Here’s a practical, code-first playbook for exploring numerical data 👇

How we approach EDA (with Python code + outputs):

- Basics: shape, dtypes, and missing values.
- Denoscriptives: mean/median, variance, and percentiles for quick sanity checks.
- Distributions: histograms, boxplots, density to spot skew and spread.
- Relationships: scatter plots and a correlation heatmap to find signals.
- Outliers: z-scores/IQR to flag anomalies worth investigating.
- Scaling: MinMax vs Z-score depending on the model and metric.
- Segments: groupby comparisons to surface patterns you miss in globals.
- Decisions: tie insights back to the question and next steps.

What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.