Pivot Tables & Cross-Tabulations (pivot_table, crosstab)0%
Concatenating DataFrames (pd.concat)

Pivot Tables & Cross-Tabulations (pivot_table, crosstab)

Beginner12 min read•Updated: Jul 11, 2026
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Pivot Tables & Cross-Tabulations (pivot_table, crosstab)

Reading Time: 8 Mins | Level: Beginner to Advanced

1. Pivot Tables in Pandas

If you know PivotTables in Microsoft Excel, pd.pivot_table() will feel instantly familiar. A Pivot Table aggregates data and reshapes it into a two-dimensional grid with designated row headers, column headers, and calculated cell values.

2. Anatomy of df.pivot_table()

Python
import pandas as pd
 
df = pd.DataFrame({
'Year': [2024, 2024, 2025, 2025, 2024, 2025],
'Region': ['North', 'South', 'North', 'South', 'North', 'South'],
'Product': ['Laptop', 'Laptop', 'Laptop', 'Tablet', 'Tablet', 'Laptop'],
'Sales': [50000, 60000, 55000, 30000, 35000, 65000]
})
 
# Create Pivot Table: Rows = Region, Columns = Product, Values = Total Sales
pivot = df.pivot_table(
index='Region',
columns='Product',
values='Sales',
aggfunc='sum',
fill_value=0, # Replace NaN with 0 for product-region combinations with no sales
margins=True, # Adds 'All' row and column subtotals (Grand Totals!)
margins_name='Total'
)
print(pivot)

Output:

Output
Product Laptop Tablet Total
Region
North 105000 35000 140000
South 125000 30000 155000
Total 230000 65000 295000

3. What Does margins=True Do?

In Excel, PivotTables automatically append Grand Total rows and columns. In Pandas, setting margins=True generates these subtotal rows and columns automatically.

4. Cross-Tabulations with pd.crosstab()

pd.crosstab() is specifically designed for computing frequency tables (counts or percentages of occurrences between two categorical variables):

Python
# Frequency count table of Region vs Product
freq_table = pd.crosstab(df['Region'], df['Product'])
print(freq_table)
 
# Normalized percentage table (Proportions that sum to 100%)
pct_table = pd.crosstab(df['Region'], df['Product'], normalize='index') * 100
print(pct_table)
Interactive Quiz

Multiple Choice Questions

Question 1
What parameter in df.pivot_table() specifies the mathematical aggregation to apply (defaulting to 'mean')?
Question 2
What does margins=True add to a Pandas pivot table?
Question 3
How do you replace empty NaN cells in a pivot table with 0?
Question 4
What is the primary use case for pd.crosstab()?
Question 5
In pd.crosstab(df['A'], df['B'], normalize='index'), what does normalize='index' do?
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