numpy.float_power — NumPy v1.13 Manual (original) (raw)
numpy. float_power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, _extobj_]) = <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.
New in version 1.12.0.
| Parameters: | x1 : array_like The bases. x2 : array_like The exponents. 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 theufunc docs. |
|---|---|
| Returns: | y : ndarray The bases in x1 raised to the exponents in x2. |
See also
power function that preserves type
Examples
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.]])