pandas.Series.prod#

Series.prod(axis=None, skipna=True, level=None, numeric_only=None, min_count=0, **kwargs)[source]#

Return the product of the values over the requested axis.

Parameters

axis{index (0)}: Axis for the function to be applied on.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. 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

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

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

pandas.Series.pow

pandas.Series.product