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# 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(19680801)

    # 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()

Total running time of the script: ( 0 minutes 0.075 seconds)

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