Subclassing numpy ndarray problem

The reason you are not getting the result you expect is because you are re-assigning self within the method remove_some. You are just creating a new local variable self. If your array shape were not to change, you could simply do self[:] = ... and you could keep the reference to self and all would be well, but you are trying to change the shape of self. Which means we need to re-allocate some new memory and change where we point when we refer to self.

I don't know how to do this. I thought it could be achieved by __array_finalize__ or __array__ or __array_wrap__. But everything I've tried is falling short.

Now, there's another way to go about this that doesn't subclass ndarray. You can make a new class that keeps an attribute that is an ndarray and then override all the usual __add__, __mul__, etc.. Something like this:

Class Data(object):    def __init__(self, inarr):        self._array = np.array(inarr)    def remove_some(x):        self._array = self._array[x]    def __add__(self, other):        return np.add(self._array, other)

Well, you get the picture. It's a pain to override all the operators, but in the long run, I think more flexible.

You'll have to read this thoroughly to do it right. There are methods like __array_finalize__ that need to be called a the right time to do "cleanup".

Perhaps make this a function, rather than a method:

import numpy as npdef remove_row(arr,col,val):    return arr[arr[col]!=val]z = np.array([(1,2,3), (4,5,6), (7,8,9)],    dtype=[('a', int), ('b', int), ('c', int)])z=remove_row(z,'a',4)print(repr(z))# array([(1, 2, 3), (7, 8, 9)], #       dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<i4')])

Or, if you want it as a method,

import numpy as npclass Data(np.ndarray):    def __new__(cls, inputarr):        obj = np.asarray(inputarr).view(cls)        return obj    def remove_some(self, col, val):        return self[self[col] != val]z = np.array([(1,2,3), (4,5,6), (7,8,9)],    dtype=[('a', int), ('b', int), ('c', int)])d = Data(z)d = d.remove_some('a', 4)print(d)

The key difference here is that remove_some does not try to modify self, it merely returns a new instance of Data.

I tried to do the same, but it is really very complex to subclass ndarray.

If you only have to add some functionality, I would suggest to create a class which stores the array as attribute.

class Data(object):    def __init__(self, array):        self.array = array    def remove_some(self, t):        //operate on self.array        passd = Data(z)print(d.array)