Understanding Python’s Mutable Default Argument Problem

In Python, the way default arguments work can sometimes lead to unexpected behavior, especially when using mutable objects like lists or dictionaries. In this article, we’ll explore why mutable default arguments behave in a non-intuitive way and how to avoid these pitfalls.

Problem Demonstration

Consider the following Python code:

def foo(x=[]): 

     x.append(1) 

     return x 

print(foo())         # First call 

print(foo())            # Second call        

At first glance, you might expect that each call to foo() would return [1]. However, running the code produces this output:

[1] [1, 1]        

Why does the second call return [1, 1] instead of [1]? Let’s break down what’s happening step by step.

Step-by-Step Breakdown

1. How Default Arguments Work in Python

In Python, default arguments are evaluated once when the function is defined, not each time the function is called. This is crucial to understanding why the code behaves the way it does.

If a function has a default argument, and that argument is a mutable object (such as a list or a dictionary), any changes made to that object will persist across subsequent function calls. This differs from immutable objects like integers or strings, which do not maintain changes between function calls.

2. First Call to foo()

print(foo())              # First call        

• When you call foo() for the first time, you don’t pass any argument, so the default value x=[] (an empty list) is used.
• Inside the function, x.append(1) is executed. This appends 1 to the list x, making x = [1].
• The function then returns the modified list, so the output of the first call is:

[1]        

At this point, the list x has been modified and now contains [1].

3. Second Call to foo()

print(foo())                # Second call        

• You might expect the second call to foo() to once again use an empty list as the default value for x. However, this is not what happens.
• Instead, because the list x is mutable and was created when the function was first defined, the same list from the first call (which now contains [1]) is reused.
• Inside the function, x.append(1) is executed again. This appends another 1 to the list x, making x = [1, 1].
• The function returns the modified list, so the output of the second call is:

[1, 1]        

The list is not reset between function calls. Instead, the modifications from previous calls persist.

Why Does This Happen?

The root of the problem lies in the fact that Python only evaluates the default argument (x=[]) once, when the function is first defined. Since lists are mutable, any modifications to the list will affect all future calls to the function, unless you explicitly pass a different argument.

Solution: Use None as the Default Argument

To avoid this issue, you can use None as the default argument and initialize the list inside the function. This ensures that a new list is created each time the function is called.

Here’s how you can modify the function:

def foo(x=None): 

         if x is None: 
                
                  x = []     # Create a new list for each function call 
 
          x.append(1) 
          
          return x 

print(foo())                     # Output: [1] 

print(foo())                      # Output: [1]        

Explanation of the Solution:

• Step 1: The default value of x is set to None.
• Step 2: Inside the function, you check if x is None. If it is, a new list (x = []) is created.
• Step 3: The function then proceeds to append 1 to the new list and return it.

Now, each call to foo() creates a fresh list, so the behavior is as expected:

[1] [1]        

Conclusion

Understanding how default arguments work in Python, especially with mutable objects, is crucial to avoid unexpected behavior. The key takeaway is that default arguments are only evaluated once, so changes to mutable objects persist across function calls. Using None as the default value and initializing the mutable object inside the function is a simple and effective way to solve this problem.

By following this approach, you can avoid the pitfalls of mutable default arguments and ensure your functions behave as intended across multiple calls.