From the website, “Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informational statistical graphs.”

Seaborn excels at doing Exploratory Data Analysis (EDA) which is an important early step in any data analysis project. Seaborn uses a “dataset-oriented” API that offers a consistent way to create multiple visualizations that show the relationships between many variables. In practice, Seaborn works best when using Pandas dataframes and when the data is in tidy format.

In my opinion the most interesting new plot is the relationship plot or relplot() function which allows you to plot with the new scatterplot() and lineplot() on data-aware grids. Prior to this release, scatter plots were shoe-horned into seaborn by using the base matplotlib function plt.scatter and were not particularly powerful. The lineplot() is replacing the tsplot() function which was not as useful as it could be. These two changes open up a lot of new possibilities for the types of EDA that are very common in Data Science/Analysis projects.

The other useful update is a brand new introduction document which very clearly lays out what Seaborn is and how to use it. In the past, one of the biggest challenges with Seaborn was figuring out how to have the “Seaborn mindset.” This introduction goes a long way towards smoothing the transition.

These are online read-only versions. However you can Run ▶ all the codes online by clicking here ➞

Open your Prompt and type and run the following command (individually):

•   pip install seaborn  
  

Once Installed now we can import it inside our python code.

You can and Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click ➞ ✰ Star and ⵖ Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.

   

2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

   Kernel > Restart & Clear Output

This will clear all the outputs and now you can understand each statement and learn interactively.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a ⭐ star.

You may use this tutorial freely at your own risk. See LICENSE.