Fast Win Data Science
Are you having problems with Matplotlib? If you’re a beginner, it may be because you haven’t taken the time to learn some of its idiosyncrasies. If you suspect that’s the case, do yourself a favor and read on! This won’t hurt or take too long.
The open source Matplotlib library masters plotting in Python. It allows you to generate quick and simple graphs, as well as complex and elaborate graphs where you control every aspect of the visualization. Its popularity and maturity mean that you can always find useful tips and useful code examples.
Like any powerful software, Matplotlib can be, as one author put it, “syntactically tedious.” The simplest plots are easy, but the difficulty increases quickly. And although resources like matplotlib gallery provide useful code examples; If you want something slightly different than what is provided, you may find yourself scratching your head.
In fact, many people use Matplotlib by copying and pasting other people’s code and then hacking around the edges until they get something they like. As a user once told me: “No matter how many times I use Matplotlib, it always feels like the first time!”
Fortunately, you can greatly alleviate this pain by taking the time to learn a few key aspects of the package. So in this article, we will focus on the nomenclature and plotting interfaces that can cause confusion. Armed with this knowledge, you may find Matplotlib a tool you should embrace rather than one you should avoid or use reluctantly.
Based on my experience learning Matplotlib, here are three issues that cause confusion:
- The somewhat awkward nomenclature used for plots.
- The coexistence of two draw interfaces that I will call data plotting approach and the object oriented style.
- Map manipulation methods on the two interfaces that have similar but different Names.
Let’s take a look at these one by one.
