sns.scatterplot(data=df, x="a", y="b", hue="cat")- Encode extra dimensions:
size, style, hue - Identify clusters and outliers in business data
- Built-in legend from hue, size, and style parameters
sns.regplot(x, y) — scatter + linear fit + CI bandsns.lmplot() — regplot with category faceting- Confidence band width reflects uncertainty of the fit
order=2 for polynomial regression curves
sns.pairplot(df, hue="segment")- All pairwise scatter plots + diagonal histograms
- Instantly reveals which variable pairs correlate
- Use
vars=["a","b","c"] to limit columns shown
sns.jointplot(data=df, x="a", y="b", kind="hex")- Marginal distributions on both x and y axes
- Kinds: scatter, kde, hex, hist, reg
- Best for deep bivariate analysis of two variables
- Pearson r: linear relationship strength (−1 to +1)
df.corr() computes the full correlation matrix- |r| > 0.7 = strong; 0.4–0.7 = moderate; < 0.4 = weak
- Always visualize — correlation ≠ causation
- Part A: Customer success metrics scatter exploration
- Part B: Support response time impact regression plot
- Part C: Revenue drivers pair plot and correlation matrix
- Annotate key insights directly on each chart