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# numpy.float_power

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

  First array elements raised to powers from second array, element-wise.

  Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. This differs from the power function in that integers, float16, and float32 are promoted to floats with a minimum precision of float64 so that the result is always inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers.

  Negative values raised to a non-integral value will return `nan`. To get complex results, cast the input to complex, or specify the `dtype` to be `complex` (see the example below).

  Parameters:
    x1 array_like

      The bases.

    x2 array_like

      The exponents. If `x1.shape != x2.shape`, they must be broadcastable to a common shape (which becomes the shape of the output).

    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

      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 keyword-only arguments, see the ufunc docs.

  Returns:
    y ndarray

      The bases in x1 raised to the exponents in x2. This is a scalar if both x1 and x2 are scalars.

  See also

  `power`

    power function that preserves type

  Examples

      >>> import numpy as np

  Cube each element in a list.

      >>> x1 = range(6)
      >>> x1
      [0, 1, 2, 3, 4, 5]
      >>> np.float_power(x1, 3)
      array([   0.,    1.,    8.,   27.,   64.,  125.])

  Raise the bases to different exponents.

      >>> x2 = [1.0, 2.0, 3.0, 3.0, 2.0, 1.0]
      >>> np.float_power(x1, x2)
      array([  0.,   1.,   8.,  27.,  16.,   5.])

  The effect of broadcasting.

      >>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
      >>> x2
      array([[1, 2, 3, 3, 2, 1],
             [1, 2, 3, 3, 2, 1]])
      >>> np.float_power(x1, x2)
      array([[  0.,   1.,   8.,  27.,  16.,   5.],
             [  0.,   1.,   8.,  27.,  16.,   5.]])

  Negative values raised to a non-integral value will result in `nan` (and a warning will be generated).

      >>> x3 = np.array([-1, -4])
      >>> with np.errstate(invalid='ignore'):
      ...     p = np.float_power(x3, 1.5)
      ...
      >>> p
      array([nan, nan])

  To get complex results, give the argument `dtype=complex`.

      >>> np.float_power(x3, 1.5, dtype=complex)
      array([-1.83697020e-16-1.j, -1.46957616e-15-8.j])