.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_gallery_statistics_hist.py:
==========
Histograms
==========
Demonstrates how to plot histograms with matplotlib.
.. code-block:: default
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
# Fixing random state for reproducibility
np.random.seed(19680801)
Generate data and plot a simple histogram
-----------------------------------------
To generate a 1D histogram we only need a single vector of numbers. For a 2D
histogram we'll need a second vector. We'll generate both below, and show
the histogram for each vector.
.. code-block:: default
N_points = 100000
n_bins = 20
# Generate a normal distribution, center at x=0 and y=5
x = np.random.randn(N_points)
y = .4 * x + np.random.randn(100000) + 5
fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
# We can set the number of bins with the `bins` kwarg
axs[0].hist(x, bins=n_bins)
axs[1].hist(y, bins=n_bins)
.. image:: /gallery/statistics/images/sphx_glr_hist_001.png
:class: sphx-glr-single-img
Updating histogram colors
-------------------------
The histogram method returns (among other things) a `patches` object. This
gives us access to the properties of the objects drawn. Using this, we can
edit the histogram to our liking. Let's change the color of each bar
based on its y value.
.. code-block:: default
fig, axs = plt.subplots(1, 2, tight_layout=True)
# N is the count in each bin, bins is the lower-limit of the bin
N, bins, patches = axs[0].hist(x, bins=n_bins)
# We'll color code by height, but you could use any scalar
fracs = N / N.max()
# we need to normalize the data to 0..1 for the full range of the colormap
norm = colors.Normalize(fracs.min(), fracs.max())
# Now, we'll loop through our objects and set the color of each accordingly
for thisfrac, thispatch in zip(fracs, patches):
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
# We can also normalize our inputs by the total number of counts
axs[1].hist(x, bins=n_bins, density=True)
# Now we format the y-axis to display percentage
axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
.. image:: /gallery/statistics/images/sphx_glr_hist_002.png
:class: sphx-glr-single-img
Plot a 2D histogram
-------------------
To plot a 2D histogram, one only needs two vectors of the same length,
corresponding to each axis of the histogram.
.. code-block:: default
fig, ax = plt.subplots(tight_layout=True)
hist = ax.hist2d(x, y)
.. image:: /gallery/statistics/images/sphx_glr_hist_003.png
:class: sphx-glr-single-img
Customizing your histogram
--------------------------
Customizing a 2D histogram is similar to the 1D case, you can control
visual components such as the bin size or color normalization.
.. code-block:: default
fig, axs = plt.subplots(3, 1, figsize=(5, 15), sharex=True, sharey=True,
tight_layout=True)
# We can increase the number of bins on each axis
axs[0].hist2d(x, y, bins=40)
# As well as define normalization of the colors
axs[1].hist2d(x, y, bins=40, norm=colors.LogNorm())
# We can also define custom numbers of bins for each axis
axs[2].hist2d(x, y, bins=(80, 10), norm=colors.LogNorm())
plt.show()
.. image:: /gallery/statistics/images/sphx_glr_hist_004.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 1.061 seconds)
.. _sphx_glr_download_gallery_statistics_hist.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download
:download:`Download Python source code: hist.py `
.. container:: sphx-glr-download
:download:`Download Jupyter notebook: hist.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
Keywords: matplotlib code example, codex, python plot, pyplot
`Gallery generated by Sphinx-Gallery
`_