step_4.py

n_colors = 256 # Use 256 colors for the diverging color palette
palette = sns.diverging_palette(20, 220, n=n_colors) # Create the palette
color_min, color_max = [-1, 1] # Range of values that will be mapped to the palette, i.e. min and max possible correlation

def value_to_color(val):
    val_position = float((val - color_min)) / (color_max - color_min) # position of value in the input range, relative to the length of the input range
    ind = int(val_position * (n_colors - 1)) # target index in the color palette
    return palette[ind]

ax.scatter(
    x=x.map(x_to_num),
    y=y.map(y_to_num),
    s=size * size_scale,
    c=color.apply(value_to_color), # Vector of square color values, mapped to color palette
    marker='s'
)

You can replace color with corr['value'] since this is the DataSeries we want to apply the colors to.