Scatterplot

Scatter plots show the relationship between two quantitative variables by plotting data points on a two-dimensional grid. They're fundamental for identifying correlations, clusters, and outliers in your data. By adding color, size, or shape encoding, scatter plots can display additional dimensions. Regression lines can be added to quantify relationships.

Example Visualization

Scatter plot of height vs weight colored by gender with regression line

Try this prompt

"Create a scatter plot analyzing the relationship between 'Height (cm)' and 'Weight (kg)' for 200 individuals. Generate realistic biometric data: heights 150-195cm, weights 45-110kg with positive correlation (r≈0.75). Color points by 'Gender' (Male: blue, Female: pink) using different markers (circles, squares). Add separate linear regression lines for each gender with 95% confidence intervals shaded. Include R² values in the legend. Add a marginal histogram/KDE on both axes showing distributions. Label axes with units, add gridlines, and title 'Height vs Weight by Gender (n=200)'. Annotate any outliers (>2 std from regression line)."

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Python Code Example

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Common Use Cases

1Correlation analysis between metrics
2Cluster identification in data
3Outlier detection
4Regression modeling

Pro Tips

Use transparency for overlapping points

Add marginal distributions for context

Include regression line with confidence interval