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Python Seaborn Data Analysis Tips - Figure level vs Axes level plots

Updated: Aug 21, 2023

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From Seaborn Documentation

The figure-level plotting tools, relplot, displot, catplot, provide powerful functionalities for visualizing data relationships, distributions, and categorical variables in a concise and intuitive manner.

Starting with relplot, this tool is particularly useful for exploring the relationships between two continuous variables. It creates a scatter plot by default, allowing us to identify any potential patterns, correlations, or trends in the data. Additionally, relplot offers the flexibility to incorporate additional dimensions using color, size, or style encodings, which can further enhance our understanding of the underlying relationships. With its concise syntax and built-in options for subplots and facet grids, relplot enables us to easily compare multiple relationships simultaneously.

Moving on to displot, this tool is designed to provide insights into the distribution of a single variable. Whether it's examining the shape, spread, or skewness of the data, displot offers various visualization options such as histograms, kernel density estimation plots, or rug plots. With a few lines of code, displot can generate informative visualizations that help us understand the underlying distribution and identify outliers or unusual patterns in the data.

Lastly, catplot comes in handy when working with categorical variables. It allows us to plot the relationship between categorical variables and one or two continuous variables. With catplot, we can create various types of plots like bar plots, box plots, or point plots, enabling us to compare and analyze the distributions or relationships across different categories. Additionally, catplot offers options for grouping, ordering, and styling the categorical variables, making it a versatile tool for visualizing categorical data in a meaningful way.

In summary, relplot, displot, catplot are powerful figure-level plotting tools that provide efficient and flexible ways to explore relationships, distributions, and categorical variables. With their intuitive syntax and a wide range of visualization options, these tools enable us to gain valuable insights into our data quickly and effectively.

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