Stop Python Functions Overwriting Inputs

Question

I have a simple function that is supposed to run down the diagonal of an array and turn all the values to 0.

def diagonal_zeros(dataset):
    zero = dataset[:]
    length = len(zero)
    for i in range(length):
        zero[i, i] = 0
    return zero

When I run this function on an array, it outputs the new, correct 'zero' array, but it also goes back and overwrites the original 'dataset.' I had thought that the line zero = dataset[:] would have prevented this.

I do not, however, get the same behavior with this function:

def seperate_conditions(dataset, first, last):
    dataset = dataset[first:last, :]
    return dataset

Which leaves the first dataset unchanged. I've been reading StackOverflow answers to related questions, but I cannot for the life of me figure this out. I'm working on a scientific analysis pipeline so I really want to be able to refer back to the matrices at every step.

Thanks

You are using numpy, I presume. numpy slicing returns a view, not a copy. — juanpa.arrivillaga
– juanpa.arrivillaga, Commented Nov 17, 2017 at 17:11

g.visona · Accepted Answer · 2017-11-20 14:25:50Z

6

Arguments in python are passed by assignment (thanks to @juanpa.arrivillaga for the correction) and not by value. This means that generally the function does not recieve a copy of the argument, but a "pointer" to the argument itself. If you alter the object referenced by the argument in the function, you are modifying the same object outside. Here's a page with some more information.

A possibility is to use the copy module inside your function, to create a copy of the dataset.

As an example, for your code:

import copy
myDataset = [[1,2,3],[2,3,4],[3,4,5]]

def diagonal_zeros(dataset):
    zero = copy.deepcopy(dataset)
    length = len(zero)
    for i in range(length):
        zero[i][i] = 0
    return zero

result = diagonal_zeros(myDataset)
print(result)     #[[0, 2, 3], [2, 0, 4], [3, 4, 0]]
print(myDataset)  #[[1, 2, 3], [2, 3, 4], [3, 4, 5]]

This article helped me a lot with this concept.

edited Nov 20, 2017 at 14:25

answered Nov 17, 2017 at 17:02

g.visona

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2 Comments

g.visona Over a year ago

@juanpa.arrivillaga you are right, I edited the comment for clarity.

user 88 91 Over a year ago

the same applies to VBS (and probably many other languages) as well, I found this while searching for the same issue for VB and this explains the issue perfectly. You could possibly point out that it applies to other languages than just python

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