How does Pandas groupby work and what aggregation patterns are most useful?

GroupBy is the Pandas implementation of the split-apply-combine pattern: split the DataFrame into groups by one or more column values, apply an aggregation or transformation to each group, and combine the results into a new DataFrame. It is the primary tool for summary statistics on tabular data.

import pandas as pd

sales = pd.DataFrame({
    'region':  ['East','East','West','West','East','West'],
    'product': ['A','B','A','B','A','A'],
    'revenue': [100, 200, 150, 80, 120, 90],
    'units':   [10, 20, 15, 8, 12, 9],
})

# Single-column groupby with single aggregation
sales.groupby('region')['revenue'].sum()
# East    420   West    320

# Multiple aggregations on one column
sales.groupby('region')['revenue'].agg(['sum', 'mean', 'count', 'std'])

# Different aggregations per column
sales.groupby('region').agg(
    total_revenue=('revenue', 'sum'),
    avg_units    =('units',   'mean'),
    num_orders   =('revenue', 'count'),
)

# Multi-column groupby
sales.groupby(['region', 'product'])['revenue'].sum()

# transform — returns same-length Series aligned with original index
# (useful for adding group statistics back as a new column)
sales['region_total'] = sales.groupby('region')['revenue'].transform('sum')

# filter — keep only groups satisfying a condition
big_regions = sales.groupby('region').filter(lambda g: g['revenue'].sum() > 400)

transform vs agg: agg reduces each group to a scalar, returning a smaller DataFrame; transform keeps the original shape, broadcasting the group result back to each row. Use transform when you want to add a group statistic as a feature column without losing row-level detail.

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