Intro Matplotlib, What is different in Axes and Figure ?

Whenever we need to visualize some image, statistical chart, or even data in python, Matplotlib the Python 2D plotting library, usually comes into mind.

Though we could just search for someone’s code whenever we need plot some kinds of chart, such as histogram, piechart. However, I believed there are still a little bit concepts we could be familiar with at the beginning of learning Matplotlib library to help get more sense of it and be able to make a good tune to our chart whenever we use it.

The following explanation refer to These general Concepts.

Everything in Matplotlib is organized in a hierarchy.

Top-level: the matplotlib’s “state-machine environment” (provided by matplotlib.pyplot)

Second-level: the first level of the object-oriented interface, in which pyplot is used only for a few functions such as figure creation.

Note: Official documatation recommands, for non-interactive plotting, it is suggested to use pyplot to create the figures and then the OO interface for plotting.

According to hierarchy, there are three common comfusing objects: figure, axes, and axis.

In a nutshell, one figure object contains one or more axes objects, and one axes object contains at least two axis objects.

Usually, the axes objects within the figure are used for most plotting actions.

The image below shows the componenet of a figure object.

The easiest way to create a new figure is :

fig = plt.figure()   # an empty figure with no axes
# a figure with a 2x2 grid of Axes
fig, axes = plt.subplots(2, 2) 

Axes: This is what you think of as ‘a plot’.

Axis: The number-line-like object which takes care of something like setting the data limits and generating the ticks. (the marks on the axis).

e.g.

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

val = 3 + 5 * np.random.rand(3) # each 
pos = np.arange(3) + 0.5 # the bar centers on the y axis

labels = ['Tom', 'Tim', 'Sam']

# a figure with a 1x2 grid of Axes
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,3)) 
axes[0].barh(pos, val, align='center', color='b')
axes[0].set_yticks(pos)
axes[0].set_yticklabels(labels)

axes[1].bar(pos, val, align='center', color='r')
axes[1].set_xticks(pos)
axes[1].set_xticklabels(labels)

result: