Summary
This chapter explains the Matplotlib object model with an emphasis on the Figure and Axes artists and demonstrates how to create a Figure and its Axes using pyplot.subplots and render plots via Axes.plot and plt.show().

General domain of usage
data visualization

`matplotlib` has three layers:
* **Backend layer**: renders plot to screens or files;
* **Artist layer**: describes how data is arranged, is made up of one object, `Artist`;
* **Scripting layer**: connects the previous two layers and simplifies access to them.

The main focus is on the scripting layer with the `pyplot` module and the artist layer. The artist layer includes the following:

* **Containers** (e.g. `Figure`, `Axes`);
* **Primitives** (e.g., line, rectangle, circle, text, etc).

`Figure` is the main `Artist` object and can be thought of as a canvas where all the plots will be located.  Basically, it holds everything together. 

On the other hand, `Axes` is an object made up of two axis objects, x-axis and y-axis. 



`Figure` = canvas, `Axes` = x-axis + y-axis.

Note

Now let's look at the creation of the `Figure` and its `Axes`:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot()
plt.show()

What is the main purpose of using `plt.subplots()` in the code above, and what does it return?

Master the foundational skills of data visualization using the object-oriented approach in Matplotlib. Learn to control every aspect of your figures, from axes and legends to colors and styles. Create professional-quality static charts, handle time-series data, and export high-resolution graphics for reports and publications.

Understanding Figures and Axes