.. _api-histogram_demo: api example code: histogram_demo.py =================================== .. plot:: /home/mdboom/Work/builds/matplotlib/doc/mpl_examples/api/histogram_demo.py :: """ Make a histogram of normally distributed random numbers and plot the analytic PDF over it """ import numpy as np import matplotlib.pyplot as plt import matplotlib.mlab as mlab mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) fig = plt.figure() ax = fig.add_subplot(111) # the histogram of the data n, bins, patches = ax.hist(x, 50, normed=1, facecolor='green', alpha=0.75) # hist uses np.histogram under the hood to create 'n' and 'bins'. # np.histogram returns the bin edges, so there will be 50 probability # density values in n, 51 bin edges in bins and 50 patches. To get # everything lined up, we'll compute the bin centers bincenters = 0.5*(bins[1:]+bins[:-1]) # add a 'best fit' line for the normal PDF y = mlab.normpdf( bincenters, mu, sigma) l = ax.plot(bincenters, y, 'r--', linewidth=1) ax.set_xlabel('Smarts') ax.set_ylabel('Probability') #ax.set_title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$') ax.set_xlim(40, 160) ax.set_ylim(0, 0.03) ax.grid(True) plt.show() Keywords: python, matplotlib, pylab, example, codex (see :ref:how-to-search-examples)