Python Data Science Handbook, 2nd Edition

Chapter 33. Customizing Ticks

Matplotlib’s default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This chapter will give several examples of adjusting the tick locations and formatting for the particular plot type you’re interested in.

Before we go into examples, however, let’s talk a bit more about the object hierarchy of Matplotlib plots. Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the Figure is the bounding box within which plot elements appear. Each Matplotlib object can also act as a container of subobjects: for example, each Figure can contain one or more Axes objects, each of which in turn contains other objects representing plot contents.

The tickmarks are no exception. Each axes has attributes xaxis and yaxis, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes.

Major and Minor Ticks

Within each axes, there is the concept of a major tickmark, and a minor tickmark. As the names imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots (see Figure 33-1).

In [1]: import matplotlib.pyplot as plt
        plt.style.use('classic')
        import numpy as np

        %matplotlib inline

In [2]: ax = plt.axes(xscale='log', yscale ...