmkV8fDxjx46lTZs2/OEPf+DQoUN06dKFVatWISLExcWRnJzMxo0bGTJkCFX56UWJfiIxAi7NAPunHezXJWGMMac7JkgkDngDqANcZYzJK+nYqj4F4QY1atSgV69etGrVimeffdbf3rBhQ3bu3EliYiJ5eXk0adKEffv2MXz4cHr27MmwYcMASE9PJzs72z8VceDAAebMmUPTpk0ZPXo03377LfHx8fz444+VrqqHUrmIhAEuccLOGNOyuNduIiICTAIaA5eVZnwVd4iLi+Ptt9+mT58+PPLII4wdO7bY46w/lUVgaK8xhvfee+8Ez4xx48bRuHFjVq9eTWFhoT+HgqJUZcIqSSQip4nI206JAf4DnA1cYYwp1uVNcZ8aNWrw0UcfMW3aNCZNmgTABRdcwJtvvglY+RNKyt3br18//v3vf/tzJqxatQqwQn6bNGlCXFwcr732GgUFBRH4JIoS3YS7ZH0SMMAJISJyGjAKK9/wroAR1ihjjFb7izD169dn9uzZXHTRRTRs2JAJEyZwww03MH78eBo1asTkyZOLPe++++7j9ttvJy0tDWMMKSkpfPTRR9x8880MGDCAd955h549exYZNStKVSWsmnAi0h4rJ4QnsbA6B6woiltEjRuaoiiK4jxqgMsguEzOlClTGD16tEdqFEWpTJQ6BywiH5Rxfh0HtShloJFmilK5KGsEvL+MbTPwqpsCo5nhw4fz7rvv+t/7RssLFy6kR48eDBw4kNTUVIYOHer3Cvjkk09ITU2lW7du/OUvf6F///6AldPhggsuoGPHjlxwwQVs2LABOLGI5HXXXeePLgMrn+0HH5T1O6koSjRS6nDKGPPHSAmJVrKysvx1wcAKLLjyyivLPG/VqlWsW7eOpk2b0rVrV5YuXUp6ejqjRo1i8eLFtGzZkiFDhviPT01NZfHixSQkJDBv3jzGjh3Le+9ZKTC++OIL1qxZQ/369Vm0aBFPP/00V111FRkZGSxbtoypU6c6/8EVRXEdfZ4tg+DCjlOmTAkphLZLly40b94cgA4dOrBlyxZq1arF6aefTsuWVlzLkCFDmDhxImD5yQ4bNoyNGzciIuTlHY9BCSwi2b17d2655Rb27NnDjBkzGDBggE5LKEqMootwYZCQkEBhYSFgRYDl5ub69wUXdMzPzy+xoCNY/rM9e/bku+++48MPP/QXhgRO8Jm97rrrmDZtGpMnT+aPf6zyDymKErOoAQ6DlJQUVq5cCcCsWbOKjFqLIzU1lZ9//pktW7YA8NZbb/n3ZWRk0KxZM8AaZZfG8OHDeeaZZwBo27ZtBdUriuI1aoDDYOTIkSxatIguXbrw5ZdflhndVb16dV544QUuueQSunXrRuPGjalbty4Ad999N/fccw9du3YtM0y3cePGnH322Tr6VZQYJ6xIOK+JxUi4zMxMatWqhTGGW265hTPPPJO//vWv5brGsWPHaNeuHd98843fgCuK4iwaCVcJeemll+jQoQNt27YlIyODUaNGlX1SAPPmzSM1NZVbb71Vja+ixDg6AlYURSkGHQEriqJUYtQAK4qieIQaYEVRFI9QA6woiuIRaoAVRVE8Qg2woiiKR6gBVhRF8Qg1wIqiKB6hBlhRFMUjosoAi0h9EXlfRI6KyFYRudZrTYqiKG4RbZm8nwdygcZAB+BjEVltjFnnrSxFURTniZoRsIjUBAYA9xljMo0xnwMfANd5q0xRFMUdomkE3BooMMb8GNC2GugeeJCI3AjcaL/NEZHvIqTPaRoC+7wWUQFiVTeodi+IVd0AZ7ndQTQZ4FpARlBbBlA7sMEYMxGYCCAiK9zOVuQWsao9VnWDaveCWNUNlna3+4iaKQggE6gT1FYHOOKBFkVRFNeJJgP8I5AgImcGtLUHdAFOUZRKSdQYYGPMUWAG8A8RqSkiXYGrgNdKOW1iRMS5Q6xqj1XdoNq9IFZ1QwS0R1VFDBGpD7wC9AX2A383xrzhrSpFURR3iCoDrCiKUpWImikIRVGUqoYaYEVRFI+ISQPsZc4IERktIitEJEdEpgTt6y0i60XkmIh8JiKnBexLEpFXROSwiOwSkTucOrcc2pNEZJL9nR0RkVUicmks6BeR10Vkp32NH0VkRCzoDrjWmSKSLSKvB7Rda/8tjorITHsNxLev1Hs8nHPLoXmhrTnT3jbEinb7WoNF5Af7WptE5EK7PXruF2NMzG3AdOAtrOCNblgBG20j1PfVwG+B/wBTAtob2joGAcnAeGB5wP5HgSVAPeBsYBdwSbjnllN7TWAckIL149sfy886Jdr1A22BJPt1qn2Nc6Ndd8C15trXej3g8xwBLrLv4zeAN0O5x8M5t5yaFwIjSvhbRLv2vsBW4Hyse72ZvUXV/RIxo+nUhmVEcoHWAW2vAf+KsI6HKWqAbwSWBenMAlLt9zuAiwP2P+S78cI514HPsQYrB0fM6McKEd0JXBMLuoHBwNtYP34+A/wI8EbAMa3s+7p2Wfd4OOeWU/dCijfAsaB9GfCnYtqj6n6JxSmIknJGtPVIj4+2tg7A79e8CWgrIvWApoH7Kao5nHMrjIg0xvo+18WCfhF5QUSOAeuxDPAn0a5bROoA/wD+FrQruO9N2MaHsu/xcM4tL4+KyD4RWSoiPWJBu4jEA+lAIxH5SUS2i8hzIlK9mP49vV9i0QCHlDPCA0rTVSvgffC+cM+tECKSCEwDphpj1seCfmPMzfZ5F2IF7eTEgO6HgEnGmG1B7WX1Xdo9Hs655WEMcDrWo/tE4EMRaRUD2hsDicBArHulA9ARuDeE/iGC90ssGuBozRlRmq7MgPfB+8I9t9yISBzWo10uMNoBDRHTb4wpMFaq0ubAn6NZt4h0APoATxezu6y+S7vHwzk3ZIwxXxpjjhhjcowxU4GlwGUxoD3L/vffxpidxph9wFMhaocI3i+xaICjNWfEOlsH4M9v3ApYZ4w5iPXI3D7g+EDN4ZxbLkREgElYo4QBxpi8WNIfQIKvjyjW3QNrgfMXEdkF3AkMEJFviun7dCAJ6/4u6x4P59xwMIBEu3b7b7fd1htMdN0v5Z3cjoYNeBNrtbQm0JXIekEkYK2APoo1iky22xrZOgbYbY9RdIX0X8AirBXSVPuP5VtdrfC5FdD/IrAcqBXUHrX6gZOxFrJqAfFAP+AoVq6QaNZdAzglYHsCeNfuty1wGOsRuSbwOkW9AUq8x8M5txzaT7K/Z9/9PdT+zs+Kdu32df4BfG3fO/WwvBMeirb7xXNjWkEjUh+Yad8QvwDXRrDvcVi/rIHbOHtfH6wFoiysFeSUgPOSsPJcHAZ2A3cEXbfC55ZD+2m23mysRybfNjSa9ds3/iLgkH2NtcBIJ/qOxPcedO+8HvD+Wvv+PQrMAuqHeo+Hc245vvOvsR6hD2H9aPeNBe32dRKBF2ztu4AJQHK03S+aC0JRFMUjYnEOWFEUpVKgBlhRFMUj1AAriqJ4hBpgRVEUj1ADrCiK4hFqgBVFUTxCDbCieICIpIiIEZF0r7Uo3qEGWCkVEWksIk+LyEY7OfceEVkmIreKSK2A47bYBsXYx22zE2tfUcw1TcB2RKwE91dH9pN5zjagCfAtgIj0sL+Pht7KUiKJGmClREQkBfgGuAS4D+gE9MIKqe0NXBl0yj+wjEprrNDhLcD7IvLvYi4/0j62M1bavndE5DdOf4bSEJFqkewvEGMlFdpljMn3SoMSBYQTWqlb5d6AT7FGajVL2C8Br7cAdxZzzI1Y4c89A9oMMDDgfSJW6OmjJfSTYp9zLfA5Vij1egKSX9vHtQE+xgqf3YOVU+CUgP1TgI+w0ixuB/aU8tnPBxbYujKA+UBTe98lWLkFDgIHgDnA2eXRG3BMesDrwG1KKH3pFtubjoCVYrHrdPUDnjdW4ukTMLaFKINJWMZjQEkHGCsjWz6WIS6Nx7Fi+jsA/wNmiUgzW28TYDHwHdAFK2a/FvCBnX7TR3cgDcuw9S6uExFpD3wG/ISVEOZ8rIoWCfYhNYFn7H56YBnoD4sZUZeoN4htHP9+2mI9GdxWzr6UWMTrXwDdonMDzsMaif0uqH07x5P4vBjQvoViRsD2vuXAJwHv/SNgrAQm99ptl5Zwfoq9//8C2uKw0hc+bL//BzA/6Lx69nld7PdTgL3YteVK+ezTCMhyFcJ3VRMoALqVQ6/vmHT7fQ/7fcPy9KVbbG86AlbKi6/CwFdYKflCQTgxN+trIpIJHAPuwDLen5ZxnS98L4wxhcCXWNMOYBXovEiOV/DNxBpZgpWz1cd3xpicMvrpiDXlUPyHEWklIm/YlXZ9ma/igBbl0BsS5ehLiUESyj5EqaL8hGU0UwMbjTGbAezabGVi1+dqjWWwA7kLmA0cNsbsCVutZZQ+xkp6HszugNfFTqcEIWXs/xCrAOMo+9984HvAjWmBSPalRBgdASvFYozZj1VKfXSgu1kFGIGV3PvdoPZdxpifyml8z/e9sCt7dAF+sJu+wZo/3WpfN3Arb0mbb7C8PU5ARBpglRx/xBgzzxjzA1bdr+IGM6XpDSbX/je+gn0pMYgaYKU0bsa6R1aKyBARaSMirUVkCFa5lYKg42uLyCkicqqIXCAiTwPPA88ZYxY5oOfPIjJQRM7CWpg6DfiPve95oC7wloicJyKni0gfEZkoIuUt6jge6Gif215EzhKRESLSAmtBcR8wUkTOEJHuWFVGinMnK01vMFuxnjguF5FG9o9eefpSYhGvJ6F1i+4Nq5TOs1hTEjlYi29fA/cAtQOO28JxF6ocrMW6mcCVxVyziBtaCBpS7HOGAsuw3Lo2ELRoB5yJNdI+iFWxYAPwb6CavX8K8FGIfXbD8qrIwqqqMA9oYu/rheVtkW3/28/+XoaHqpegRTi77T6sMjaFHHdDK7Uv3WJ704oYStRjB4RsBjobY1Z4q6ZsYk2v4h06BaEoiuIRaoAVRVE8QqcgFEVRPEJHwIqiKB6hBlhRFMUj1AAriqJ4hBpgRVEUj1ADrCiK4hH/D70fQuclzaZ8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 360x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
"plt.axis([0, 60000, 0, 10])\n",
"position_text = {\n",
" \"Hungary\": (5000, 1),\n",
" \"Korea\": (18000, 1.7),\n",
" \"France\": (29000, 2.4),\n",
" \"Australia\": (40000, 3.0),\n",
" \"United States\": (52000, 3.8),\n",
"}\n",
"for country, pos_text in position_text.items():\n",
" pos_data_x, pos_data_y = sample_data.loc[country]\n",
" country = \"U.S.\" if country == \"United States\" else country\n",
" plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
" arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
" plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
"save_fig('money_happy_scatterplot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"sample_data.to_csv(os.path.join(\"datasets\", \"lifesat\", \"lifesat.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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</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>Hungary</th>\n",
" <td>12239.894</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Korea</th>\n",
" <td>27195.197</td>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>37675.006</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>50961.865</td>\n",
" <td>7.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States</th>\n",
" <td>55805.204</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GDP per capita Life satisfaction\n",
"Country \n",
"Hungary 12239.894 4.9\n",
"Korea 27195.197 5.8\n",
"France 37675.006 6.5\n",
"Australia 50961.865 7.3\n",
"United States 55805.204 7.2"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_data.loc[list(position_text.keys())]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure tweaking_model_params_plot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
"plt.axis([0, 60000, 0, 10])\n",
"X=np.linspace(0, 60000, 1000)\n",
"plt.plot(X, 2*X/100000, \"r\")\n",
"plt.text(40000, 2.7, r\"$\\theta_0 = 0$\", fontsize=14, color=\"r\")\n",
"plt.text(40000, 1.8, r\"$\\theta_1 = 2 \\times 10^{-5}$\", fontsize=14, color=\"r\")\n",
"plt.plot(X, 8 - 5*X/100000, \"g\")\n",
"plt.text(5000, 9.1, r\"$\\theta_0 = 8$\", fontsize=14, color=\"g\")\n",
"plt.text(5000, 8.2, r\"$\\theta_1 = -5 \\times 10^{-5}$\", fontsize=14, color=\"g\")\n",
"plt.plot(X, 4 + 5*X/100000, \"b\")\n",
"plt.text(5000, 3.5, r\"$\\theta_0 = 4$\", fontsize=14, color=\"b\")\n",
"plt.text(5000, 2.6, r\"$\\theta_1 = 5 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
"save_fig('tweaking_model_params_plot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4.853052800266436, 4.911544589158483e-05)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import linear_model\n",
"lin1 = linear_model.LinearRegression()\n",
"Xsample = np.c_[sample_data[\"GDP per capita\"]]\n",
"ysample = np.c_[sample_data[\"Life satisfaction\"]]\n",
"lin1.fit(Xsample, ysample)\n",
"t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
"t0, t1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure best_fit_model_plot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3))\n",
"plt.axis([0, 60000, 0, 10])\n",
"X=np.linspace(0, 60000, 1000)\n",
"plt.plot(X, t0 + t1*X, \"b\")\n",
"plt.text(5000, 3.1, r\"$\\theta_0 = 4.85$\", fontsize=14, color=\"b\")\n",
"plt.text(5000, 2.2, r\"$\\theta_1 = 4.91 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
"save_fig('best_fit_model_plot')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22587.49\n"
]
},
{
"data": {
"text/plain": [
"5.96244744318815"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cyprus_gdp_per_capita = gdp_per_capita.loc[\"Cyprus\"][\"GDP per capita\"]\n",
"print(cyprus_gdp_per_capita)\n",
"cyprus_predicted_life_satisfaction = lin1.predict(cyprus_gdp_per_capita)[0][0]\n",
"cyprus_predicted_life_satisfaction"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure cyprus_prediction_plot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(5,3), s=1)\n",
"X=np.linspace(0, 60000, 1000)\n",
"plt.plot(X, t0 + t1*X, \"b\")\n",
"plt.axis([0, 60000, 0, 10])\n",
"plt.text(5000, 7.5, r\"$\\theta_0 = 4.85$\", fontsize=14, color=\"b\")\n",
"plt.text(5000, 6.6, r\"$\\theta_1 = 4.91 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
"plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita], [0, cyprus_predicted_life_satisfaction], \"r--\")\n",
"plt.text(25000, 5.0, r\"Prediction = 5.96\", fontsize=14, color=\"b\")\n",
"plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
"save_fig('cyprus_prediction_plot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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</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>Portugal</th>\n",
" <td>19121.592</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovenia</th>\n",
" <td>20732.482</td>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spain</th>\n",
" <td>25864.721</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GDP per capita Life satisfaction\n",
"Country \n",
"Portugal 19121.592 5.1\n",
"Slovenia 20732.482 5.7\n",
"Spain 25864.721 6.5"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_data[7:10]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.766666666666667"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(5.1+5.7+6.5)/3"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"backup = oecd_bli, gdp_per_capita\n",
"\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]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[5.96242338]]\n"
]
}
],
"source": [
"# Code example\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn\n",
"\n",
"# Load the data\n",
"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
"gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
" encoding='latin1', na_values=\"n/a\")\n",
"\n",
"# 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\"]]\n",
"\n",
"# Visualize the data\n",
"country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction')\n",
"plt.show()\n",
"\n",
"# Select a linear model\n",
"model = sklearn.linear_model.LinearRegression()\n",
"\n",
"# Train the model\n",
"model.fit(X, y)\n",
"\n",
"# Make a prediction for Cyprus\n",
"X_new = [[22587]] # Cyprus' GDP per capita\n",
"print(model.predict(X_new)) # outputs [[ 5.96242338]]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"oecd_bli, gdp_per_capita = backup"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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</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>Brazil</th>\n",
" <td>8669.998</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mexico</th>\n",
" <td>9009.280</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chile</th>\n",
" <td>13340.905</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czech Republic</th>\n",
" <td>17256.918</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>74822.106</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Switzerland</th>\n",
" <td>80675.308</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Luxembourg</th>\n",
" <td>101994.093</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GDP per capita Life satisfaction\n",
"Country \n",
"Brazil 8669.998 7.0\n",
"Mexico 9009.280 6.7\n",
"Chile 13340.905 6.7\n",
"Czech Republic 17256.918 6.5\n",
"Norway 74822.106 7.4\n",
"Switzerland 80675.308 7.5\n",
"Luxembourg 101994.093 6.9"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"position_text2 = {\n",
" \"Brazil\": (1000, 9.0),\n",
" \"Mexico\": (11000, 9.0),\n",
" \"Chile\": (25000, 9.0),\n",
" \"Czech Republic\": (35000, 9.0),\n",
" \"Norway\": (60000, 3),\n",
" \"Switzerland\": (72000, 3.0),\n",
" \"Luxembourg\": (90000, 3.0),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure representative_training_data_scatterplot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_data.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
"plt.axis([0, 110000, 0, 10])\n",
"\n",
"for country, pos_text in position_text2.items():\n",
" pos_data_x, pos_data_y = missing_data.loc[country]\n",
" plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
" arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
" plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
"\n",
"X=np.linspace(0, 110000, 1000)\n",
"plt.plot(X, t0 + t1*X, \"b:\")\n",
"\n",
"lin_reg_full = linear_model.LinearRegression()\n",
"Xfull = np.c_[full_country_stats[\"GDP per capita\"]]\n",
"yfull = np.c_[full_country_stats[\"Life satisfaction\"]]\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, 110000, 1000)\n",
"plt.plot(X, t0full + t1full * X, \"k\")\n",
"\n",
"save_fig('representative_training_data_scatterplot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:116: RuntimeWarning: overflow encountered in multiply\n",
" x = um.multiply(x, x, out=x)\n",
"/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:117: RuntimeWarning: overflow encountered in reduce\n",
" ret = umr_sum(x, axis, dtype, out, keepdims)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure overfitting_model_plot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"full_country_stats.plot(kind='scatter', x=\"GDP per capita\", y='Life satisfaction', figsize=(8,3))\n",
"plt.axis([0, 110000, 0, 10])\n",
"\n",
"from sklearn import preprocessing\n",
"from sklearn import pipeline\n",
"\n",
"poly = preprocessing.PolynomialFeatures(degree=60, include_bias=False)\n",
"scaler = preprocessing.StandardScaler()\n",
"lin_reg2 = linear_model.LinearRegression()\n",
"\n",
"pipeline_reg = pipeline.Pipeline([('poly', poly), ('scal', scaler), ('lin', lin_reg2)])\n",
"pipeline_reg.fit(Xfull, yfull)\n",
"curve = pipeline_reg.predict(X[:, np.newaxis])\n",
"plt.plot(X, curve)\n",
"save_fig('overfitting_model_plot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Country\n",
"New Zealand 7.3\n",
"Sweden 7.2\n",
"Norway 7.4\n",
"Switzerland 7.5\n",
"Name: Life satisfaction, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_country_stats.loc[[c for c in full_country_stats.index if \"W\" in c.upper()]][\"Life satisfaction\"]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>Subject Descriptor</th>\n",
" <th>Units</th>\n",
" <th>Scale</th>\n",
" <th>Country/Series-specific Notes</th>\n",
" <th>GDP per capita</th>\n",
" <th>Estimates Start After</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Botswana</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>6040.957</td>\n",
" <td>2008.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>29363.027</td>\n",
" <td>2014.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malawi</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>354.275</td>\n",
" <td>2011.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>37044.891</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>Gross domestic product per capita, current prices</td>\n",
" <td>U.S. dollars</td>\n",
" <td>Units</td>\n",
" <td>See notes for: Gross domestic product, curren...</td>\n",
" <td>74822.106</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Subject Descriptor Units \\\n",
"Country \n",
"Botswana Gross domestic product per capita, current prices U.S. dollars \n",
"Kuwait Gross domestic product per capita, current prices U.S. dollars \n",
"Malawi Gross domestic product per capita, current prices U.S. dollars \n",
"New Zealand Gross domestic product per capita, current prices U.S. dollars \n",
"Norway Gross domestic product per capita, current prices U.S. dollars \n",
"\n",
" Scale Country/Series-specific Notes \\\n",
"Country \n",
"Botswana Units See notes for: Gross domestic product, curren... \n",
"Kuwait Units See notes for: Gross domestic product, curren... \n",
"Malawi Units See notes for: Gross domestic product, curren... \n",
"New Zealand Units See notes for: Gross domestic product, curren... \n",
"Norway Units See notes for: Gross domestic product, curren... \n",
"\n",
" GDP per capita Estimates Start After \n",
"Country \n",
"Botswana 6040.957 2008.0 \n",
"Kuwait 29363.027 2014.0 \n",
"Malawi 354.275 2011.0 \n",
"New Zealand 37044.891 2015.0 \n",
"Norway 74822.106 2015.0 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdp_per_capita.loc[[c for c in gdp_per_capita.index if \"W\" in c.upper()]].head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving figure ridge_model_plot\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,3))\n",
"\n",
"plt.xlabel(\"GDP per capita\")\n",
"plt.ylabel('Life satisfaction')\n",
"\n",
"plt.plot(list(sample_data[\"GDP per capita\"]), list(sample_data[\"Life satisfaction\"]), \"bo\")\n",
"plt.plot(list(missing_data[\"GDP per capita\"]), list(missing_data[\"Life satisfaction\"]), \"rs\")\n",
"\n",
"X = np.linspace(0, 110000, 1000)\n",
"plt.plot(X, t0full + t1full * X, \"r--\", label=\"Linear model on all data\")\n",
"plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
"\n",
"ridge = linear_model.Ridge(alpha=10**9.5)\n",
"Xsample = np.c_[sample_data[\"GDP per capita\"]]\n",
"ysample = np.c_[sample_data[\"Life satisfaction\"]]\n",
"ridge.fit(Xsample, ysample)\n",
"t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
"plt.plot(X, t0ridge + t1ridge * X, \"b\", label=\"Regularized linear model on partial data\")\n",
"\n",
"plt.legend(loc=\"lower right\")\n",
"plt.axis([0, 110000, 0, 10])\n",
"save_fig('ridge_model_plot')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"backup = oecd_bli, gdp_per_capita\n",
"\n",
"def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
" return sample_data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Replace this linear model:\n",
"model = sklearn.linear_model.LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# with this k-neighbors regression model:\n",
"model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[5.76666667]]\n"
]
}
],
"source": [
"X = np.c_[country_stats[\"GDP per capita\"]]\n",
"y = np.c_[country_stats[\"Life satisfaction\"]]\n",
"\n",
"# Train the model\n",
"model.fit(X, y)\n",
"\n",
"# Make a prediction for Cyprus\n",
"X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n",
"print(model.predict(X_new)) # outputs [[ 5.76666667]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"nav_menu": {},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "195px"
},
"toc_section_display": "block",
"toc_window_display": true
},
"toc_position": {
"height": "616px",
"left": "0px",
"right": "20px",
"top": "106px",
"width": "213px"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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