numpy.sqrt¶

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

Return the non-negative square-root of an array, element-wise.

Parameters:

x:array_like The values whose square-roots are required. 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 An array of the same shape asx, containing the positivesquare-root of each element inx. If any element inx iscomplex, a complex array is returned (and the square-roots ofnegative reals are calculated). If all of the elements inxare real, so isy, with negative elements returningnan.Ifout was provided,y is a reference to it.This is a scalar ifx is a scalar.

See also

lib.scimath.sqrt

A version which returns complex numbers when given negative reals.

Notes

sqrt has–consistent with common convention–as its branch cut thereal “interval” [-inf, 0), and is continuous from above on it.A branch cut is a curve in the complex plane across which a givencomplex function fails to be continuous.

Examples

>>>np.sqrt([1,4,9])array([ 1.,  2.,  3.])

>>>np.sqrt([4,-1,-3+4J])array([ 2.+0.j,  0.+1.j,  1.+2.j])

>>>np.sqrt([4,-1,numpy.inf])array([  2.,  NaN,  Inf])