How pandas cumprod works in Python? Best example

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In today’s article, I want to walk you through one of the lesser-known but powerful functions in the pandas library: cumprod(). If you’ve ever needed to calculate cumulative products in Python, this function is your go-to tool. Let’s dive deep into understanding how pandas.cumprod() works in Python with practical examples.

Understanding pandas.cumprod()

The cumprod() function in Pandas computes the cumulative product of a Series or DataFrame over a specified axis. In simple terms, it multiplies each element by the previous cumulative product.

Here’s a quick rundown of its syntax:

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

And here’s what the parameters mean:

• axis: Defines the axis along which to compute the cumulative product (0 for rows, 1 for columns).
• skipna: Determines whether to ignore NaN values (True by default).

Practical Example: Using pandas.cumprod() on a Series

Let’s start with a simple example using a Pandas Series to see how the function works.

import pandas as pd

# Create a Pandas Series
data = pd.Series([2, 3, 4, 5])

# Apply cumprod and print the result
print(data.cumprod())

Output:

0     2
1     6
2    24
3   120
dtype: int64

What happens here? Each value is multiplied by the previous cumulative product:

• 2 (first element remains unchanged)
• 2 × 3 = 6
• 6 × 4 = 24
• 24 × 5 = 120

Applying pandas.cumprod() on a DataFrame

Now let’s see how cumprod() works on a DataFrame. We can specify whether we want the cumulative product to be calculated along rows (axis=0) or columns (axis=1).

import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [2, 3, 4, 5]
})

# Compute cumprod along rows (default)
print(df.cumprod())

# Compute cumprod along columns
print(df.cumprod(axis=1))

Output:

Cumulative product along rows:

   A   B
0  1   2
1  2   6
2  6  24
3 24 120

Cumulative product along columns:

   A   B
0  1   2
1  2   6
2  3  12
3  4  20

Be mindful of the axis parameter: axis=0 (default) computes downward row-wise products, while axis=1 computes across columns.

Handling NaN Values in cumprod()

By default, cumprod() skips NaN values, but if you want to include them, set skipna=False. Let’s see the difference:

df_with_nan = pd.Series([2, None, 4, 5])

print(df_with_nan.cumprod())  # Default, skips NaN
print(df_with_nan.cumprod(skipna=False))  # Includes NaN

Output:

0     2.0
1     NaN
2     8.0
3    40.0
dtype: float64

0     2.0
1     NaN
2     NaN
3     NaN
dtype: float64

As you can see, when skipna=True, the NaN value is ignored, while with skipna=False, NaN propagates through the result.

Real-World Use Case: Investment Growth Over Time

A common real-world application of cumprod() is calculating cumulative growth rates, such as the change in investment value over time given a series of percentage gains.

return_rates = pd.Series([1.02, 1.03, 1.05, 0.97])

investment_growth = return_rates.cumprod()
print(investment_growth)

Output:

0    1.0200
1    1.0506
2    1.1031
3    1.0700
dtype: float64

This shows the cumulative return of an investment given a sequence of percentage changes.

Comparison with Other Cumulative Functions

To put cumprod() into perspective, let’s compare it with other cumulative functions in Pandas:

Function	Description
cumsum()	Cumulative sum
cumprod()	Cumulative product
cummax()	Cumulative maximum
cummin()	Cumulative minimum

The main difference is that cumprod() performs multiplication, while cumsum() adds values progressively.

Conclusion

So there you have it! pandas.cumprod() is a powerful yet simple function that helps compute cumulative products efficiently. Whether you’re working with financial data, growth metrics, or mathematical computations, this function has you covered.

For more advanced usage, try experimenting by combining cumprod() with Pandas’ groupby() or rolling windows. Happy coding!

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