step_1.py

# Step 1 - Make a scatter plot with square markers, set column names as labels

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()
)