A Complete Guide to Plotting Categorical Variables with Seaborn

A Complete Guide to Plotting Categorical Variables with Seaborn

See how Seaborn can make your plots looks nicer, convey more info, and require few lines of code

The Data

In this post we will use one of Seaborn’s conveniently available datasets about the Titanic, which I’m sure many readers have seen before. Seaborn has quite a few datasets ready to be loaded into Python to practice with; they are great for practicing data processing, exploration, and basic machine learning techniques.

titanic = sns.load_dataset('titanic')

titanic.head()

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titanic.head()

titanic.info()

titanic['species'].unique()

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titanic.info()

This data set is great because it has a decent number of entries — almost 900 — while also having an interesting story to dig into. There are lots of questions to ask and relationships between variables to explore making it a great example data set. Most critical for this article is that there is also a good mix of numerical and categorical variables to explore.

data-visualization seaborn python python-programming

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