numpy.subtract¶

numpy.
subtract
(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'subtract'>¶ Subtract arguments, elementwise.
 Parameters
 x1, x2array_like
The arrays to be subtracted from each other. 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
 yndarray
The difference of x1 and x2, elementwise. This is a scalar if both x1 and x2 are scalars.
Notes
Equivalent to
x1  x2
in terms of array broadcasting.Examples
>>> np.subtract(1.0, 4.0) 3.0
>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.subtract(x1, x2) array([[ 0., 0., 0.], [ 3., 3., 3.], [ 6., 6., 6.]])
The

operator can be used as a shorthand fornp.subtract
on ndarrays.>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1  x2 array([[0., 0., 0.], [3., 3., 3.], [6., 6., 6.]])