Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.

Overview

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12_Python_Seaborn_Module

Introduction 👋

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.

What’s New?

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.


Table of contents 📋

No. Name
01 Seaborn_Loading_Dataset
02 Seaborn_Controlling_Aesthetics
03 Seaborn_Matplotlib_vs_Seaborn
04 Seaborn_Color_Palettes
05 Seaborn_LM Plot_&_Reg_Plot
06 Seaborn_Scatter_Plot_&_Joint_Plot
07 Seaborn_Additional_Regression_Plots
08 Seaborn_Categorical_Data_Plot
09 Seaborn_Dist_Plot
10 Seaborn_Strip_Plot
11 Seaborn_Box_Plot
12 Seaborn_Violin_Plot
13 Seaborn_Bar_Plot_and_Count_Plot
14 Seaborn_TimeSeries_and_LetterValue_Plot
15 Seaborn_Factor_Plot
16 Seaborn_PairGrid_Plot
17 Seaborn_FacetGrid_Plot
18 Seaborn_Heat_Map
19 Seaborn_Cluster_Map
datasets
11 Python Seaborn Statistical Data Visualization.pdf

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


Install Seaborn Module:

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

  •   pip install seaborn  
    

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


Frequently asked questions

How can I thank you for writing and sharing this tutorial? 🌷

You can Star Badge and Fork Badge 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.


How can I read this tutorial without an Internet connection? GIF

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

    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.


Authors ✍️

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.


Licence 📜

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

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