Numpy modify array in place?

If you want to apply mathematical operations to a numpy array in-place, you can simply use the standard in-place operators +=, -=, /=, etc. So for example:

>>> 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])

The in-place version of these operations is a tad faster to boot, especially for larger arrays:

>>> 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

This is a trick that it is slightly more general than the other useful answers here:

def normalize(array, imin = -1, imax = 1):    """I = Imin + (Imax-Imin)*(D-Dmin)/(Dmax-Dmin)"""    dmin = array.min()    dmax = array.max()    array[...] = imin + (imax - imin)*(array - dmin)/(dmax - dmin)

Here we are assigning values to the view array[...] rather than assigning these values to some new local variable within the scope of the function.

x = np.arange(5, dtype='float')print xnormalize(x)print x>>> [0. 1. 2. 3. 4.]>>> [-1.  -0.5  0.   0.5  1. ]

EDIT:

It's slower; it allocates a new array. But it may be valuable if you are doing something more complicated where builtin in-place operations are cumbersome or don't suffice.

def normalize2(array, imin=-1, imax=1):    dmin = array.min()    dmax = array.max()    array -= dmin;    array *= (imax - imin)    array /= (dmax-dmin)    array += iminA = np.random.randn(200**3).reshape([200] * 3)%timeit -n5 -r5 normalize(A)%timeit -n5 -r5 normalize2(A)>> 47.6 ms ± 678 µs per loop (mean ± std. dev. of 5 runs, 5 loops each)>> 26.1 ms ± 866 µs per loop (mean ± std. dev. of 5 runs, 5 loops each)

def normalize(array, imin = -1, imax = 1):    """I = Imin + (Imax-Imin)*(D-Dmin)/(Dmax-Dmin)"""    dmin = array.min()    dmax = array.max()    array -= dmin;    array *= (imax - imin)    array /= (dmax-dmin)    array += imin    print array[0]