- class numpy.ufunc#
Functions that operate element by element on whole arrays.
To see the documentation for a specific ufunc, use
info. For example,
np.info(np.sin). Because ufuncs are written in C (for speed) and linked into Python with NumPy’s ufunc facility, Python’s help() function finds this page whenever help() is called on a ufunc.
A detailed explanation of ufuncs can be found in the docs for Universal functions (ufunc).
op(*x[, out], where=True, **kwargs)
Apply op to the arguments *x elementwise, broadcasting the arguments.
The broadcasting rules are:
Dimensions of length 1 may be prepended to either array.
Arrays may be repeated along dimensions of length 1.
- outndarray, None, or tuple of ndarray and None, optional
Alternate array object(s) in which to put the result; if provided, it must have a shape that the inputs broadcast to. A tuple of arrays (possible only as a keyword argument) must have length equal to the number of outputs; use None for uninitialized outputs to be allocated by the ufunc.
- wherearray_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None, locations within it where the condition is False will remain uninitialized.
For other keyword-only arguments, see the ufunc docs.
- rndarray or tuple of ndarray
r will have the shape that the arrays in x broadcast to; if out is provided, it will be returned. If not, r will be allocated and may contain uninitialized values. If the function has more than one output, then the result will be a tuple of arrays.
Call self as a function.
accumulate(array[, axis, dtype, out])
Accumulate the result of applying the operator to all elements.
at(a, indices[, b])
Performs unbuffered in place operation on operand 'a' for elements specified by 'indices'.
outer(A, B, /, **kwargs)
Apply the ufunc op to all pairs (a, b) with a in A and b in B.
reduce(array[, axis, dtype, out, keepdims, ...])
array's dimension by one, by applying ufunc along one axis.
reduceat(array, indices[, axis, dtype, out])
Performs a (local) reduce with specified slices over a single axis.
resolve_dtypes(dtypes, *[, signature, ...])
Find the dtypes NumPy will use for the operation.