The following introductory text was written in 2008 by John D. Hunter (1968-2012), the original author of Matplotlib.
Matplotlib is a library for making 2D plots of arrays in Python. Although it has its origins in emulating the MATLAB graphics commands, it is independent of MATLAB, and can be used in a Pythonic, object-oriented way. Although Matplotlib is written primarily in pure Python, it makes heavy use of NumPy and other extension code to provide good performance even for large arrays.
Matplotlib is designed with the philosophy that you should be able to create simple plots with just a few commands, or just one! If you want to see a histogram of your data, you shouldn't need to instantiate objects, call methods, set properties, and so on; it should just work.
For years, I used to use MATLAB exclusively for data analysis and visualization. MATLAB excels at making nice looking plots easy. When I began working with EEG data, I found that I needed to write applications to interact with my data, and developed an EEG analysis application in MATLAB. As the application grew in complexity, interacting with databases, http servers, manipulating complex data structures, I began to strain against the limitations of MATLAB as a programming language, and decided to start over in Python. Python more than makes up for all of MATLAB's deficiencies as a programming language, but I was having difficulty finding a 2D plotting package (for 3D VTK more than exceeds all of my needs).
When I went searching for a Python plotting package, I had several requirements:
Plots should look great - publication quality. One important requirement for me is that the text looks good (antialiased, etc.)
Postscript output for inclusion with TeX documents
Embeddable in a graphical user interface for application development
Code should be easy enough that I can understand it and extend it
Making plots should be easy
Finding no package that suited me just right, I did what any self-respecting Python programmer would do: rolled up my sleeves and dived in. Not having any real experience with computer graphics, I decided to emulate MATLAB's plotting capabilities because that is something MATLAB does very well. This had the added advantage that many people have a lot of MATLAB experience, and thus they can quickly get up to steam plotting in python. From a developer's perspective, having a fixed user interface (the pylab interface) has been very useful, because the guts of the code base can be redesigned without affecting user code.
The Matplotlib code is conceptually divided into three parts: the
pylab interface is the set of functions provided by
pylab which allow the user to create plots with code
quite similar to MATLAB figure generating code
(Pyplot tutorial). The Matplotlib frontend or Matplotlib
API is the set of classes that do the heavy lifting, creating and
managing figures, text, lines, plots and so on
(Artist tutorial). This is an abstract interface that knows
nothing about output. The backends are device-dependent drawing
devices, aka renderers, that transform the frontend representation to
hardcopy or a display device (What is a backend?). Example
backends: PS creates PostScript® hardcopy, SVG
creates Scalable Vector Graphics
hardcopy, Agg creates PNG output using the high quality Anti-Grain
library that ships with Matplotlib, GTK embeds Matplotlib in a
application, GTKAgg uses the Anti-Grain renderer to create a figure
and embed it in a Gtk+ application, and so on for PDF, WxWidgets, Tkinter, etc.
Matplotlib is used by many people in many different contexts. Some people want to automatically generate PostScript files to send to a printer or publishers. Others deploy Matplotlib on a web application server to generate PNG output for inclusion in dynamically-generated web pages. Some use Matplotlib interactively from the Python shell in Tkinter on Windows. My primary use is to embed Matplotlib in a Gtk+ EEG application that runs on Windows, Linux and Macintosh OS X.
Matplotlib's original logo (2003 -- 2008).
Matplotlib logo (2008 - 2015).