.. _statistics-histogram_demo_features: statistics example code: histogram_demo_features.py =================================================== .. plot:: /home/tcaswell/source/p/matplotlib/doc/mpl_examples/statistics/histogram_demo_features.py :: """ ========================================================= Demo of the histogram (hist) function with a few features ========================================================= In addition to the basic histogram, this demo shows a few optional features: * Setting the number of data bins * The normed flag, which normalizes bin heights so that the integral of the histogram is 1. The resulting histogram is an approximation of the probability density function. * Setting the face color of the bars * Setting the opacity (alpha value). 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.mlab as mlab import matplotlib.pyplot as plt np.random.seed(0) # example data mu = 100 # mean of distribution sigma = 15 # standard deviation of distribution x = mu + sigma * np.random.randn(437) num_bins = 50 fig, ax = plt.subplots() # the histogram of the data n, bins, patches = ax.hist(x, num_bins, normed=1) # add a 'best fit' line y = mlab.normpdf(bins, mu, sigma) ax.plot(bins, y, '--') ax.set_xlabel('Smarts') ax.set_ylabel('Probability density') ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$') # Tweak spacing to prevent clipping of ylabel fig.tight_layout() plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:how-to-search-examples)