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Pandas DataFrame | cumprod method

Pandas
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Documentation
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas DataFrame.cumprod(~) method computes the cumulative product along the row or column of the source DataFrame.

Parameters

1. axislink | int or string | optional

Whether to compute the cumulative product along the row or the column:

Axis

Description

Compute the cumulative product of each column.

0 or "index"

Compute the cumulative product of each row.

1 or "columns"

By default, axis=0.

2. skipnalink | boolean | optional

Whether or not to ignore NaN. By default, skipna=True.

Return Value

A DataFrame holding the cumulative product of the row or column values.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]})
df
   A  B
0  3  5
1  4  6

Cumulative product of each column

To compute the cumulative product for each column:

df.cumprod()
   A   B
0  3   5
1  12  30

Cumulative product of each row

To compute the cumulative product for each row:

df.cumprod(axis=1)
   A  B
0  3  15
1  4  24

Dealing with missing values

Consider the following DataFrame with a missing value:

df = pd.DataFrame({"A":[3,pd.np.nan,5]})
df
A
0 3.0
1 NaN
2 5.0

By default, skipna=True, which means that missing values are ignored:

df.cumprod()   # skipna=True
A
0 3.0
1 NaN
2 15.0

To take into account missing values:

df.cumprod(skipna=False)
A
0 3.0
1 NaN
2 NaN

Here, notice how we end up with a NaN after the first NaN.

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Published by Isshin Inada
Edited by 0 others
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