pandas.DataFrame.cumprod — pandas 2.2.3 documentation (original) (raw)

DataFrame.cumprod(axis=None, skipna=True, *args, **kwargs)[source]#

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0

The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

*args, **kwargs

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame

Return cumulative product of Series or DataFrame.

See also

core.window.expanding.Expanding.prod

Similar functionality but ignores NaN values.

Return the product over DataFrame axis.

Return cumulative maximum over DataFrame axis.

Return cumulative minimum over DataFrame axis.

Return cumulative sum over DataFrame axis.

Return cumulative product over DataFrame axis.

Examples

Series

s = pd.Series([2, np.nan, 5, -1, 0]) s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64

By default, NA values are ignored.

s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64

To include NA values in the operation, use skipna=False

s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64

DataFrame

df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0

To iterate over columns and find the product in each row, use axis=1

df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0