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Multiple Statistics in a Pivot Table – Advanced pivot_table() Techniques 2026

Creating pivot tables with multiple statistics (sum, mean, count, std, etc.) on the same or different columns is a very common requirement for professional reports. In 2026, Pandas pivot_table() makes this elegant and flexible using dictionaries and lists inside the aggfunc parameter.

TL;DR — How to Apply Multiple Statistics

Use a **dictionary** to assign different functions to different columns
Use a **list** to apply multiple functions to the same column
Combine both approaches for rich multi-metric pivot tables
Use margins=True to add grand totals

1. Multiple Statistics on Different Columns

import pandas as pd

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

pivot = pd.pivot_table(
    df,
    values=["amount", "quantity"],
    index="region",
    columns=df["order_date"].dt.to_period("M").rename("month"),
    aggfunc={
        "amount": ["sum", "mean", "max"],     # multiple stats for amount
        "quantity": ["sum", "mean"]           # multiple stats for quantity
    },
    margins=True
).round(2)

print(pivot)

2. Multiple Statistics on the Same Column

sales_pivot = pd.pivot_table(
    df,
    values="amount",
    index=["region", "category"],
    columns=df["order_date"].dt.year.rename("year"),
    aggfunc=["sum", "mean", "count", "std", "min"],   # multiple functions on amount
    margins=True
).round(2)

print(sales_pivot)

3. Combined Approach – Rich Multi-Metric Pivot Table

report = pd.pivot_table(
    df,
    values=["amount", "quantity", "customer_id"],
    index="region",
    columns="category",
    aggfunc={
        "amount": ["sum", "mean", "max"],
        "quantity": "sum",
        "customer_id": "nunique"
    },
    margins=True,
    margins_name="Grand Total"
).round(2)

print(report)

4. Best Practices in 2026

Use a dictionary in aggfunc when applying different statistics to different columns
Use a list when you want several statistics on the same column
Always round the final pivot table for clean reports
Add margins=True to include grand totals
Use meaningful index and column names (including date components via .dt)

Conclusion

Applying multiple statistics within a single pivot table is one of the most useful reporting techniques in Pandas. In 2026, using pivot_table() with a combination of dictionary and list in aggfunc allows you to create comprehensive, multi-dimensional reports that show totals, averages, counts, and more — all in one clean table. This approach is widely used for management dashboards and business intelligence reports.

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