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 operator
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 .
- x : array_like
Only integer and boolean types are handled.
- out : ndarray, 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 freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
- where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
For other keyword-only arguments, see the ufunc docs.
- out : ndarray or scalar
Result. This is a scalar if x is a scalar.
- Return the binary representation of the input number as a string.
bitwise_notis an alias for
>>> np.bitwise_not is np.invert True
 Wikipedia, “Two’s complement”, https://en.wikipedia.org/wiki/Two’s_complement
We’ve seen that 13 is represented by
00001101. 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])