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What are the implications of Python's pass-by-object-reference (or pass-by-assignment) calling convention?
What are the implications of Python's pass-by-object-reference (or pass-by-assignment) calling convention?
Jun 13, 2025 am 12:20 AM
The Python function parameter transfer mechanism is "passed by object reference" or "passed by assignment", which means that the variable is passed reference to the object, rather than actual value or direct copying. 1. If an immutable object (such as integers, strings, tuples), modifications within the function will create a new object, and the original object remains unchanged; 2. If a mutable object (such as lists, dictionaries), modifications within the function will affect the original object; 3. If a mutable object is reassigned within the function, the connection from the original object will be disconnected and the original object remains unchanged; 4. Practical suggestions include distinguishing between mutable and immutable types, avoiding the copy when accidentally modified, and understanding the impact of reassignment within the function.
Python's function argument passing mechanism is often described as "pass-by-object-reference" or sometimes "pass-by-assignment." This means that when you pass a variable to a function, you're not passing the actual value or a direct copy of the variable, but rather a reference to the object that the variable points to. However, how this behaves in practice can be confusing, especially if you're coming from languages ??like C or Java.
Here's what you need to know about how this affects your code and why it matters.
Immutable Objects Don't Change Outside the Function
When you pass something like an integer, string, or tuple (which are immutable types) into a function, any attempt to change their value inside the function will result in the creation of a new object. The original object outside the function remains unchanged.
For example:
def modify(x):
x = 100
a = 5
modify(a)
print(a) # Still prints 5 Even though x inside the function gets assigned 100, that doesn't affect a outside because integers are immutable. Python creates a new reference for x , leaving the original untouched.
This behavior can help avoid accidental data corruption, but it might also confuse people expecting in-place modifications.
Mutable Objects Can Be Changed In Place
With mutable objects like lists or dictionaries, things work differently. Since they can be modified in place, changes made inside the function will affect the original object.
def add_item(lst):
lst.append(42)
my_list = [1, 2, 3]
add_item(my_list)
print(my_list) # Now prints [1, 2, 3, 42] Here, lst and my_list refer to the same list object. Appending to the list modifyes the shared object, so the change is visible outside the function.
If you don't want this behavior, make a copy before passing:
- Use
list.copy()for lists - Use
dict.copy()for dictionaries - Or slice:
new_list = original[:]
This distinction between mutable and immutable types is central to understanding how Python handles function arguments.
Assignment Inside Functions May Break the Link
Another important detail: if you reassign a mutable object inside a function, it breaks the link to the original object.
def replace_list(lst):
lst = [9, 8, 7] # Reassignment breaks the connection
original = [1, 2, 3]
replace_list(original)
print(original) # Still prints [1, 2, 3] In this case, even though lst started pointing to the same list as original , the assignment lst = [9, 8, 7] makes it point to a new list. The original list remains unchanged.
So:
- Modifying in place affects the original
- Reassigning does not
This nuance trips up many developers, especially when debugging functions that seem like they should modify data but don't.
Practical Takeaways
To avoid confusion:
- Know whether you're working with mutable or immutable types
- If you don't want a function to modify a mutable object, pass a copy
- Understand that reassigning variables inside functions breaks the connection to the original object
It's not magic — just how references and assignments work in Python.
Basically that's it.
The above is the detailed content of What are the implications of Python's pass-by-object-reference (or pass-by-assignment) calling convention?. For more information, please follow other related articles on the PHP Chinese website!
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