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.

Discover the essentials of data visualization with Matplotlib. Learn its core concepts, explore its advantages, and create your first simple plot using this fundamental plotting library.

Master how to visualize data through the most popular plot types. Learn to build line, scatter, and bar charts to clearly communicate insights from your data.

Learn to make your plots more informative and visually appealing. Add titles, legends, colors, and grids, and discover how to arrange multiple subplots effectively.

Explore statistical visualizations that help analyze data distributions and patterns. Create histograms, box plots, and pie charts to uncover deeper statistical insights.

Take your visualization skills to the next level with Seaborn. Create advanced plots like countplots, KDEs, pair plots, and heatmaps while mastering Seaborn’s elegant style and customization options.

Creating a Canvas

Creating a Canvas