stacked_plot_function.py

def percentage_stacked_plot(columns_to_plot, super_title):
    
    '''
    Prints a 100% stacked plot of the response variable for independent variable of the list columns_to_plot.

            Parameters:
                    columns_to_plot (list of string): Names of the variables to plot
                    super_title (string): Super title of the visualization

            Returns:
                    None
    '''
    
    number_of_columns = 2
    number_of_rows = math.ceil(len(columns_to_plot)/2)

    # create a figure
    fig = plt.figure(figsize=(12, 5 * number_of_rows)) 
    fig.suptitle(super_title, fontsize=22,  y=.95)
 

    # loop to each column name to create a subplot
    for index, column in enumerate(columns_to_plot, 1):

        # create the subplot
        ax = fig.add_subplot(number_of_rows, number_of_columns, index)

        # calculate the percentage of observations of the response variable for each group of the independent variable
        # 100% stacked bar plot
        prop_by_independent = pd.crosstab(df_telco[column], df_telco['Churn']).apply(lambda x: x/x.sum()*100, axis=1)

        prop_by_independent.plot(kind='bar', ax=ax, stacked=True,
                                 rot=0, color=['springgreen','salmon'])

        # set the legend in the upper right corner
        ax.legend(loc="upper right", bbox_to_anchor=(0.62, 0.5, 0.5, 0.5),
                  title='Churn', fancybox=True)

        # set title and labels
        ax.set_title('Proportion of observations by ' + column,
                     fontsize=16, loc='left')

        ax.tick_params(rotation='auto')

        # eliminate the frame from the plot
        spine_names = ('top', 'right', 'bottom', 'left')
        for spine_name in spine_names:
            ax.spines[spine_name].set_visible(False)