How to Use cumprod in pandas for Cumulative Product Calculation

In pandas, use cumprod() to calculate the cumulative product of values in a Series or DataFrame along a specified axis. It multiplies values sequentially, returning a new object with the cumulative product at each step.

Syntax

The cumprod() function syntax is:

• Series.cumprod(axis=None, skipna=True, *args, **kwargs)
• DataFrame.cumprod(axis=0, skipna=True, *args, **kwargs)

Parameters explained:

• axis: For DataFrame, 0 for rows (default), 1 for columns.
• skipna: Whether to ignore NaN values (default is True).

python

import pandas as pd

# For Series
series.cumprod(skipna=True)

# For DataFrame
df.cumprod(axis=0, skipna=True)

Example

This example shows how to calculate the cumulative product of a pandas Series and DataFrame.

python

import pandas as pd

# Create a Series
s = pd.Series([1, 2, 3, 4])
print('Series cumulative product:')
print(s.cumprod())

# Create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})
print('\nDataFrame cumulative product along rows (axis=0):')
print(df.cumprod())

print('\nDataFrame cumulative product along columns (axis=1):')
print(df.cumprod(axis=1))

Output

Series cumulative product:
0     1
1     2
2     6
3    24
dtype: int64

DataFrame cumulative product along rows (axis=0):
   A   B
0  1   4
1  2  20
2  6 120

DataFrame cumulative product along columns (axis=1):
   A   B
0  1   4
1  2  10
2  3  18

Common Pitfalls

Common mistakes when using cumprod() include:

• Not specifying axis correctly for DataFrames, leading to unexpected results.
• Ignoring NaN values when skipna=False, which causes the cumulative product to become NaN from that point onward.
• Using cumprod() on non-numeric data, which will raise errors.

python

import pandas as pd

# DataFrame with NaN
df = pd.DataFrame({
    'A': [1, None, 3],
    'B': [4, 5, None]
})

# Wrong: skipna=False causes NaN to propagate
print('Cumulative product with skipna=False:')
print(df.cumprod(skipna=False))

# Right: skipna=True ignores NaN and continues
print('\nCumulative product with skipna=True:')
print(df.cumprod(skipna=True))

Output

Cumulative product with skipna=False:
     A    B
0  1.0  4.0
1  NaN  NaN
2  NaN  NaN

Cumulative product with skipna=True:
     A     B
0  1.0   4.0
1  1.0  20.0
2  3.0  20.0

Quick Reference

Summary tips for using cumprod():

• Use axis=0 to calculate cumulative product down rows (default).
• Use axis=1 to calculate cumulative product across columns.
• Set skipna=True to ignore missing values.
• Works on numeric data only.

Key Takeaways

Use pandas cumprod() to get cumulative product of Series or DataFrame values.

Specify axis to control direction of cumulative product in DataFrames.

Set skipna=True to ignore NaN values and continue calculation.

Avoid using cumprod() on non-numeric data to prevent errors.

Check results carefully when NaN values are present to avoid unexpected NaNs.