Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Ultimate Visualization with Python

Creating a Canvas

First of all, matplotlib consists of 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)

We will mainly focus on the scripting layer with the pyplot module you have already seen and the artist layer. Artist layer contains 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.

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

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

Code Description

import matplotlib.pyplot as plt

First, we import the pyplot submodule as plt.

fig, ax = plt.subplots()

Then we use its subplots() function, which returns a figure and one Axes object by default.

ax.plot()

Next we apply the plot() function to this Axes object to create an empty plot.

plt.show()

Finally, using the show() function, we display the image of our canvas to the screen with one empty plot on it.

Everything was clear?

Section 1. Chapter 3