This page shows an interesting bar chart.

Here is the Python code [#1]
import matplotlib.pyplot as plt import rsPlot import numpy as np barWidth1 = 0.20 barGap1 =0.05 barWidth2 = barWidth1 + barGap1 groups1 = ["One", "Two", "Both"] edgecolor1 = "#000000" bars1 = [33.3, 71.4, 60.0] bars2 = [40.0, 75.0, 55.6] r1 = np.arange(len(bars1)) r2 = [x + barWidth2 for x in r1] bars0 = [ [r1,bars1, "A", "#9999FF"], [r2,bars2, "B", "#FF9999"], ] plot1 = rsPlot.plot("plot51-1",1,0,72,640,480) for r4,bars4,label4,color4 in bars0: plt.bar(r4, bars4, color=color4, width=barWidth1, edgecolor=edgecolor1, label=label4) plt.xticks([r1 + barWidth1 for r1 in range(len(bars1))], groups1) plt.legend() plot1.save()

Here is the output of the Python code.
(module rsPlot imported) ./plot51-1-01.png : 550 x 414 , 12,132 bytes

• A = player A batting average , team A winning percentage
• B = player B batting average , team B winning percentage
• one = first half of season (who did best?)
• two = second half of season (who did best?)
• both = entire season (who did best?)

• A = company A customer satisfaction
• B = company B customer satisfaction
• one = city 1 (who did best?)
• two = city 2 (who did best?)
• both = overall (who did best?)