invert(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'invert'>¶
Compute bit-wise inversion, or bit-wise NOT, element-wise.
Computes the bit-wise NOT of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
For signed integer inputs, the two’s complement is returned. In a two’s-complement system negative numbers are represented by the two’s complement of the absolute value. This is the most common method of representing signed integers on computers . A N-bit two’s-complement system can represent every integer in the range to .
Only integer and boolean types are handled.
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 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
For other keyword-only arguments, see the ufunc docs.
Result. This is a scalar if x is a scalar.
Return the binary representation of the input number as a string.
bitwise_not is an alias for
>>> np.bitwise_not is np.invert True
We’ve seen that 13 is represented by
The invert or bit-wise NOT of 13 is then:
>>> x = np.invert(np.array(13, dtype=np.uint8)) >>> x 242 >>> np.binary_repr(x, width=8) '11110010'
The result depends on the bit-width:
>>> x = np.invert(np.array(13, dtype=np.uint16)) >>> x 65522 >>> np.binary_repr(x, width=16) '1111111111110010'
When using signed integer types the result is the two’s complement of the result for the unsigned type:
>>> np.invert(np.array(, dtype=np.int8)) array([-14], dtype=int8) >>> np.binary_repr(-14, width=8) '11110010'
Booleans are accepted as well:
>>> np.invert(np.array([True, False])) array([False, True])