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Multiple Grouped Summaries in Pandas – Advanced GroupBy Techniques 2026

When you need to calculate several different summary statistics across multiple groups and columns, Pandas offers powerful and elegant solutions. In 2026, using groupby() combined with named aggregation in .agg() is the cleanest, most readable, and most performant way to create complex multi-group summaries.

TL;DR — Recommended Pattern

Use groupby([col1, col2]) with named aggregation
Apply different functions to different columns in one operation
Use method chaining for clarity
Combine with date components for time-based grouping

1. Basic Multiple Grouped Summaries

import pandas as pd

df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])

summary = (
    df
    .groupby(["region", "category"])
    .agg(
        total_sales=("amount", "sum"),
        avg_sale=("amount", "mean"),
        order_count=("amount", "count"),
        unique_customers=("customer_id", "nunique"),
        total_quantity=("quantity", "sum")
    )
    .round(2)
    .reset_index()
)

print(summary)

2. Advanced Multi-Level Grouped Summaries with Dates

monthly_report = (
    df
    .groupby([
        "region",
        df["order_date"].dt.to_period("M").rename("month"),
        "category"
    ])
    .agg(
        total_sales=("amount", "sum"),
        average_order_value=("amount", "mean"),
        transaction_count=("amount", "count"),
        unique_customers=("customer_id", "nunique"),
        max_sale=("amount", "max"),
        min_sale=("amount", "min")
    )
    .round(2)
    .reset_index()
)

print(monthly_report)

3. Using Custom Functions in Multiple Grouped Summaries

def cv(x):
    """Coefficient of variation"""
    return x.std() / x.mean() if x.mean() != 0 else 0

report = (
    df
    .groupby(["region", "category"])
    .agg(
        total_revenue=("amount", "sum"),
        avg_revenue=("amount", "mean"),
        revenue_volatility=("amount", cv),
        customer_count=("customer_id", "nunique"),
        product_diversity=("product_id", "nunique")
    )
    .round(2)
)

4. Best Practices in 2026

Use **named aggregation** syntax (`new_name=("column", "func")`) for clear, meaningful column names
Group by multiple columns including date components using .dt.to_period()
Always use method chaining to keep complex aggregations readable
Round results and use .reset_index() for clean output
Define small custom functions when you need special calculations

Conclusion

Creating multiple grouped summaries is one of the most powerful capabilities in Pandas. In 2026, the combination of groupby() with named aggregation allows you to generate rich, insightful reports with very clean and maintainable code. Whether you are analyzing sales by region and month, user behavior by category, or any multi-dimensional data — this pattern will serve you extremely well.

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