Hands-On Data Structures and Algorithms with Python - Second Edition

Multiple bar charts

In trying to visualize data, stacking a number of bars enables one to further understand how one piece of data or variable varies compared to another:

    data = [             [8., 57., 22., 10.],             [16., 7., 32., 40.],           ]     import numpy as np     x_values = np.arange(4)     plt.bar(x_values + 0.00, data[0], color='r', width=0.30)     plt.bar(x_values + 0.30, data[1], color='y', width=0.30)     plt.show() 

The y values for the first batch of data are [8., 57., 22., 10.]. The second batch is [16., 7., 32., 40.]. When the bars are plotted, 8 and 16 will occupy the same x position, side by side.

x_values = np.arange(4) generates the array with values [0, 1, 2, 3]. The first set of bars are drawn first at position x_values + 0.30. Thus, the first x values ...