Required CSV
prepare_country_stats()
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def prepare_country_stats(oecd_bli, gdp_per_capita): oecd_bli = oecd_bli[oecd_bli["INEQUALITY"] == "TOT"] oecd_bli = oecd_bli.pivot( index="Country", columns="Indicator", values="Value") gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True) gdp_per_capita.set_index("Country", inplace=True) full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True) full_country_stats.sort_values(by="GDP per capita", inplace=True) remove_indices = [0, 1, 6, 8, 33, 34, 35] keep_indices = list(set(range(36)) - set(remove_indices)) return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices] |
Completed code for example 1.1
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import pandas as pd import matplotlib import matplotlib.pyplot as plt import sklearn.linear_model import sklearn.neighbors import numpy as np def prepare_country_stats(oecd_bli, gdp_per_capita): oecd_bli = oecd_bli[oecd_bli["INEQUALITY"] == "TOT"] oecd_bli = oecd_bli.pivot( index="Country", columns="Indicator", values="Value") gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True) gdp_per_capita.set_index("Country", inplace=True) full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True) full_country_stats.sort_values(by="GDP per capita", inplace=True) remove_indices = [0, 1, 6, 8, 33, 34, 35] keep_indices = list(set(range(36)) - set(remove_indices)) return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices] if __name__ == "__main__": oecd_bli = pd.read_csv("oecd_bli_2015.csv", thousands=',') gdp_per_capita = pd.read_csv( "gdp_per_capita.csv", thousands=',', delimiter='\t', encoding='latin1', na_values='n/a') country_stats = prepare_country_stats(oecd_bli, gdp_per_capita) country_stats.plot(kind='scatter', x="GDP per capita", y="Life satisfaction") X = np.c_[country_stats["GDP per capita"]] Y = np.c_[country_stats["Life satisfaction"]] model = sklearn.linear_model.LinearRegression() # model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3) model.fit(X, Y) for x in range(10, 50): plot_x = 1000 * x plot_y = float(model.predict([[plot_x]])) plt.scatter(plot_x, plot_y, s=10, color='r') plt.show() |
Plotting using sklearn.linear_model.LinearRegression():
Plotting using sklearn.neighbors.KNeighborsRegressor()with sampling n = 3:
