Mastering Seaborn: Demystifying the Complex Plots!





When it comes to Data Visualization, Seaborn is like a hidden gem! While doing Exploratory Data Analysis, we often start with Matplotlib, and explore relatively less with Seaborn! But if only you knew the full potential of seaborn, you would be astonished to see how much more you can explore in your data.

Unlike Matplotlib, you don’t need to write plenty of lines of code to create appealing plots! Seaborn is easy to write and most importantly it integrates seamlessly with pandas, one would say that Seaborn is in love with pandas!

First things first, import seaborn as sns , ‘sns’ is the commonly used alias. We know NumPy is short for Numerical Python, and Matplotlib is short for Mathematical Plotting Library. But Guess what Seaborn is short for? A Movie Character!! Yes, Apparently it’s named after a character named Samuel Norman Seaborn from the television show “The West Wing” — thus, the standard alias is the character’s initials (“sns”).

There are many plots in Seaborn and it gets confusing to remember them! When I was thinking of an easy way to remember them, I realized that we can highly categorize them into the following sections based on variables, such as: