equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Input arrays. If x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs>
.
Output array, element-wise comparison of x1
and :None:None:`x2`. Typically of type bool, unless dtype=object
is passed. This is a scalar if both x1
and :None:None:`x2` are scalars.
Return (x1 == x2) element-wise.
>>> np.equal([0, 1, 3], np.arange(3)) array([ True, True, False])
What is compared are values, not types. So an int (1) and an array of length one can evaluate as True:
This example is valid syntax, but we were not able to check execution>>> np.equal(1, np.ones(1)) array([ True])
The ==
operator can be used as a shorthand for np.equal
on ndarrays.
>>> a = np.array([2, 4, 6])See :
... b = np.array([2, 4, 2])
... a == b array([ True, True, False])
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.core.less
numpy.ma.core.greater_equal
numpy.ma.core.not_equal
numpy.ma.core.less_equal
numpy.ma.core.greater
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