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numpy.ufunc
class numpy.ufunc[source]-
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).
Calling ufuncs:
op(*x[, out], where=True, **kwargs)Apply
opto the arguments*xelementwise, 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.
- Parameters
-
*xarray_like-
Input arrays.
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
outarray will be set to the ufunc result. Elsewhere, theoutarray will retain its original value. Note that if an uninitializedoutarray is created via the defaultout=None, locations within it where the condition is False will remain uninitialized. - **kwargs
-
For other keyword-only arguments, see the ufunc docs.
- Returns
-
rndarray or tuple of ndarray-
rwill have the shape that the arrays inxbroadcast to; ifoutis provided, it will be returned. If not,rwill be allocated and may contain uninitialized values. If the function has more than one output, then the result will be a tuple of arrays.
- Attributes
Methods
__call__(*args, **kwargs)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
opto all pairs (a, b) with a inAand b inB.reduce(array[, axis, dtype, out, keepdims, …])Reduces
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.
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Licensed under the 3-clause BSD License.
https://numpy.org/doc/1.20/reference/generated/numpy.ufunc.html