I have a class that contains two object instance variables.

import numpy as np
class test(object):
    def __init__(self):
        self.a=np.array((1,2))
        self.b=np.array((1,2))

I need a method that does similar operations to each variable. I thought that I could loop over the variable and it would change the values in place. However, when i execute the numpy insert and assign the value to a, it does not change self.a.

    def indirect_method(self):
        arrs = [self.a, self.b]
        for a in arrs:
            a = np.insert(l,1,3)
t=test()

t.indirect_method()

t.a
Out[82]: array([1, 2])

t.b
Out[83]: array([1, 2])

If I print the ID of self.a and a, initially they are the same, but after the insert operation the ID of a changes.

    def indirect_method(self):
        arrs = [self.a,]
        print(id(self.a))
        for a in arrs:
            print(id(a))
            a = np.insert(l,1,3)
            print(id(a))
            

t=test()

t.indirect_method()

4756930896
4756930896
4744668432

If I modify the object variables directly it works fine.

    def direct_method(self):
        self.a = np.insert(self.a,1,3)
        self.b = np.insert(self.b,1,3)

t=test()

t.direct_method()

t.a
Out[77]: array([1, 3, 2])

t.b
Out[78]: array([1, 3, 2])

How can I update self.a and self.b through an intermediary variable? This would save a lot of duplicated code since the only part of my method that needs to change is the location where the data is inserted.

I have a class that contains two object instance variables.

import numpy as np
class test(object):
    def __init__(self):
        self.a=np.array((1,2))
        self.b=np.array((1,2))

I need a method that does similar operations to each variable. I thought that I could loop over the variable and it would change the values in place. However, when i execute the numpy insert and assign the value to a, it does not change self.a.

    def indirect_method(self):
        arrs = [self.a, self.b]
        for a in arrs:
            a = np.insert(l,1,3)
t=test()

t.indirect_method()

t.a
Out[82]: array([1, 2])

t.b
Out[83]: array([1, 2])

If I print the ID of self.a and a, initially they are the same, but after the insert operation the ID of a changes.

    def indirect_method(self):
        arrs = [self.a,]
        print(id(self.a))
        for a in arrs:
            print(id(a))
            a = np.insert(l,1,3)
            print(id(a))
            

t=test()

t.indirect_method()

4756930896
4756930896
4744668432

If I modify the object variables directly it works fine.

    def direct_method(self):
        self.a = np.insert(self.a,1,3)
        self.b = np.insert(self.b,1,3)

t=test()

t.direct_method()

t.a
Out[77]: array([1, 3, 2])

t.b
Out[78]: array([1, 3, 2])

How can I update self.a and self.b through an intermediary variable? This would save a lot of duplicated code since the only part of my method that needs to change is the location where the data is inserted.

• python

• When you do a = np.insert(...), that's just changing which object local variable a is bound to. It doesn't change the original object. To do what you're asking, you should use a dictionary with two elements, 'a' and 'b'. – Tim Roberts Commented Nov 21, 2024 at 4:44
• This question is similar to: How to change the values of instance attributes in a loop. If you believe it’s different, please edit the question, make it clear how it’s different and/or how the answers on that question are not helpful for your problem. – Anerdw Commented Nov 21, 2024 at 6:13
• No, I believe it is similar. If I saw that one when reviewing the question before posting, I was not following. Now it’s coming together. – Echo6 Commented Nov 21, 2024 at 14:27

1 Answer 1

I think this should work fine.

import numpy as np
class test(object):
    def __init__(self):
        self.a=np.array((1,2))
        self.b=np.array((1,2))

    def indirect_method(self):
        for el in ['a', 'b']:
            if hasattr(self, el):
                setattr(self, el, np.array((1,2,3,4,5)))

x = test()
x.indirect_method()
print(x.a) ## output [1 2 3 4 5]