from data_parser import DataParser import numpy as np import matplotlib.pyplot as plt import pandas as pd parser = DataParser('europe.csv') matrix_non_standarized = np.matrix(parser.get_numerical_csv()) # Create a figure instance fig = plt.figure(1, figsize=(9, 6)) # Create an axes instance ax = fig.add_subplot(111) ## Custom x-axis labels ax.set_xticklabels([ 'Area', 'GDP', 'Inflation', 'Life.expect', 'Military', 'Pop.growth', 'Unemployment' ]) ## add patch_artist=True option to ax.boxplot() ## to get fill color bp = ax.boxplot(matrix_non_standarized, patch_artist=True) ## change outline color, fill color and linewidth of the boxes for box in bp['boxes']: # change outline color box.set(color='#7570b3', linewidth=2) # change fill color box.set(facecolor='#1b9e77')
'GDP': matrix_for_correlation[1], 'Inflation': matrix_for_correlation[2], 'Life.expect': matrix_for_correlation[3], 'Military': matrix_for_correlation[4], 'Pop.growth': matrix_for_correlation[5], 'Unemployment': matrix_for_correlation[6] } df = pd.DataFrame(matrix_for_correlation_with_keys, columns=[ 'Area', 'GDP', 'Inflation', 'Life.expect', 'Military', 'Pop.growth', 'Unemployment']) correlation_matrix = df.corr() # If you want to show heatmap: covariance_matrix = np.cov(np.array(parser.get_numerical_csv()).T) print('COV', covariance_matrix) autovals_corr, autovecs_corr = np.linalg.eig(correlation_matrix) print('AUTOVALS | AUTOVECS, COR') print(autovals_corr, autovecs_corr) autovals_cov, autovecs_cov = np.linalg.eig(covariance_matrix) print('AUTOVALS | AUTOVECS, COV') print(autovals_cov, autovecs_cov) # PCA n_components = 7 pca = PCA(n_components=n_components)