You are reading an old version of the documentation (v1.4.0). For the latest version see https://matplotlib.org/stable/

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

Multiple axes (i.e. subplots) are created with the
`subplot()` command:

(Source code, png, hires.png, pdf)

The `hist()` command automatically generates
histograms and returns the bin counts or probabilities:

(Source code, png, hires.png, pdf)

You can add arbitrary paths in matplotlib using the
`matplotlib.path` module:

(Source code, png, hires.png, pdf)

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

(Source code, png, hires.png, pdf)

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.

(Source code, png, hires.png, pdf)

Bar charts are simple to create using the `bar()`
command, which includes customizations such as error bars:

(Source code, png, hires.png, pdf)

It’s also simple to create stacked bars (bar_stacked.py), candlestick bars (finance_demo.py), and horizontal bar charts (barh_demo.py).

The `pie()` command allows you to easily 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.

(Source code, png, hires.png, pdf)

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.

(Source code, png, hires.png, pdf)

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.

(Source code, png, hires.png, pdf)

The `fill()` command lets you
plot filled curves and polygons:

(Source code, png, hires.png, pdf)

Thanks to Andrew Straw for adding this function.

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

(Source code, png, hires.png, pdf)

See `matplotlib.ticker` and `matplotlib.dates` for details and usage.

You can make sophisticated financial plots by combining the various plot functions, layout commands, and labeling tools provided by matplotlib. The following example emulates one of the financial plots in ChartDirector:

(Source code, png, hires.png, pdf)

Jeff Whitaker’s *Basemap* add-on toolkit makes it possible to plot data on many different map projections. This example shows how to plot contours, markers and text on an orthographic projection, with NASA’s “blue marble” satellite image as a background.

(Source code, png, hires.png, pdf)

The `semilogx()`,
`semilogy()` and
`loglog()` functions simplify the creation of
logarithmic plots.

(Source code, png, hires.png, pdf)

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.

(Source code, png, hires.png, pdf)

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 freetype2
and the 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.

(Source code, png, hires.png, pdf)

You can embed matplotlib into pygtk, wx, Tk, FLTK, or Qt applications. Here is a screenshot of an EEG viewer called pbrain, which is part of the NeuroImaging in Python suite NIPY.

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: