Pandas: Cross Tabulation with pd.crosstab()

Compute frequency counts and cross-tabulation summaries using the Pandas pd.crosstab() function. Calculate factor relationships, compute percentages using normalization, apply aggregation functions, and add row/column margins.

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Introduction to pd.crosstab() 🔝

The pd.crosstab() function builds a frequency table of two or more categorical factors. By default, it counts frequency, but it can also compute summary statistics when combined with numerical values and aggregation functions.

Create categorical sample dataset 🔝

We start with a student performance dataset containing grade sections, pass/fail results, and numerical exam scores.

import pandas as pd
df = pd.DataFrame({
    "SECTION": ["A", "A", "A", "B", "B", "B", "B"],
    "RESULT": ["Pass", "Pass", "Fail", "Pass", "Fail", "Fail", "Pass"],
    "SCORE": [85, 90, 40, 78, 45, 35, 88]
})
print(df)
assert df.shape == (7, 3)

Expected output

  SECTION RESULT  SCORE
0       A   Pass     85
1       A   Pass     90
2       A   Fail     40
3       B   Pass     78
4       B   Fail     45
5       B   Fail     35
6       B   Pass     88

Basic frequency cross-tabulation 🔝

Pass two Series (or column arrays) to pd.crosstab() to compute frequency counts across factor levels.

ct = pd.crosstab(df["SECTION"], df["RESULT"])
print(ct)
assert ct.loc["A", "Pass"] == 2

Expected output

RESULT   Fail  Pass
SECTION            
A           1     2
B           2     2

Adding row and column totals (margins) 🔝

Set margins=True to display row and column totals. Customize the label using margins_name.

ct_margins = pd.crosstab(df["SECTION"], df["RESULT"], margins=True, margins_name="Total")
print(ct_margins)
assert ct_margins.loc["Total", "Total"] == 7

Expected output

RESULT   Fail  Pass  Total
SECTION                   
A           1     2      3
B           2     2      4
Total       3     4      7

Calculating proportions and percentages 🔝

Pass normalize=True to get total proportions, or normalize="index" / normalize="columns" to calculate row-wise or column-wise relative percentages.

ct_norm = pd.crosstab(df["SECTION"], df["RESULT"], normalize="index") * 100
print(ct_norm.round(1))
assert round(ct_norm.loc["B", "Pass"], 1) == 50.0

Expected output

RESULT   Fail  Pass
SECTION            
A        33.3  66.7
B        50.0  50.0

Applying aggregation functions with values 🔝

Pass a numerical Series to values along with an aggfunc (e.g., "mean", "sum", "max") to summarize numerical metrics across categories.

ct_mean = pd.crosstab(df["SECTION"], df["RESULT"], values=df["SCORE"], aggfunc="mean")
print(ct_mean)
assert ct_mean.loc["A", "Pass"] == 87.5

Expected output

RESULT   Fail  Pass
SECTION            
A        40.0  87.5
B        40.0  83.0

Exercise: student performance cross-tabulation 🔝

Compute a cross-tabulation table between GENDER (["M", "F", "M", "F", "F"]) and GRADE (["A", "B", "A", "A", "B"]) with row and column totals enabled (margins=True). Verify that the overall total equals 5.

Exercise solution 🔝

Solution constructing the Series and computing the cross-tabulation.

gender = pd.Series(["M", "F", "M", "F", "F"], name="GENDER")
grade = pd.Series(["A", "B", "A", "A", "B"], name="GRADE")
answer = pd.crosstab(gender, grade, margins=True)
print(answer)
assert answer.loc["All", "All"] == 5

Expected output

GRADE   A  B  All
GENDER           
F       1  2    3
M       2  0    2
All     3  2    5

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice the pd.crosstab() examples interactively directly in your browser:

View on GitHub

Reference: Pandas crosstab() documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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