Data Aggregation and Grouping

GroupBy Operations

Pandas groupby enables powerful aggregation.

Basic GroupBy

df.groupby('column').mean() groups by column and computes mean. Multiple functions: .agg(['mean', 'sum']).

Named aggregation: .agg(mean_col=('col', 'mean')). This clarifies output column names.

Multi-Level Grouping

groupby(['col1', 'col2']) creates hierarchical groups. level parameter accesses index levels.

Result is hierarchical DataFrame. Use .unstack() to pivot to wide format.

Apply and Transform

.apply() runs functions on groups. .transform() applies functions preserving index.

Filter groups: .filter(lambda x: condition). This removes groups failing condition.

Key Takeaways

1. GroupBy enables flexible aggregation
2. Named aggregation clarifies output
3. Transform applies functions to groups