![]() Recent Matplotlib versions make it relatively easy to set new global plotting styles (see Customizing Matplotlib: Configurations and Style Sheets), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs-for example, Seaborn (discussed in Visualization With Seaborn), ggpy, HoloViews, Altair, and even Pandas itself can be used as wrappers around Matplotlib's API.Įven with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.įor this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly. Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine. Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned. ![]() If there is 'if else' logic around the plots, only one plot is included in pdf or word output. If I have several ggplots in a row there is now problem. In recent years, however, the interface and style of Matplotlib have begun to show their age. I am using rstudio on a windows 10 professional machine and am using rmarkdown::render to create files from an rmarkdown notebook. Here we will generate some data, show some of it in a table, plot it, and show the results of fitting a simple linear regression. It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world. This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib. Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish. One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends. 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 multi-platform 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 package for visualization in Python. ![]()
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