numpy.subtract#

numpy.subtract(x1,x2,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True[,signature])=<ufunc'subtract'>#

Subtract arguments, element-wise.

Parameters:
x1, x2array_like

The arrays to be subtracted from each other.Ifx1.shape!=x2.shape, they must be broadcastable to a commonshape (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 havea shape that the inputs broadcast to. If not provided or None,a freshly-allocated array is returned. A tuple (possible only as akeyword argument) must have length equal to the number of outputs.

wherearray_like, optional

This condition is broadcast over the input. At locations where thecondition is True, theout array will be set to the ufunc result.Elsewhere, theout array will retain its original value.Note that if an uninitializedout array is created via the defaultout=None, locations within it where the condition is False willremain uninitialized.

**kwargs

For other keyword-only arguments, see theufunc docs.

Returns:
yndarray

The difference ofx1 andx2, element-wise.This is a scalar if bothx1 andx2 are scalars.

Notes

Equivalent tox1-x2 in terms of array broadcasting.

Examples

>>>importnumpyasnp>>>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 onndarrays.

>>>x1=np.arange(9.0).reshape((3,3))>>>x2=np.arange(3.0)>>>x1-x2array([[0., 0., 0.],       [3., 3., 3.],       [6., 6., 6.]])
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