Grouping and aggregation Updated November 2026

pd.crosstab

pd.crosstab(df["a"], df["b"]) counts how many rows have each combination of a and b, returning a table with a values as rows and b values as columns. normalize="index" turns counts into row percentages and margins=True adds totals.

Syntax

pd.crosstab(index, columns, values=None, aggfunc=None, margins=False, normalize=False)

Part	What it is
`index`	Series (or list of Series) for the rows
`columns`	Series (or list) for the columns
`values / aggfunc`	Aggregate a third column instead of counting rows
`margins`	True adds an All row and column
`normalize`	True, "index" or "columns" to show proportions

Examples

import pandas as pd

df = pd.DataFrame({"gender": ["F", "M", "F", "F", "M"],
                   "plan": ["free", "pro", "pro", "free", "free"],
                   "spend": [0, 20, 20, 0, 0]})
pd.crosstab(df["gender"], df["plan"])

Result: plan free pro gender F 2 1 M 1 1

pd.crosstab(df["gender"], df["plan"], margins=True)

Result: plan free pro All gender F 2 1 3 M 1 1 2 All 3 2 5

pd.crosstab(df["gender"], df["plan"], normalize="index").round(2)

Result: plan free pro gender F 0.67 0.33 M 0.50 0.50

Each row sums to 1: the share of plans within each gender.

Common mistakes

TypeError when passing column names as strings: crosstab takes Series, so write df["a"], not "a".
NaN in the table after using values: pass aggfunc too, or fill with .fillna(0).
Percentages look wrong: normalize=True divides by the grand total; use "index" for row shares or "columns" for column shares.

Practice pd.crosstab

These drills check your answer on live data, free, no account needed.

Counts with crosstabCount the orders for each region (rows) and product (columns) with `pd.crosstab`.

Questions

crosstab or pivot_table?

crosstab is a shortcut for counting combinations of two or more columns and supports normalize directly. pivot_table is more general for aggregating a values column.

How do I cross-tabulate three variables?

Pass a list for index or columns: pd.crosstab([df.a, df.b], df.c) gives a two-level row index.

pivot_tableHow to build a pivot table in pandas with pivot_table: index, columns, values, aggfunc, fill_value for empty cells, margins for totals, several aggregations.
value_countsHow to count values in a pandas column with value_counts: counts sorted by frequency, percentages with normalize=True, including NaN, and two-column combos.
groupbyHow groupby works in pandas: group by one or more columns, apply sum, mean, count or size, get a flat table with as_index=False, and iterate over groups.
cut and qcutHow to bin numeric data in pandas: pd.cut for fixed ranges with labels like age bands, pd.qcut for equal-sized quantile groups, right-closed edges and labels.