numpy.equal¶

numpy.
equal
(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'equal'>¶ Return (x1 == x2) elementwise.
 Parameters
 x1, x2array_like
Input arrays. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output). outndarray, None, or tuple of ndarray and None, optional
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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 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. **kwargs
For other keywordonly arguments, see the ufunc docs.
 Returns
 outndarray or scalar
Output array, elementwise comparison of x1 and x2. Typically of type bool, unless
dtype=object
is passed. This is a scalar if both x1 and x2 are scalars.
See also
Examples
>>> 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:
>>> np.equal(1, np.ones(1)) array([ True])
The
==
operator can be used as a shorthand fornp.equal
on ndarrays.>>> a = np.array([2, 4, 6]) >>> b = np.array([2, 4, 2]) >>> a == b array([ True, True, False])