In this post, we will learn how to make Sinaplot in R and show why it is a better way visualize numerical data from multiple categories. In an earlier post, we discussed the benefits of making Violinplot than making a boxplot. This is mainly due to the fact that Boxplot relies only five summary stats… Continue reading Sinaplot vs Violin plot: Why Sinaplot is better than Violinplot
How to Combine Two Seaborn plots with Shared y-axis
In this tutorial, we will see how to join or combine two plots with shared y-axis. As an example, we will make a scatterplot and join with with marginal density plot of the y-axis variable matching the variable colors. Thanks to Seaborn’s creator Michael Waskom’s wonderful tip on how to do this. 👉 Want more?… Continue reading How to Combine Two Seaborn plots with Shared y-axis
How to Change Colors in Seaborn (Custom Palettes and Manual Colors)
Seaborn picks sensible default colors when you map a variable to hue, but real projects often need more control—consistent brand colors across plots, color-blind–friendly choices, or publication-ready figures. This hands-on tutorial shows exactly how to change colors in Seaborn, with clear, reproducible examples you can copy-paste. What you’ll learn, step by step: Apply Seaborn’s built-in… Continue reading How to Change Colors in Seaborn (Custom Palettes and Manual Colors)
How to Customize Titles in Multi-Panel plots with Seaborn?
Multi-panel plots or small multiples are a great way visualize the relationship between two variables with respect ot the values of other variables. Seaborn offers a few different ways to make a multi-panel plots, with FacetGrid is the class behind multi-panel plots in Seaborn. In this post, we will see how can we customize the… Continue reading How to Customize Titles in Multi-Panel plots with Seaborn?
Pandas Bootstrap_plot(): Understand uncertainty
One of the key parts of data analysis is to use summary statistics to understand the trend in the data. Understanding the variability in such summary statistics can be extremely useful to put weight on such summary statistics. Bootstrapping, resampling data with replacement is an extremely useful tool to quantify uncertainty. It was originally developed… Continue reading Pandas Bootstrap_plot(): Understand uncertainty