You are reading an old version of the documentation (v2.1.0). For the latest version see https://matplotlib.org/stable/

Here you’ll find a host of example plots with the code that generated them.

The `hist()`

command automatically generates
histograms and returns the bin counts or probabilities:

The mplot3d toolkit (see Getting started and mplot3d toolkit) has support for simple 3d graphs including surface, wireframe, scatter, and bar charts.

Thanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for
the `mplot3d`

toolkit. This toolkit is included with all standard Matplotlib
installs.

The `streamplot()`

function plots the streamlines of
a vector field. In addition to simply plotting the streamlines, it allows you
to map the colors and/or line widths of streamlines to a separate parameter,
such as the speed or local intensity of the vector field.

This feature complements the `quiver()`

function for
plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the
streamplot function.

In support of the
Phoenix mission to
Mars (which used Matplotlib to display ground tracking of spacecraft),
Michael Droettboom built on work by Charlie Moad to provide an extremely
accurate 8-spline approximation to elliptical arcs (see
`Arc`

), which are insensitive to zoom level.

Use the `bar()`

command to make bar charts, which
includes customizations such as error bars:

You can also create stacked bars (bar_stacked.py), or horizontal bar charts (barh.py).

The `pie()`

command allows you to create pie
charts. Optional features include auto-labeling the percentage of area,
exploding one or more wedges from the center of the pie, and a shadow effect.
Take a close look at the attached code, which generates this figure in just
a few lines of code.

The `scatter()`

command makes a scatter plot
with (optional) size and color arguments. This example plots changes
in Google’s stock price, with marker sizes reflecting the
trading volume and colors varying with time. Here, the
alpha attribute is used to make semitransparent circle markers.

Matplotlib has basic GUI widgets that are independent of the graphical
user interface you are using, allowing you to write cross GUI figures
and widgets. See `matplotlib.widgets`

and the
widget examples.

The `fill()`

command lets you
plot filled curves and polygons:

Thanks to Andrew Straw for adding this function.

You can plot timeseries data with major and minor ticks and custom tick formatters for both.

See `matplotlib.ticker`

and `matplotlib.dates`

for details and usage.

The `semilogx()`

,
`semilogy()`

and
`loglog()`

functions simplify the creation of
logarithmic plots.

Thanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions log-scaling infrastructure.

The `legend()`

command automatically
generates figure legends, with MATLAB-compatible legend placement
commands.

Thanks to Charles Twardy for input on the legend command.

Below is a sampling of the many TeX expressions now supported by Matplotlib’s
internal mathtext engine. The mathtext module provides TeX style mathematical
expressions using FreeType
and the DejaVu, BaKoMa computer modern, or STIX
fonts. See the `matplotlib.mathtext`

module for additional details.

Matplotlib’s mathtext infrastructure is an independent implementation and does not require TeX or any external packages installed on your computer. See the tutorial at Writing mathematical expressions.

Although Matplotlib’s internal math rendering engine is quite
powerful, sometimes you need TeX. Matplotlib supports external TeX
rendering of strings with the *usetex* option.

You can embed Matplotlib into pygtk, wx, Tk, or Qt applications. Here is a screenshot of an EEG viewer called pbrain.

The lower axes uses `specgram()`

to plot the spectrogram of one of the EEG channels.

For examples of how to embed Matplotlib in different toolkits, see:

Matplotlib supports plotting in the style of `xkcd`

.

```
# An extra set of sample plots for the thumbnail image.
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
data = np.random.randn(2, 100)
fig, axs = plt.subplots(2, 2, figsize=(5, 5))
axs[0, 0].hist(data[0])
axs[1, 0].scatter(data[0], data[1])
axs[0, 1].plot(data[0], data[1])
axs[1, 1].hist2d(data[0], data[1])
plt.show()
```

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