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Different Statistics in a Pivot Table – Advanced pivot_table() in Pandas 2026

One of the most powerful features of pivot_table() is the ability to apply different aggregation functions to different columns or even multiple functions to the same column. In 2026, this capability makes pivot_table() the go-to tool for creating rich, multi-metric summary reports.

TL;DR — How to Use Different Statistics

Use a dictionary in aggfunc to assign different functions per column
Use a list of functions for multiple statistics on the same column
Combine with index and columns for multi-dimensional views

1. Different Statistics for 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"],      # two stats for amount
        "quantity": "sum"               # only sum 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"],
    margins=True
).round(2)

print(sales_pivot)

3. Advanced Example – Mixed Aggregations

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="Total"
).round(2)

print(report)

4. Best Practices in 2026

Use a **dictionary** in aggfunc when you need different statistics per column
Use a **list** of functions when you want multiple stats on the same column
Always include margins=True for grand totals when creating reports
Round results for clean, professional-looking output
Combine with date components extracted via .dt for time-based pivots

Conclusion

Applying different statistics within a single pivot table is one of the most useful capabilities in Pandas. In 2026, using pivot_table() with a dictionary in aggfunc allows you to create comprehensive, multi-metric reports in just a few lines of code. This technique is perfect for management dashboards, monthly reports, and any situation where you need to see multiple perspectives of your data at once.

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