Nicer function to plot Pearson scores using Pandas and seaborn. Updated in 2022 for better aesthetics.

daves_pearson.py

def daves_pearson(corr, threashold = False, empty_dimensions = True, title = False):
    """ 
        Based on http://seaborn.pydata.org/examples/many_pairwise_correlations.html 
        
        Parameters
        -----------------------
        corr : Pandas Pearson coorelation matrix object
        threashold : Threashold filter for absolute score value.  Useful to suppress display of 
            non-correlated values.
        empty_dimensions : Removes features that don't meet threashold.
        title : Plot title.
    
    """
    
    if threashold:
        corr = corr[corr.abs() >= threashold]
        
    if not empty_dimensions:
        # drop dimensions with nothing in them after threashold filter
        # and after removing matrix identity
        np.fill_diagonal(corr.values, np.NAN)
        corr = corr.dropna(how = "all", axis = 0) # columns
        corr = corr.dropna(how = "all", axis = 1) # rows
        
    # Generate a mask for the upper triangle
    mask = np.zeros_like(corr, dtype=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.color_palette("vlag", as_cmap=True)

    # Draw the heatmap with the mask and correct aspect ratio
    sns.heatmap(corr, mask=mask, vmax=.3, annot=True, cmap = cmap,
                square = True,
                linewidths = 1, linecolor='lightgrey', cbar_kws={"shrink": .5}, ax=ax)
    
    ax.axhline(y=0, color='k',linewidth=1)
    ax.axhline(y=corr.shape[1], color = 'k',linewidth=3)
    ax.axvline(x=0, color='k',linewidth = 1)
    ax.axvline(x=corr.shape[0], color = 'k',linewidth=3)
    
    if title:
        ax.set_title(title)
    
    plt.xticks(rotation=45)
    plt.yticks(rotation=0)
    
daves_pearson(corr_all, threashold = .2, empty_dimensions = False, title = "Test")

You can add the cmap back in if you like.