numpy.prod

2022-08-18 14:21:34 浏览数 (1)

numpy.prod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]

Return the product of array elements over a given axis.

Parameters:

a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the elements in the input array. If axis is negative it counts from the last to the first axis. New in version 1.7.0. If axis is a tuple of ints, a product is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before. dtype : dtype, optional The type of the returned array, as well as of the accumulator in which the elements are multiplied. The dtype of a is used by default unless a has an integer dtype of less precision than the default platform integer. In that case, if a is signed then the platform integer is used while if a is unsigned then an unsigned integer of the same precision as the platform integer is used. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the prod method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised. initial : scalar, optional The starting value for this product. See reduce for details. New in version 1.15.0. where : array_like of bool, optional Elements to include in the product. See reduce for details. New in version 1.17.0.

Returns:

product_along_axis : ndarray, see dtype parameter above. An array shaped as a but with the specified axis removed. Returns a reference to out if specified.

See also

ndarray.prod equivalent method

numpy.doc.ufuncs

Section “Output arguments”

Notes

Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32-bit platform:

代码语言：javascript复制
>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x)
16 # may vary

The product of an empty array is the neutral element 1:

代码语言：javascript复制
>>> np.prod([])
1.0

Examples

By default, calculate the product of all elements:

代码语言：javascript复制
>>> np.prod([1.,2.])
2.0

Even when the input array is two-dimensional:

代码语言：javascript复制
>>> np.prod([[1.,2.],[3.,4.]])
24.0

But we can also specify the axis over which to multiply:

代码语言：javascript复制
>>> np.prod([[1.,2.],[3.,4.]], axis=1)
array([  2.,  12.])

Or select specific elements to include:

代码语言：javascript复制
>>> np.prod([1., np.nan, 3.], where=[True, False, True])
3.0

If the type of x is unsigned, then the output type is the unsigned platform integer:

代码语言：javascript复制
>>> x = np.array([1, 2, 3], dtype=np.uint8)
>>> np.prod(x).dtype == np.uint
True

If x is of a signed integer type, then the output type is the default platform integer:

代码语言：javascript复制
>>> x = np.array([1, 2, 3], dtype=np.int8)
>>> np.prod(x).dtype == int
True

You can also start the product with a value other than one:

代码语言：javascript复制
>>> np.prod([1, 2], initial=5)
10
linux mapreduce

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