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Python

Python Pass by Reference: Explained with Examples

By Filip on 10/05/2024

Learn how to efficiently modify variables within Python functions using the concept of "pass by reference" with illustrative examples.

Python Pass by Reference: Explained with Examples

Table of Contents

Introduction

In Python, the concept of "pass by reference" works a bit differently than in some other programming languages. Let's delve into how Python handles function arguments and what it means for your code.

Step-by-Step Guide

Python doesn't have true "pass by reference" like some other languages. Instead, it uses a system often called "pass by assignment" or "call by object/sharing." Here's how it works:

  1. Variables as References: In Python, variables act like labels stuck onto objects. When you assign a value to a variable, you're not putting the actual data into the variable itself. Instead, the variable holds a reference pointing to the memory location where the data is stored.

  2. Passing Arguments: When you pass an argument to a function, Python creates a new variable inside the function. This new variable receives a copy of the reference from the argument you passed in.

  3. Modification Inside the Function: Now, here's where it gets interesting:

    • Mutable Objects (Lists, Dictionaries, etc.): If the function modifies a mutable object (like a list) using the reference it received, the changes will be reflected outside the function. This is because both the original variable and the function's variable are pointing to the same object in memory.

    • Immutable Objects (Integers, Strings, Tuples): If the function tries to modify an immutable object (like an integer) using the reference, it can't change the original object. Instead, Python creates a new object with the modified value and assigns the function's variable to point to this new object. The original variable outside the function remains unchanged.

In essence:

  • Python passes a copy of the reference, not the actual object.
  • Changes to mutable objects through the reference affect the original.
  • Changes to immutable objects create new objects and don't affect the original.

Simulating Pass-by-Reference:

While Python doesn't have true pass-by-reference, you can achieve similar results:

  • Using Mutable Containers: Wrap the value you want to modify inside a mutable object (like a list). Changes to the element inside the list will be visible outside the function.

  • Returning Modified Values: Have your function return the modified value, and then reassign it to the original variable outside the function.

Key Takeaway:

Understanding Python's pass-by-assignment model is crucial to avoid unexpected behavior when modifying variables inside functions.

Code Example

This Python code demonstrates the concept of pass-by-assignment and how it works with mutable and immutable objects. It shows that modifying a mutable object inside a function affects the original object, while modifying an immutable object does not. The code also provides examples of simulating pass-by-reference using mutable containers and return values.

# Demonstrating Python's Pass-by-Assignment

# Mutable Objects (Lists)
def modify_list(my_list):
  """Modifies the list in-place."""
  my_list.append(4)  # Modifies the original list

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

# Immutable Objects (Integers)
def modify_integer(x):
  """Attempts to modify the integer, but creates a new object."""
  x += 1
  print("Inside function:", x) 

my_number = 10
modify_integer(my_number)
print("Outside function:", my_number)  # Output: 10 (unchanged)

# Simulating Pass-by-Reference with Mutable Containers
def modify_value_in_list(data):
  """Modifies the value inside the list."""
  data[0] = "Modified"

my_data = ["Original"]
modify_value_in_list(my_data)
print(my_data)  # Output: ['Modified']

# Simulating Pass-by-Reference with Return Values
def increment_and_return(x):
  """Increments the value and returns it."""
  return x + 1

my_number = 5
my_number = increment_and_return(my_number)
print(my_number)  # Output: 6

Explanation:

  1. modify_list: The function appends 4 to the list. Since lists are mutable, the change is reflected outside the function.
  2. modify_integer: The function attempts to increment the integer. However, integers are immutable. The function creates a new integer object with the incremented value, but the original variable outside remains unchanged.
  3. modify_value_in_list: This function simulates pass-by-reference by using a list to hold the value. Modifying the element inside the list affects the original data.
  4. increment_and_return: This function demonstrates another way to simulate pass-by-reference by returning the modified value, which is then reassigned to the original variable.

Additional Notes

  • Visual Analogy: Imagine a whiteboard. Variables are like magnets, and objects are like notes. When you assign a value, you're sticking a magnet to a note. Passing an argument is like making a copy of the magnet (reference) and sticking it to the same note.
  • Importance of Mutability: The key difference in behavior arises from whether the object itself can be changed (mutable) or not (immutable). This distinction is crucial for understanding the outcome of function calls.
  • No True Pointers: Unlike C/C++, Python doesn't expose direct memory addresses (pointers) to the programmer. References are managed behind the scenes.
  • Predictable Behavior: While Python's approach might seem different, it leads to consistent and predictable behavior once you understand the underlying mechanism.
  • Alternative Terminology: The terms "call by object" or "call by sharing" are sometimes used to describe Python's system more accurately, emphasizing that objects are shared, not copied, during function calls.
  • Debugging Tip: When working with mutable objects, be mindful that changes made inside a function can have lasting effects outside its scope. Use this to your advantage for efficient data manipulation, but be wary of unintended side effects.
  • Design Choice: Python's pass-by-assignment model contributes to its beginner-friendliness by abstracting away low-level memory management while still providing flexibility for experienced programmers.

Summary

Python doesn't have true "pass by reference." Instead, it uses "pass by assignment" where:

1. Variables are References: Variables in Python act as labels pointing to objects in memory, not containers for the objects themselves.

2. Passing Arguments Creates Copies of References: When passing arguments to functions, Python creates new variables inside the function, holding copies of the references from the original arguments.

3. Modification Depends on Mutability:

  • Mutable Objects (Lists, Dictionaries): Changes made inside the function using the copied reference will affect the original object outside the function.
  • Immutable Objects (Integers, Strings, Tuples): Attempting to modify these inside a function using the copied reference will create a new object, leaving the original object untouched.

Simulating Pass-by-Reference:

  • Wrap values in mutable containers (like lists) to modify them within functions and have the changes reflected outside.
  • Return modified values from functions and reassign them to the original variables.

Key Point: Understanding Python's pass-by-assignment is crucial to avoid unexpected behavior when working with variables inside functions.

Conclusion

In conclusion, while Python might appear to have "pass by reference" at first glance, it actually employs a system called "pass by assignment." This mechanism involves passing copies of references to objects, rather than the objects themselves. The key to understanding this concept lies in the distinction between mutable and immutable objects. Modifications made to mutable objects within a function, using the passed reference, will impact the original object outside the function's scope. Conversely, attempting to modify immutable objects within a function will result in the creation of new objects, leaving the original object untouched. To achieve effects similar to "pass by reference," you can leverage mutable containers like lists or return modified values from functions and reassign them. Mastering Python's pass-by-assignment model is essential for writing predictable and efficient code, especially when dealing with function arguments and variable modifications.

References

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