seaborn

What problem does it solve?

Creating clear, informative, and aesthetically pleasing statistical visualizations can be complex and time-consuming with basic plotting libraries. This skill simplifies the process, allowing you to quickly generate publication-quality graphics for exploratory data analysis and reporting.

Core Features & Use Cases

• Dataset-Oriented Plotting: Work directly with DataFrames to create relational, distribution, categorical, and regression plots with automatic statistical estimation.
• Publication-Quality Aesthetics: Leverage built-in themes, color palettes, and faceting capabilities to produce complex multi-panel figures with minimal code.
• Use Case: Quickly explore the relationships between multiple variables in a clinical trial dataset by generating a pairplot to visualize distributions and correlations, identifying potential trends for further investigation.

Quick Start

To create a scatter plot of total_bill vs tip colored by day: import seaborn as sns import matplotlib.pyplot as plt df = sns.load_dataset('tips') sns.scatterplot(data=df, x='total_bill', y='tip', hue='day') plt.show()