corr_map.py

    def heatmap(x, y, size):
        fig, ax = plt.subplots()
    
        # Mapping from column names to integer coordinates
        x_labels = [v for v in sorted(x.unique())]
        y_labels = [v for v in sorted(y.unique())]
        x_to_num = {p[1]:p[0] for p in enumerate(x_labels)} 
        y_to_num = {p[1]:p[0] for p in enumerate(y_labels)} 
    
        size_scale = 500
        ax.scatter(
            x=x.map(x_to_num), # Use mapping for x
            y=y.map(y_to_num), # Use mapping for y
            s=size * size_scale, # Vector of square sizes, proportional to size parameter
            marker='s' # Use square as scatterplot marker
        )
    
        # Show column labels on the axes
        ax.set_xticks([x_to_num[v] for v in x_labels])
        ax.set_xticklabels(x_labels, rotation=45, horizontalalignment='right')
        ax.set_yticks([y_to_num[v] for v in y_labels])
        ax.set_yticklabels(y_labels)
    
    data = pd.read_csv('https://raw.githubusercontent.com/drazenz/heatmap/master/autos.clean.csv')
    columns = ['bore', 'stroke', 'compression-ratio', 'horsepower', 'city-mpg', 'price'] 
    corr = data[columns].corr()
    corr = pd.melt(corr.reset_index(), id_vars='index') # Unpivot the dataframe, so we can get pair of arrays for x and y
    corr.columns = ['x', 'y', 'value']
    heatmap(
        x=corr['x'],
        y=corr['y'],
        size=corr['value'].abs()
    )