.. _statistics-histogram_demo_features:

statistics example code: histogram_demo_features.py
===================================================



.. plot:: /home/tcaswell/src/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`)