Summary  
This chapter introduces how to create and manage Figure and Axes objects in matplotlib’s artist layer via the pyplot module, demonstrating the use of subplots() to set up a canvas, plotting on the Axes, and printing the canvas with 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()

Data is everywhere around us, and making sense of it is extremely important. Visualization helps you deal with data by finding certain patterns and insights in it. You will develop a solid foundation of data visualization using Python and its libraries, such as matplotlib and seaborn, to get as much information from data as possible in a neat and concise way.

First, we will discuss plotting applications, Matplotlib library, and its advantages. Afterwards you will create your first simple plot using Matplotlib.

Now you are ready to create various commonly used in practice plots. Without further ado, let's dive in right now!

Plotting and visualization is not only about creating a plot or several plots, it is also about making them clear, understandable and pleasant to look at. Time to learn plots customization in maplotlib, let's do it!

Statistics is essential in working with data, hence why it is extremely important to know how to create various statistical plots. Now it's time for us to explore the most common ones.

Seaborn is another powerful plotting library in Python built on top of matplotlib. Now that you are familiar with matplotlib, it's time to dive into visualization with seaborn.

Creating a Canvas

Creating a Canvas