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Classe « DataFrame »

Méthode pandas.DataFrame.product

Signature de la méthode product

def product(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, min_count: 'int' = 0, **kwargs) 

Description

help(DataFrame.product)

Return the product of the values over the requested axis.

Parameters
----------
axis : {index (0), columns (1)}
    Axis for the function to be applied on.
    For `Series` this parameter is unused and defaults to 0.

    .. warning::

        The behavior of DataFrame.prod with ``axis=None`` is deprecated,
        in a future version this will reduce over both axes and return a scalar
        To retain the old behavior, pass axis=0 (or do not pass axis).

    .. versionadded:: 2.0.0

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.

Returns
-------
Series or scalar

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

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