.. _sphx_glr_gallery_statistics_histogram_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 .. image:: /gallery/statistics/images/sphx_glr_histogram_features_001.png :align: center .. code-block:: python import numpy as np 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, density=1) # add a 'best fit' line y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2)) 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() .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: histogram_features.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: histogram_features.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_