What are the common pitfalls when working with Python’s mutable data types?

Don't mutate in Pandas. Don't use `inplace` in Pandas. Check out my Youtube content or my book Effective Pandas for more details.

Accidental modification of mutable data types in Python, such as lists and dictionaries, is a common pitfall. Modifying these objects in place without intending to can lead to unexpected behavior and bugs in your code. Be cautious when using methods that alter the original object, such as append() or extend() for lists, and update() for dictionaries. Instead, consider using immutable alternatives like tuples or employing defensive programming techniques such as creating copies of mutable objects when necessary to prevent unintended modifications.

When defining functions with mutable objects as optional parameters, use "None" as the default value in the function signature and set the default inside the function. This prevents updating the default value with each call. For example: def foo(my_list=[]): my_list.append(0) print(my_list) Calling `foo()` twice prints: [0] [0, 0] To avoid this, redefine `foo`: def foo(my_list=None): my_list = [] if my_list is None else my_list my_list.append(0) print(my_list) Now, `foo()` creates a new list each time it's called, instead of modifying an existing one.

One common pitfall when working with Python's mutable data types, such as lists or dictionaries, is accidental modification. Passing mutable objects as arguments to functions can lead to unintended changes outside the function because the function operates on the same memory reference. To prevent this, explicitly copy mutable objects using methods like .copy() for shallow copies or copy.deepcopy() for deep copies before making modifications, preserving the original data.

Copying mutable objects introduces complexity, as shallow copies may not recursively copy inner objects, leaving nested structures vulnerable to unintended changes. Deep copying addresses this by recursively copying all objects, but it can be memory-intensive and slow. Knowing when to use each method is crucial for effective data management.

Default arguments with mutable types in function definitions can result in unexpected behavior, persisting changes across future function calls. This can lead to challenging-to-trace bugs. To mitigate this issue, employ immutable types like None as default arguments and set mutable objects within the function body if needed.

In Python, variable equality with mutable objects can be tricky. The == operator assesses value equality, while the is operator checks for identity equality, meaning they point to the same object in memory. Misunderstanding this distinction can lead to confusion when comparing objects or checking if variables reference the same object, particularly with copies of mutable types.