Pandas DataFrame cumprod() Method – Be on the Right Side of Change

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

The Pandas library enables access to/from a DataFrame.
The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd 
import numpy as np

DataFrame cumprod()

The cumprod() method returns a DataFrame/Series of the same size containing the cumulative product.

The syntax for this method is as follows:

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

Parameters	Description
`axis`	If zero (0) or index is selected, apply the function to each column. Default is `None`. If one (1) is selected, apply the function to each row.
`skipna`	This parameter excludes NaN or NULL values. If a row/column contains these values, the result is NaN. By default, this is `True`.
`*args`	Additional keywords have no effect. However, they might be compatible with NumPy.
`**kwargs`	Additional keywords have no effect. However, they might be compatible with NumPy.

This example displays the cumulative product of the Hockey Team Stats.

df_teams = pd.DataFrame({'Bruins':   [4, 5, 9],
                         'Oilers':    [3, 6, 10],
                         'Leafs':     [2, 7, 11],
                         'Flames':  [1, 8, 12]})

result = df_teams.cumprod(axis='index')
print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_teams.
Line [2] retrieves the cumulative product and saves them to the result variable.
Line [3] outputs the result to the terminal.

Output

	Bruins	Oilers	Leafs	Flames
0	4	3	2	1
1	20	18	14	8
2	180	180	154	96

💡 Note: By default, Line [6] iterates over all the rows and determines the value for each column. This is equivalent to axis=None or axis=’index’ (used in our example).

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

👈 df.cummin()

df.cumsum() 👉

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.