xorbits.pandas.DataFrame.prod#
- DataFrame.prod(axis=None, skipna=True, level=None, min_count=0, numeric_only=None, combine_size=None, method=None)#
Return the product of the values over the requested axis.
- 参数
axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
For DataFrames, specifying
axis=None
will apply the aggregation across both axes.2.0.0(pandas) 新版功能.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
min_count (int, 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.
- 返回类型
Series or scalar
参见
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.
实际案例
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
This docstring was copied from pandas.core.frame.DataFrame.