Sample plots in Matplotlib
Here you'll find a host of example plots with the code that generated them.
Here's how to create a line plot with text labels using
Multiple subplots in one figure
Multiple axes (i.e. subplots) are created with the
Matplotlib can display images (assuming equally spaced horizontal dimensions) using the
Example of using
imshow() to display a CT scan
Contouring and pseudocolor
pcolormesh() function can make a colored representation of a two-dimensional array, even if the horizontal dimensions are unevenly spaced. The
contour() function is another way to represent the same data:
hist() function automatically generates histograms and returns the bin counts or probabilities:
You can add arbitrary paths in Matplotlib using the
Thanks to John Porter, Jonathon Taylor, Reinier Heeres, and Ben Root for the
mplot3d toolkit. This toolkit is included with all standard Matplotlib installs.
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.
bar() function to make bar charts, which includes customizations such as error bars:
pie() function 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.
table() function adds a text table to an axes.
scatter() function 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.
fill() function 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.
Thanks to Andrew Straw, Darren Dale and Gregory Lielens for contributions log-scaling infrastructure.
polar() function generates polar plots.
legend() function automatically generates figure legends, with MATLAB-compatible legend-placement functions.
Thanks to Charles Twardy for input on the legend function.
TeX-notation for text objects
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.
Native TeX rendering
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:
XKCD-style sketch plots
Just for fun, Matplotlib supports plotting in the style of
Many plot types can be combined in one figure to create powerful and flexible representations of data.
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) axs[1, 0].scatter(data, data) axs[0, 1].plot(data, data) axs[1, 1].hist2d(data, data) plt.show()