.. _statistics-multiple_histograms_side_by_side: statistics example code: multiple_histograms_side_by_side.py ============================================================ .. plot:: /home/tcaswell/src/p/matplotlib/doc/mpl_examples/statistics/multiple_histograms_side_by_side.py :: """ ======================================================= Demo of how to produce multiple histograms side by side ======================================================= This example plots horizontal histograms of different samples along a categorical x-axis. Additionally, the histograms are plotted to be symmetrical about their x-position, thus making them very similar to violin plots. To make this highly specialized plot, we can't use the standard ``hist`` method. Instead we use ``barh`` to draw the horizontal bars directly. The vertical positions and lengths of the bars are computed via the ``np.histogram`` function. The histograms for all the samples are computed using the same range (min and max values) and number of bins, so that the bins for each sample are in the same vertical positions. Selecting different bin counts and sizes can significantly affect the shape of a histogram. The Astropy docs have a great section on how to select these parameters: http://docs.astropy.org/en/stable/visualization/histogram.html """ import numpy as np import matplotlib.pyplot as plt np.random.seed(0) number_of_bins = 20 # An example of three data sets to compare number_of_data_points = 387 labels = ["A", "B", "C"] data_sets = [np.random.normal(0, 1, number_of_data_points), np.random.normal(6, 1, number_of_data_points), np.random.normal(-3, 1, number_of_data_points)] # Computed quantities to aid plotting hist_range = (np.min(data_sets), np.max(data_sets)) binned_data_sets = [ np.histogram(d, range=hist_range, bins=number_of_bins)[0] for d in data_sets ] binned_maximums = np.max(binned_data_sets, axis=1) x_locations = np.arange(0, sum(binned_maximums), np.max(binned_maximums)) # The bin_edges are the same for all of the histograms bin_edges = np.linspace(hist_range[0], hist_range[1], number_of_bins + 1) centers = 0.5 * (bin_edges + np.roll(bin_edges, 1))[:-1] heights = np.diff(bin_edges) # Cycle through and plot each histogram fig, ax = plt.subplots() for x_loc, binned_data in zip(x_locations, binned_data_sets): lefts = x_loc - 0.5 * binned_data ax.barh(centers, binned_data, height=heights, left=lefts) ax.set_xticks(x_locations) ax.set_xticklabels(labels) ax.set_ylabel("Data values") ax.set_xlabel("Data sets") plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)