xorbits.pandas.DataFrame.product#

DataFrame.product(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.