heatmap.py

import seaborn as sns
corr = X.corr()

# randomly pick 30 of the interest columns to drop to make the visualization more readable

drops = np.random.choice(X.filter(regex=('interest_1?[0-9]{2}')).columns, 
                 size=30, replace=False) 
corr = corr.loc[~corr.index.isin(drops)]
corr.drop(columns=drops, inplace=True)

# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, annot=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})