Matplotlib

{{#invoke:Infobox|infobox}} 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.^[1] SciPy makes use of Matplotlib.

Matplotlib was originally written by John D. Hunter, has an active development community,^[2] 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,^[3] and further joined by Thomas Caswell.^[4]^[5]

Template:As of, matplotlib 2.0.x supports Python versions 2.7 through 3.6. Python3 support started with Matplotlib 1.2. Matplotlib 1.4 is the last version to support Python 2.6.^[6]

Matplotlib has pledged to not support Python 2 past 2020 by signing the Python 3 Statement.^[7]

Your First Machine Learning Project in Python Step-By-Step

https://machinelearningmastery.com/machine-learning-in-python-step-by-step/

Machine Learning Mastery With Python

https://machinelearningmastery.com/machine-learning-with-python/

Comparison with MATLAB

Pyplot is a Matplotlib module which provides a MATLAB-like interface.^[8] 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

File:Matplotlib basic v.svg

<source lang="numpy"> >>> 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() </source> Template:Clear Histogram

File:Matplotlib histogram v.svg

<source lang="numpy"> >>> import matplotlib.pyplot as plt >>> from numpy.random import normal,rand >>> x = normal(size=200) >>> plt.hist(x, bins=30) >>> plt.show() </source> Template:Clear Scatter plot

File:Matplotlib scatter v.svg

<source lang="numpy"> >>> import matplotlib.pyplot as plt >>> from numpy.random import rand >>> a = rand(100) >>> b = rand(100) >>> plt.scatter(a, b) >>> plt.show() </source> Template:Clear 3D plot

File:Matplotlib 3d v.svg

<source lang="numpy"> >>> 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() </source> More examples

Mpl example qbo.svg

Image plot
Mpl example Helmoltz coils.svg

Contour plot
Weight Growth of RN First Rate Line-of-Battle Ships 1630-1875.svg

Scatter plot
Logarithmic Spiral Pylab.svg

Polar plot
Temp-sunspot-co2.svg

Line plot
Mpl example Rosenbrock function.svg

3-D plot
Mandelbrot set, plotted with Matplotlib.svg

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.^[9]

Basemap: map plotting with various map projections, coastlines, and political boundaries^[10]
Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.^[11] (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^[12]

Related projects

Biggles^[13]
Chaco^[14]
DISLIN
GNU Octave
Gnuplot-py^[15]
PLplot – Python bindings available
PyCha^[16] – libcairo implementation
PyPlotter^[17] – compatible with Jython
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^[18] – Python interactive visualization library that targets modern web browsers for presentation

References

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External links

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Template:Official website

Template:SciPy ecosystem

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[8] Matplotlib – Introduction

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Matplotlib

Contents

Comparison with MATLAB

Examples

Toolkits

Related projects

See also

References

External links

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