Summary  
This chapter explains how to create a plotting canvas in Matplotlib by instantiating a Figure and its Axes using the pyplot subplots function, and then rendering it with plot and show, illustrating the artist and scripting layers.  

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?

Learn how to explore and communicate data through visual analysis. The course covers creating common plots, customizing visualizations, and using statistical charts to examine data distributions, relationships, and patterns. Exploratory data analysis techniques are applied to identify trends, anomalies, and insights in real-world datasets.

Creating a Canvas