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Use pd.crosstab() with its normalize argument to turn category counts into proportions. Choose "index" for row percentages, "columns" for column percentages, or "all" for each cell’s share of the whole table. These settings use different denominators, so select the one that matches the question you want the table to answer.

Create row, column, and overall percentages

Suppose df is a pandas DataFrame with categorical columns named group and outcome. Each crosstab cell represents a combination of those categories.

import pandas as pd

# Each row sums to 1: outcome distribution within each group.
row_pct = pd.crosstab(df["group"], df["outcome"], normalize="index")

# Each column sums to 1: group distribution within each outcome.
column_pct = pd.crosstab(df["group"], df["outcome"], normalize="columns")

# All cells together sum to 1: share of the full dataset.
overall_share = pd.crosstab(df["group"], df["outcome"], normalize="all")

The denominator determines the interpretation:

  • normalize="index" divides each cell by its row total. Use it to ask how outcomes are distributed within each group.
  • normalize="columns" divides each cell by its column total. Use it to ask how groups are distributed within each outcome.
  • normalize="all" divides each cell by the total across the table. Use it to find each combination’s share of all observations.

The pandas API also accepts True for whole-table normalization and numeric forms such as 0 and 1. The pandas guide demonstrates normalize=True for global normalization and normalize="columns" for column normalization. Named strings make the intended denominator easier to read in instructional code. See the pandas.crosstab API reference and pandas guide to cross-tabulations.

Convert proportions to percentage values

Normalized crosstabs contain proportions on a 0-to-1 scale. For example, a cell value of 0.25 means one quarter of the observations for the selected denominator. To return numeric values on a 0-to-100 scale, multiply the result by 100:

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row_pct_100 = row_pct.mul(100)

State the denominator in a heading, table label, or accompanying explanation. “Outcome distribution within each group” describes row percentages; “share of all observations” describes overall percentages. The word “percentage” alone does not identify which calculation was used.

Add totals with margins

Set margins=True to add an All row and column. Use margins_name to give them a clearer label:

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row_pct_with_totals = pd.crosstab(
    df["group"],
    df["outcome"],
    normalize="index",
    margins=True,
    margins_name="Total",
)

With normalization enabled, the margin values are normalized too. Check those totals against the chosen denominator before presenting the table; a margin should not be assumed to mean a raw count when the output is normalized.

When a crosstab is a count versus an aggregation

Without a values argument, pd.crosstab() produces a frequency table: it counts observations in each category combination. If you pass values, you must also supply aggfunc; pandas then aggregates those values within each combination. That is not automatically a percentage calculation. Before describing an aggregate as a percentage, define what its numerator and denominator mean.

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For broader reshaping or numeric aggregation workflows, pandas.pivot_table may be a better fit; its API documents aggregation options for pivoted data.

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Check missing values, categories, and empty results

  • Decide how to handle missing values. The dropna argument defaults to True; the API describes it as excluding columns whose entries are all NA. Decide whether missing categories belong in your analysis and inspect the resulting table before interpreting its denominator.
  • Account for unobserved categorical values. Categorical inputs can include categories with no observed instances, and those categories can appear in the output. A zero-observation category is not evidence of a positive share.
  • Investigate an unexpectedly empty crosstab. The API notes that an empty DataFrame can result when the inputs have no overlapping indexes. Check that the input Series are aligned and that their indexes refer to the observations you intend to compare.

For the full argument definitions and behavior, consult the pandas.crosstab API reference.

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