dask.dataframe.DataFrame.prod

DataFrame.prod(axis=None, skipna=True, split_every=False, dtype=None, out=None, min_count=None, numeric_only=None)

Return the product of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.prod.

Some inconsistencies with the Dask version may exist.

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

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 (Not supported in Dask)

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

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
Series or DataFrame (if level specified)

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