Ultimate Visualization with Python

1. Matplotlib Introduction

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

Plotting Applications

Why Use Matplotlib?

Creating a Canvas

Basic Plotting

2. Creating Commonly Used Plots

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

3. Plots Customization

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!

Adding Title to the Plot

Adding Legend

Axes Customization

Colors and Transparency

Customizing Grid

Subplots

4. More Statistical Plots

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.

Histogram

Box Plot

Pie Chart

5. Plotting with Seaborn

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.

Seaborn Introduction

Seaborn is a high-level plotting library based on matplotlib. Let’s have a look at its advantages in comparison to matplotlib:

simpler interface;
a wide range of default styles and color palettes;
works well with pandas DataFrame;
a wide range of built-in statistical functions.

Here is an example of a plot created using seaborn with just one line of code:

Apparently, everything you can accomplish using seaborn can be also done with matplotlib, yet it may often take more time and effort.

However, matplotlib being a rather low-level library in comparison, offers more flexibility for your plots, so if you need to create a rather unique plot with a lot of customizations, matplotlib is still the best choice.