In the downloadable file "More on_function" we have these pieces of code (exit is what we obtain where we execute it): FIRST CASE def lengthen_list(n,my_list = [1,2,3]): my_list.append(n) return my_list x = lengthen_list(4)print(x)#exit is [1,2,3,4]x = lengthen_list(4)print(x)#exit is [1,2,3,4,4]x = lengthen_list(4)print(x)#exit is [1,2,3,4,4,4] SECOND CASE def lengthen_list_2(n,my_list = None): if my_list == None: my_list = [1,2,3] my_list.append(n) return my_list y = lengthen_list_2(4) print (y)#exit is [1,2,3,4]y = lengthen_list_2(4) print (y) #exit is [1,2,3,4]y = lengthen_list_2(4) print (y) #exit is [1,2,3,4] Why do we have a difference in exits? It seems that in the second case, my_list gets erased each time we run the code, and this doesn't happen in the first case.

Hey Ariel, Thank you for reaching out and thank you for the great question! This is a Python's peculiarity and is important to keep it in mind when defining functions. It demonstrates the difference in behavior between mutable and immutable objects when applied to function definitions. A mutable object can change its value while keeping its ID number (you can print out the ID of an object by typing id(object_name)). Such are for example lists and dictionaries. Below, you can see an example of a mutable object changing its value and, at the same time, keeping its ID: An immutable object, on the other hand, can't change its value and keep its ID at the same time. Below is an example: In the More_on_functions.py script, the functions sum_and_mult(a, b) and add1(var_3, var_4) demonstrate how immutable objects behave when serving as arguments of a function. For example, you can see that the variable var_3 inside the function and the variable var_3 outside the function bear different values since they are different objects (have different IDs): In contrast, the functions lengthen_list(n,my_list = [1,2,3]) and lengthen_list_2(n,my_list = None) demonstrate how mutable objects behave when serving as arguments of functions. In this example, we can see that my_list and x are, in fact, the same object (have the same IDs): If you want to avoid this accumulation and append to a new list each time, then you'd need to define the function as done in lengthen_list_2(n,my_list = None): Hope this clears up the confusion! Kind regards, 365 Hristina

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Ariel Galo

Last answered:

06 Mar 2023

Posted on:

04 Mar 2023

Why the output varies in these function declatarions?

in Python Programmer Bootcamp / More on functions

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Hristina Hristova

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Posted on:

06 Mar 2023

Why the output varies in these function declatarions?

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