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Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.product.html below:

pandas.DataFrame.product — pandas 2.3.1 documentation

DataFrame.product(axis=0, skipna=True, numeric_only=False, min_count=0, **kwargs)[source]#

Return the product of the values over the requested axis.

Parameters:

axis{index (0), columns (1)}

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, default False

Include only float, int, boolean columns. Not implemented for Series.

min_countint, default 0

The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

Series or scalar

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

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