Python: shallow copy versus deep copy

It is a common practice in programming to use a variable as template for another and then later changing the value of the new variable, something like

b=a

b = 3

at this point, variable a still has its old value, it was not replaced by 3. So far so good. This works in python like other programming languages, but what about compound objects (compound objects contain other objects = lists, dictionary, tuple)? Compound object will NOT work like this.

Lets have a closer look into problem. We can check the memory address of a given variable like this:

print(id(a), id(b))

the id() function returns memory address of its variable so one can instantly check if the two are pointing to the same address or not.

In case of compound variables, the situation is different as we start from a shallow copy:

x1 = [“a”, “b”]
>>> x2 = x1
>>> print(x1)
[‘a’, ‘b’]
>>> print(x2)
[‘a’, ‘b’]
>>> print(id(x1),id(x2))
43746416 43746416

> x2 = [“c”, “d”]
>>> print(x1)
[‘a’, ‘b’]
>>> print(x2)
[‘c’, ‘d’]
>>> print(id(x1),id(x2))
43746416 43875200

x1 and x2 are originally pointing to the same memory address but as soon as one variable is modifies, it allocates a new memory address: this is a shallow copy. It is so because our list in this example is not nested. In this case if we change only one element of x2, x1 will be modified as well:

>>> x1 = [“a”, “b”]
>>> x2 = x1
>>> x2[1] = “h”
>>> print(x1)
[‘a’, ‘h’]
>>> print(x2)
[‘a’, ‘h’]

x1 and x2 are originally pointing to the same memory address but as soon as one variable is modifies, it allocates a new memory address: this is a shallow copy. It is so because our list in this example is not nested. Now if we change only one element of x2, x1 will be modified as well:

>> x1 = [“a”, “b”]
>>> x2 = x1
>>> x2[1] = “h”
>>> print(x1)
[‘a’, ‘h’]
>>> print(x2)
[‘a’, ‘h’]

be careful !

we had actually two names for one variable, it was just a shallow copy, we did not assign a new object to x2. To avoid this problem, use deep copy:

from copy import deepcopy

x2 = deepcopy(x1)

 

hopefully it helps to prevent confusing errors in your python codes.

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Debian, Fedora, Kubuntu

I use Debain since Sarge. Back then, mounting a USB disk was far from trivial. It is so much user friendly now. For a while between 2014-2016, I have used Fedora and Kubuntu instead. Both are more appropriate if you have a modern laptop as they are faster in incorporating new kernels in their distributions (compare a life cycle of 6 month vs 2 years). This particularly affects things like sound, LAN, and wireless. Without an internet connection, it is difficult to update the system in first place, if it cannot recognize your network interface. I have to mention that using geeky tricks, it is doable to get a work around solution, as I usually do not get the bleeding edge new laptops. However a more standard solution is to live with either Fedora or Kubuntu, and a year later install Debian: by then, the latest (testing) kernels usually are modern enough to support the main functions like network.

Doing science and mathematical calculations, it is frustrating to see that a system crashes every now and then for nonsense reasons: Trash crashed, Copy crashed, …. these stuff I have never ever experienced on a  stable Debian system, so the least is that I spent a week to get everything working with Debian before giving it up.

There are also some more fundamental differences in philosophy of Debian versus Fedora or Kubuntu. While you get bug fix and minimal updates for stable software, an update never actually changes a package dramatically. So if you want to see a new functionalities, it does not arrive before a major release. Debian is very stable and it makes it very interesting for systems with heavy jobs.

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