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# numpy.log2

`numpy.``log2`(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log2'>

  Base-2 logarithm of x.

  Parameters: | x : array_like Input values. 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. **kwargs For other keyword-only arguments, see the ufunc docs.
  Returns: | y : ndarray Base-2 logarithm of x.

  See also

  `log`, `log10`, `log1p`, `emath.log2`

  Notes

  New in version 1.3.0.

  Logarithm is a multivalued function: for each x there is an infinite number of z such that 2**z = x. The convention is to return the z whose imaginary part lies in [-pi, pi].

  For real-valued input data types, `log2` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields `nan` and sets the invalid floating point error flag.

  For complex-valued input, `log2` is a complex analytical function that has a branch cut [-inf, 0] and is continuous from above on it. `log2` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

  Examples

      >>> x = np.array([0, 1, 2, 2**4])
      >>> np.log2(x)
      array([-Inf,   0.,   1.,   4.])

      >>> xi = np.array([0+1.j, 1, 2+0.j, 4.j])
      >>> np.log2(xi)
      array([ 0.+2.26618007j,  0.+0.j        ,  1.+0.j        ,  2.+2.26618007j])