.. _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`)