Generating a heat map of correlations

This is an example of generating a heat map for showing correlations between variables. The correlation between variables is obtained as the Pearson correlation coefficient.

The heat map is generated from a pandas.core.frame.DataFrame and all pairs of variables (based on columns) are considered.

To better display the values of the correlation coefficient, the colors used for the annotation of the values in the plot can be selected with the parameter textcolors of the psynlig.heatmap.plot_heatmap() method (please see the documentation for more information).

from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_wine as load_data
from psynlig import plot_correlation_heatmap
plt.style.use('seaborn-talk')


data_set = load_data()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])

kwargs = {
    'text': {
        'fontsize': 'large',
    },
    'heatmap': {
        'vmin': -1,
        'vmax': 1,
        'cmap': 'viridis',
    },
    'figure': {
        'figsize': (14, 10)
    },
}

plot_correlation_heatmap(data, textcolors=['white', 'black'], **kwargs)
plt.show()