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GroupBy aggregations and pivot tables for business reporting

Dr. Elena Vasquez Apr 2026
1 tab 
import pandas as pd

df = pd.read_parquet('events.parquet')
df['event_date'] = pd.to_datetime(df['event_date'])
df['month'] = df['event_date'].dt.to_period('M').astype(str)

channel_summary = (
    df.groupby(['month', 'acquisition_channel'], as_index=False)
    .agg(
        sessions=('session_id', 'nunique'),
        users=('user_id', 'nunique'),
        revenue=('revenue', 'sum'),
        avg_order_value=('revenue', 'mean'),
    )
)

revenue_matrix = pd.pivot_table(
    channel_summary,
    index='month',
    columns='acquisition_channel',
    values='revenue',
    fill_value=0,
    aggfunc='sum',
)

print(channel_summary.head())
print(revenue_matrix.tail())
1 file · python Explain with highlit

I reach for groupby when I need trustworthy aggregates that can power dashboards or analytical reports. Clear aggregation naming matters because these outputs frequently get joined back into feature tables or exported to BI systems. pivot_table is useful when stakeholders want category x time summaries without manual spreadsheet work.

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