cumprod(x, axis=None, dtype=None, out=None, method='sequential')
This docstring was copied from numpy.cumprod.
Some inconsistencies with the Dask version may exist.
Dask added an additional keyword-only argument method
.
method
method
Arithmetic is modular when using integer types, and no error is raised on overflow.
Input array.
Axis along which the cumulative product is computed. By default the input is flattened.
Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.
Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary.
A new array holding the result is returned unless :None:None:`out` is specified, in which case a reference to out is returned.
Return the cumulative product of elements along a given axis.
>>> a = np.array([1,2,3]) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... np.cumprod(a) # intermediate results 1, 1*2 # doctest: +SKIP
... # total product 1*2*3 = 6 array([1, 2, 6])
>>> a = np.array([[1, 2, 3], [4, 5, 6]]) # doctest: +SKIP
... np.cumprod(a, dtype=float) # specify type of output # doctest: +SKIP array([ 1., 2., 6., 24., 120., 720.])
The cumulative product for each column (i.e., over the rows) of a:
>>> np.cumprod(a, axis=0) # doctest: +SKIP array([[ 1, 2, 3], [ 4, 10, 18]])
The cumulative product for each row (i.e. over the columns) of a:
>>> np.cumprod(a,axis=1) # doctest: +SKIP array([[ 1, 2, 6], [ 4, 20, 120]])See :
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