Distribution Plots (KDE, Histograms)

Understanding how data is distributed is fundamental to data analysis. Let's explore advanced techniques for visualizing distributions.

Kernel Density Estimation (KDE)

Comparing Distributions

Rug Plots

2D Density Plots

Multiple Subgroup Histograms

Cumulative Distribution

Practice: Distribution Analysis

Key Takeaways

• KDE smooths histograms into continuous density curves
• Use density=True to normalize histograms for KDE comparison
• Rug plots show actual data point locations
• 2D density plots (contours) show joint distributions
• Cumulative distributions help find percentiles
• Compare distributions using overlaid KDEs
• Skewed data shows mean ≠ median

In the next lesson, we'll explore regression and relationship plots.