Visualizing Distributions
[SYSTEM_LOG]: Identifying patterns in raw numerical arrays using Kernel Density Estimation and Histogram Binning.
The Shape of Data
In Data Science, we rarely care about individual points. We care about the density. Distribution visualization allows us to detect outliers, understand spread, and verify statistical assumptions (like normality) before feeding data into Machine Learning models.
Core Visualization Tools
- HistogramsDiscretizes quantitative data. Binning is crucial—too few bins hide details, too many create noise.
- KDEKernel Density Estimation. A non-parametric way to estimate the probability density function of a random variable.
- Rug PlotsDraws small vertical lines at every data point. Best used in combination with KDE.
Mastering `sns.displot`
Seaborn 0.11+ introduced `displot()`, a figure-level function for drawing distribution plots. It provides a unified interface for histplots and kdeplots.
# Bivariate distribution with marginals
sns.jointplot(data=df, x="bill_length_mm", y="bill_depth_mm", kind="kde")
Data FAQ
When should I use KDE over Histogram?
Use KDE when you want to visualize the continuous underlying shape without being distracted by bin edges. Use Histograms when the exact count of observations in intervals is required.
What is a 'Bimodal' distribution?
It is a distribution with two different modes (peaks). This often suggests that your data contains two distinct groups (e.g., adult and child heights) that should be analyzed separately.