I would advise against using matplotlib for: But from firsthand experience, I can vouch for a few other, better tools. It’s true that you can create interactive graphical user interfaces with realtime updates using matplotlib. The default selection of axis annotations, fonts and ability to render mathematical notation using LaTeX syntax make it perfect for preparing figures for scientific journals or homework. When generating bitmap images matplotlib provides aesthetically pleasing rendering using Anti Grain Geometry (Agg). It can generate vector images in a variety of formats using its hardcopy (non-interactive) backends. The second area of matplotlib’s excellence is data visualization for publication. Additionally, the seaborn library built on top of matplotlib provides even more visualizations with some basic data analysis, such as linear regression or kernel density estimation, built in. With pyplot you can generate a variety of plots with a small number of keystrokes and interactively augment existing figures with new data. Matplotlib’s strength in exploratory data analysis comes from the pyplot interface. There are two areas where matplotlib is particularly powerful: The question is, what is hard and what is easy to implement in matplotlib? The ability to combine these two styles leads to great flexibility – according to the library maintainers, matplotlib makes easy things easy and hard things possible. The initial code can be easily converted to object-oriented style once you have finished exploring the data and know what visualizations you are going to need. Having to look up which methods belong to which objects interrupts the flow of analytical thought and negatively affects productivity. This is reasonable, and we stick to this style in this tutorial, however I would advise not following it too rigidly when exploring new data. The Matplotlib user guide recommends using pyplot only to create figures and axes, and, once those are created, use their respective methods to create plots. In practice, it’s almost impossible to use matplotlib without pyplot. While this allows for fast experimentation, it can result in less reusable and less maintainable code. Pyplot determines the object to apply the relevant method from the context or creates the necessary objects on the fly, if they don’t exist. Hardcopy backends are also called “non-interactive”.Ī distinguishing feature of Matplotlib is the pyplot state machine which enables users to write concise procedural code. In order to produce a file on a disk, matplotlib uses hardcopy backends for a variety of bitmap (png, jpg, gif) and vector (ps, ps, svg) file formats. Outside of matplotlib documentation, user interface backends are typically referred to as “interactive”.
In order to produce a plot on the screen, the matplotlib Figure instance must be coupled with one of the supported user interface backends such as TkInter, Qt, WxWidgets or MacOs. The hierarchy starts with the top-level Figure object that may contain a series of intermediate level objects and Axes – from Scatter, to Line and Marker, and all the way down to Canvas. Matplotlib offers a hierarchy of objects abstracting various elements of a plot. Hunter in 2003 as a way of providing a plotting functionality similar to that of MATLAB, which at the time was the most popular programming language in academia. Matplotlib is the most popular plotting library for Python. After reading this post you’ll understand what matplotlib is, when and how to use it, when not to use it, and where to find help! Table of Contents In it, we’ll discuss the purpose of data visualization and construct several simple plots to showcase the basic matplotlib functionality.
This post features a basic tutorial on matplotlib plotting package for python.
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