pandas.Series.product#
- Series.product(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. For Series this parameter is unused and defaults to 0.
- 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.
Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
- 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.
Deprecated since version 1.5.0: Specifying
numeric_only=None
is deprecated. The default value will beFalse
in a future version of pandas.- 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
- scalar or Series (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