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Module « numpy.matlib »

Fonction prod - module numpy.matlib

Signature de la fonction prod

def prod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>) 

Description

help(numpy.matlib.prod)

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.

    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 `~numpy.ufunc.reduce`
    for details.
where : array_like of bool, optional
    Elements to include in the product. See `~numpy.ufunc.reduce`
    for details.

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
:ref:`ufuncs-output-type`

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

>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x)
16 # may vary

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

>>> np.prod([])
1.0

Examples
--------
By default, calculate the product of all elements:

>>> import numpy as np
>>> np.prod([1.,2.])
2.0

Even when the input array is two-dimensional:

>>> a = np.array([[1., 2.], [3., 4.]])
>>> np.prod(a)
24.0

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

>>> np.prod(a, axis=1)
array([  2.,  12.])
>>> np.prod(a, axis=0)
array([3., 8.])

Or select specific elements to include:

>>> 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:

>>> 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:

>>> 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:

>>> np.prod([1, 2], initial=5)
10

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