.. _statistics-boxplot_vs_violin_demo: statistics example code: boxplot_vs_violin_demo.py ================================================== .. plot:: /home/tcaswell/src/p/matplotlib/doc/mpl_examples/statistics/boxplot_vs_violin_demo.py :: """ =================================== Box plot vs. violin plot comparison =================================== Note that although violin plots are closely related to Tukey's (1977) box plots, they add useful information such as the distribution of the sample data (density trace). By default, box plots show data points outside 1.5 * the inter-quartile range as outliers above or below the whiskers whereas violin plots show the whole range of the data. A good general reference on boxplots and their history can be found here: http://vita.had.co.nz/papers/boxplots.pdf Violin plots require matplotlib >= 1.4. For more information on violin plots, the scikit-learn docs have a great section: http://scikit-learn.org/stable/modules/density.html """ import matplotlib.pyplot as plt import numpy as np fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(9, 4)) # generate some random test data all_data = [np.random.normal(0, std, 100) for std in range(6, 10)] # plot violin plot axes[0].violinplot(all_data, showmeans=False, showmedians=True) axes[0].set_title('violin plot') # plot box plot axes[1].boxplot(all_data) axes[1].set_title('box plot') # adding horizontal grid lines for ax in axes: ax.yaxis.grid(True) ax.set_xticks([y+1 for y in range(len(all_data))]) ax.set_xlabel('xlabel') ax.set_ylabel('ylabel') # add x-tick labels plt.setp(axes, xticks=[y+1 for y in range(len(all_data))], xticklabels=['x1', 'x2', 'x3', 'x4']) plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)