**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.

Data is everywhere around us and making sense of it is extremely important. Visulization helps us deal with data by finding certain patterns and insights in it. We 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. Without further ado, let's dive in!

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.

Seaborn Introduction

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