Language, along with web visualization toolkits based on D3js and HTML5Ĭanvas, often make Matplotlib feel clunky and old-fashioned. Newer tools like ggplot and ggvis in the R In recent years, however, the interface and style of Matplotlib haveīegun to show their age. Matplotlib’s powerful tools and ubiquity within the scientific Python Userbase, which in turn has led to an active developer base and Has been one of the great strengths of Matplotlib. This cross-platform, everything-to-everyone approach Work regardless of which operating system you are using or which outputįormat you wish. Matplotlib supportsĭozens of backends and output types, which means you can count on it to With many operating systems and graphics backends. One of Matplotlib’s most important features is its ability to play well It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities. John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.
IPython’s creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months. It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via gnuplot from the IPython command line. Matplotlib is a multiplatform data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.
We’ll now take an in-depth look at the Matplotlib tool for visualization in Python.
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