numpy修改数组到位？

如果你想数学运算适用于就地一个numpy的数组，你可以简单地使用标准就地运营商

+=

-=

/=

>>> def foo(a):...     a += 10... >>> a = numpy.arange(10)>>> aarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])>>> foo(a)>>> aarray([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

这些 *** 作的就地版本启动起来有点快，特别是对于较大的阵列：

>>> def normalize_inplace(array, imin=-1, imax=1):...         dmin = array.min()...         dmax = array.max()...         array -= dmin...         array *= imax - imin...         array /= dmax - dmin...         array += imin...     >>> def normalize_copy(array, imin=-1, imax=1):...         dmin = array.min()...         dmax = array.max()...         return imin + (imax - imin) * (array - dmin) / (dmax - dmin)... >>> a = numpy.arange(10000, dtype='f')>>> %timeit normalize_inplace(a)10000 loops, best of 3: 144 us per loop>>> %timeit normalize_copy(a)10000 loops, best of 3: 146 us per loop>>> a = numpy.arange(1000000, dtype='f')>>> %timeit normalize_inplace(a)100 loops, best of 3: 12.8 ms per loop>>> %timeit normalize_copy(a)100 loops, best of 3: 16.4 ms per loop