Seaborn is a Python data visualization library built on top of the matplotlib library. It provides a high-level interface for drawing attractive and informative statistical graphics. It is specifically designed to interact with Pandas DataFrames to create statistical plots by providing various functions to draw plots, and all the customization is done through function arguments.
In this note series, we will learn how to create various types of plots, and these are mentioned below, by using the python seaborn library with various examples and use cases. We will also analyze the results so that readers can learn how to analyze the plots and make reports or observations. Along with all, we will provide in-depth theory as to when to use which types of plots. This will help us to the selection of correct plots to make correct decisions from the given dataset.
- Scatter Plots
- Distribution Plots
- Categorical Plots
- Comparison Plots
- Seaborn Girds
- Matrix Plots
Scatter plots show the relationship between two continuous features, and these are represented in the form of numerical variables that can take any number between two selected values. These numerical variables usually contain uncountable values—for example, student age, height, marks, etc.
Seaborn library can be directly installed using the python-pip package manager when we are using native python distribution. However, we can also use one of the most famous python distributions known as the anaconda individual edition. It is widely used for data science, data analytics, and machine learning.
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