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Python

Pass by Reference in Python: Explained with Examples

By Filip on 10/05/2024

Learn how to modify Python function arguments directly using pass-by-assignment and mutable data types for efficient code.

Pass by Reference in Python: Explained with Examples

Table of Contents

Introduction

Understanding how Python handles function arguments is fundamental to writing effective code. Unlike some languages with "pass by value" or "pass by reference," Python uses a distinct approach known as "pass by assignment." This concept centers around the idea that variables in Python act like labels attached to objects, rather than containers storing values.

Step-by-Step Guide

Python doesn't quite follow the traditional "pass by value" or "pass by reference" models you might see in languages like C++ or Java. Instead, it uses a mechanism often called "pass by assignment" or "call by object/sharing." Here's how it works:

  1. Think of variables as labels: In Python, variables are more like labels stuck on objects rather than containers holding values. When you assign a value to a variable, you're attaching that label to the object representing that value.

  2. Passing arguments to functions: When you pass an argument to a function, Python creates a new label inside the function, pointing to the same object the original variable points to.

  3. Modifying mutable objects: If the object is mutable (like a list or dictionary), changes made inside the function directly affect the original object because both labels point to the same object in memory.

  4. Reassigning inside the function: If you reassign a variable inside the function, you're simply making that label point to a different object. The original variable outside the function remains unaffected.

Here's an analogy:

Imagine you have a dog named "Buddy." You tell your friend, "This is Buddy." You haven't given your friend your dog; you've given them a way to refer to your dog. If your friend teaches Buddy a trick, Buddy learns it regardless of who asked. However, if your friend decides to call their cat "Buddy," it doesn't change the fact that your dog is still named "Buddy."

Key takeaways:

  • Mutable objects (lists, dictionaries) can be modified in place within functions.
  • Reassigning a variable inside a function does not affect the original variable.
  • To achieve effects similar to "pass by reference," you can return values from functions and reassign them outside the function.

While Python's approach might seem different, understanding this "pass by assignment" concept is crucial for writing correct and predictable code.

Code Example

This code demonstrates how Python handles function arguments, specifically illustrating the difference between modifying mutable objects versus reassigning variables. It shows that changes to mutable objects inside a function affect the original object, while reassigning a variable inside a function only affects the local copy. It also provides an example of simulating "pass by reference" by returning a modified value and reassigning it to the original variable.

# Example 1: Modifying a mutable object (list)

def add_item(my_list):
  """Adds an item to the input list."""
  my_list.append("New Item")

my_items = ["Apple", "Banana"]
add_item(my_items)
print(my_items)  # Output: ['Apple', 'Banana', 'New Item'] 

# The 'add_item' function modifies the original list because 
# both 'my_list' (inside the function) and 'my_items' 
# (outside the function) point to the same list object.


# Example 2: Reassigning a variable inside a function

def change_name(name):
  """Changes the value of the input name."""
  name = "Bob"

original_name = "Alice"
change_name(original_name)
print(original_name)  # Output: Alice

# Reassigning 'name' inside the function only affects the local 
# variable 'name'. It doesn't change the value of 'original_name'.


# Example 3: Simulating "pass by reference" using return values

def get_doubled_list(numbers):
  """Returns a new list with doubled values."""
  doubled_numbers = [num * 2 for num in numbers]
  return doubled_numbers

my_numbers = [1, 2, 3]
my_numbers = get_doubled_list(my_numbers) 
print(my_numbers)  # Output: [2, 4, 6]

# To achieve an effect similar to "pass by reference," we return 
# the modified list and reassign it to the original variable.

Explanation:

  • Example 1 demonstrates how changes made to a mutable object inside a function affect the original object outside the function.
  • Example 2 shows that reassigning a variable inside a function doesn't affect the original variable outside.
  • Example 3 illustrates how to simulate the effect of "pass by reference" by returning a value from the function and reassigning it to the original variable.

Additional Notes

  • Importance of Mutability: The distinction between mutable and immutable objects is key to understanding Python's parameter passing. Focus on how this difference affects whether changes inside a function are reflected outside.

  • Visualizations: Diagrams showing variables as labels pointing to objects in memory can be incredibly helpful in understanding this concept. Search online for "Python pass by assignment diagram" for visual explanations.

  • Common Pitfalls: Beginners often stumble when trying to modify immutable objects (like integers, strings, tuples) in-place within functions. Emphasize that you need to return the modified value and reassign it outside the function for such cases.

  • Alternative Terminology: "Call by object/sharing" is a more accurate description than "pass by assignment." It highlights that functions receive their own references to objects, and changes to those objects are shared if they're mutable.

  • Practical Implications: Understanding this mechanism helps avoid unexpected behavior and write more efficient code. For instance, passing large data structures by assignment is faster than creating copies.

  • Deeper Dive: Explore the concepts of "references" and "object identity" in Python for a more comprehensive understanding. The id() function can be useful for inspecting object identity.

Summary

Python doesn't strictly adhere to "pass by value" or "pass by reference." Instead, it employs "pass by assignment." Here's a breakdown:

Core Concept:

  • Variables as Labels: Python variables act like labels attached to objects, not containers storing values.

How it Works:

  1. Function Arguments: When passing arguments, Python creates a new label inside the function, pointing to the same object as the original variable.
  2. Mutable Objects: Changes to mutable objects (e.g., lists) inside the function affect the original object because both labels reference the same memory location.
  3. Reassignment: Reassigning a variable inside a function only changes what object that local label points to, leaving the original variable untouched.

Analogy:

Think of telling a friend your dog's name. They now have a way to refer to your dog, but it's still your dog. Teaching the dog a trick affects the dog regardless of who asked. However, your friend calling their cat by the same name doesn't change your dog's name.

Key Points:

  • Mutable objects can be modified in place within functions.
  • Reassigning variables inside functions doesn't affect the original variables.
  • To mimic "pass by reference," return values from functions and reassign them outside.

Understanding "pass by assignment" is crucial for writing predictable Python code.

Conclusion

In conclusion, while Python might appear to use a familiar "pass by value" or "pass by reference" mechanism, it actually employs a distinct approach: "pass by assignment." Grasping this concept is essential for Python programmers. Remember that variables act like labels stuck to objects. When passing arguments to functions, Python creates a new label inside the function, pointing to the same object as the original variable. Modifications to mutable objects within the function affect the original object, while reassigning a variable inside the function only affects the local label. To emulate "pass by reference," return the modified value and reassign it outside the function. By understanding "pass by assignment," you can write more predictable and efficient Python code.

References

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