prod(self, axis=None, skipna=True, level=None, numeric_only=None, min_count=0, **kwargs)
Axis for the function to be applied on.
Exclude NA/null values when computing the result.
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
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.
Additional keyword arguments to be passed to the function.
Return the product of the values over the requested axis.
DataFrame.idxmax
Return the index of the maximum over the requested axis.
DataFrame.idxmin
Return the index of the minimum over the requested axis.
DataFrame.max
Return the maximum over the requested axis.
DataFrame.min
Return the minimum over the requested axis.
DataFrame.sum
Return the sum over the requested axis.
Series.idxmax
Return the index of the maximum.
Series.idxmin
Return the index of the minimum.
Series.max
Return the maximum.
Series.min
Return the minimum.
Series.sum
Return the sum.
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() 1.0This example is valid syntax, but we were not able to check execution
>>> pd.Series([np.nan]).prod(min_count=1) nanSee :
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