matplotlib

Matplotlib

Screenshot of matplotlib plots and code
Original author(s)	John D. Hunter
Developer(s)	Michael Droettboom, et al.
Initial release	2003 (2003)[1]
Stable release	2.2.3 / 10 August 2018 (2018-08-10)[2]
Preview release	3.0.0rc2 / 28 August 2018 (2018-08-28)
Repository	https://github.com/matplotlib/matplotlib
Written in	Python
Operating system	Cross-platform
Type	Plotting
License	matplotlib license
Website	matplotlib.org

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural "pylab" interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.^[3] SciPy makes use of matplotlib.

Matplotlib was originally written by John D. Hunter, has an active development community,^[4] and is distributed under a BSD-style license. Michael Droettboom was nominated as matplotlib's lead developer shortly before John Hunter's death in August 2012,^[5] and further joined by Thomas Caswell^[6][7]

As of 23 June 2017, matplotlib 2.0.x supports Python versions 2.7 through 3.6. Matplotlib 1.2 is the first version of matplotlib to support Python 3.x. Matplotlib 1.4 is the last version of matplotlib to support Python 2.6.^[8]

Matplotlib has pledged to not support Python 2 past 2020 by signing the Python 3 Statement.^[9]

Comparison with MATLAB

Pyplot is a matplotlib module which provides a MATLAB-like interface.^[10] Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.

Examples

Line plot

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> a = np.linspace(0, 10, 100)
>>> b = np.exp(-a)
>>> plt.plot(a, b)
>>> plt.show()

Histogram

>>> import matplotlib.pyplot as plt
>>> from numpy.random import normal,rand
>>> x = normal(size=200)
>>> plt.hist(x, bins=30)
>>> plt.show()

Scatter plot

>>> import matplotlib.pyplot as plt
>>> from numpy.random import rand
>>> a = rand(100)
>>> b = rand(100)
>>> plt.scatter(a, b)
>>> plt.show()

3D plot

>>> from matplotlib import cm
>>> from mpl_toolkits.mplot3d import Axes3D
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> fig = plt.figure()
>>> ax = fig.gca(projection='3d')
>>> X = np.arange(-5, 5, 0.25)
>>> Y = np.arange(-5, 5, 0.25)
>>> X, Y = np.meshgrid(X, Y)
>>> R = np.sqrt(X**2 + Y**2)
>>> Z = np.sin(R)
>>> surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm)
>>> plt.show()

More examples

• 
  Image plot
• 
  Contour plot
• 
  Scatter plot
• 
  Polar plot
• 
  Line plot
• 
  3-D plot
• 
  Image plot

A large list of example plot with their source code can be found on the Matplotlib Gallery.

Toolkits

Several toolkits are available which extend matplotlib functionality. Some are separate downloads, others ship with the matplotlib source code but have external dependencies.^[11]

• Basemap: map plotting with various map projections, coastlines, and political boundaries^[12]
• Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.^[13] (matplotlib v1.2 and above)
• Excel tools: utilities for exchanging data with Microsoft Excel
• GTK tools: interface to the GTK+ library
• Qt interface
• Mplot3d: 3-D plots
• Natgrid: interface to the natgrid library for gridding irregularly spaced data.
• matplotlib2tikz: export to Pgfplots for smooth integration into LaTeX documents^[14]

Related projects

• Biggles^[15]
• Chaco^[16]
• DISLIN
• ggplot2
• GNU Octave
• Gnuplot-py^[17]
• PLplot – Python bindings available
• PyCha^[18] – libcairo implementation
• PyPlotter^[19] – compatible with Jython
• Pyx^[20]
• ReportLab
• SageMath – uses matplotlib to draw plots
• SciPy (modules plt and gplt)
• wxPython (module wx.lib.plot.py)
• Plotly – for interactive, online matplotlib and Python graphs
• Bokeh^[21] – Python interactive visualization library that targets modern web browsers for presentation

References

External links

Wikimedia Commons has media related to Matplotlib.

• Official website
• Matplotlib Cookbook