# Sample plots in Matplotlib

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

# Line Plot

Here's how to create a line plot with text labels using plot() (opens new window).

Simple Plot

# Multiple subplots in one figure

Multiple axes (i.e. subplots) are created with the subplot() (opens new window) function:


# Images

Matplotlib can display images (assuming equally spaced horizontal dimensions) using the imshow() (opens new window) function.

Example of using imshow() (opens new window) to display a CT scan

# Contouring and pseudocolor

The pcolormesh() (opens new window) function can make a colored representation of a two-dimensional array, even if the horizontal dimensions are unevenly spaced. The contour() (opens new window) function is another way to represent the same data:

Example comparing pcolormesh() (opens new window) and contour() (opens new window) for plotting two-dimensional data

# Histograms

The hist() (opens new window) function automatically generates histograms and returns the bin counts or probabilities:

Histogram Features

# Paths

You can add arbitrary paths in Matplotlib using the matplotlib.path (opens new window) module:

Path Patch

# Three-dimensional plotting

The mplot3d toolkit (see Getting started (opens new window) and 3D plotting (opens new window)) 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.

# Streamplot

The streamplot() (opens new window) 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.

Streamplot with various plotting options.

This feature complements the quiver() (opens new window) function for plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the streamplot function.

# Ellipses

In support of the Phoenix (opens new window) 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 (opens new window)), which are insensitive to zoom level.

Ellipse Demo

# Bar charts

Use the bar() (opens new window) function to make bar charts, which includes customizations such as error bars:

Barchart Demo

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

# Pie charts

The pie() (opens new window) 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.

Pie Features

# Tables

The table() (opens new window) function adds a text table to an axes.

Table Demo

# Scatter plots

The scatter() (opens new window) 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.

Scatter Demo2

# GUI widgets

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 (opens new window) and the widget examples (opens new window).

Slider and radio-button GUI.

# Filled curves

The fill() (opens new window) function lets you plot filled curves and polygons:


Thanks to Andrew Straw for adding this function.

# Date handling

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


See matplotlib.ticker (opens new window) and matplotlib.dates (opens new window) for details and usage.

# Log plots

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

Log Demo

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

# Polar plots

The polar() (opens new window) function generates polar plots.

Polar Demo

# Legends

The legend() (opens new window) 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 (opens new window) and the DejaVu, BaKoMa computer modern, or STIX (opens new window) fonts. See the matplotlib.mathtext (opens new window) module for additional details.

Mathtext Examples

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 (opens new window).

# 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.

Tex Demo


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


The lower axes uses specgram() (opens new window) 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 xkcd.


# Subplot example

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

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])


# Download