numpy.float_power¶

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 inx1 to the positionally-corresponding power inx2.x1 andx2 must be broadcastable to the same shape. This differs fromthe power function in that integers, float16, and float32 are promoted tofloats with a minimum precision of float64 so that the result is alwaysinexact. The intent is that the function will return a usable result fornegative 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 havea shape that the inputs broadcast to. If not provided orNone,a freshly-allocated array is returned. A tuple (possible only as akeyword 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, valuesof False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

y : ndarray The bases inx1 raised to the exponents inx2.

See also

power

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]])>>>x2array([[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.]])