Backend Development
Python Tutorial
How does Python handle mutable default arguments in functions, and why can this be problematic?
How does Python handle mutable default arguments in functions, and why can this be problematic?
Jun 14, 2025 am 12:27 AM
Python's default parameters are only initialized once when defined. If mutable objects (such as lists or dictionaries) are used as default parameters, unexpected behavior may be caused. For example, when using an empty list as the default parameter, multiple calls to the function will reuse the same list instead of generating a new list each time. Problems caused by this behavior include: 1. Unexpected sharing of data between function calls; 2. The results of subsequent calls are affected by previous calls, increasing the difficulty of debugging; 3. It causes logical errors and is difficult to detect; 4. It is easy to confuse both novice and experienced developers. To avoid problems, the best practice is to set the default value to None and create a new object inside the function, such as using my_list=None instead of my_list=[] and initializing the list in the function. In addition, it is necessary to carefully determine whether shared state is required, explicitly declare intentions and clearly document API behavior.
Python's handling of mutable default arguments in function definitions can be a bit tricky, and if you're not aware of how it works, it can lead to unexpected behavior.
The issue comes from using a mutable object — like a list or dictionary — as a default argument in a function definition. The key point is that default arguments are evaluated only once , when the function is defined, not each time the function is called.
This might seem like a small detail, but it can cause bugs that are hard to track down.
What happens when you use a mutable default argument?
Let's look at a common example:
def add_item(item, my_list=[]):
my_list.append(item)
return my_list If you call this function multiple times without providing my_list , like this:
print(add_item('a')) # ['a'] print(add_item('b')) # ['a', 'b']
You might expect each call to start with a new empty list, but instead, the same list is reused across all calls.
Why? Because the default value [] was created once when the function was defined, not each time it's called.
Why is this behavior problematic?
This behavior becomes an issue because it goes against what most people actually expect. When writing functions, we usually think of default values ??as being set fresh every time the function runs.
Here are some specific problems this causes:
Accidental data sharing between function calls
One call can affect the result of later calls, which makes debugging harder.Hard-to-catch logic errors
You might spend time chasing down why your list keeps growing even though you didn't intend it to.Confusion for beginners (and sometimes pros)
This is a classic gotcha in Python interviews and real-world code alike.
How to avoid the problem
To prevent this kind of behavior, a common best practice is to use None as the default value and create a new mutable object inside the function:
def add_item(item, my_list=None):
if my_list is None:
my_list = []
my_list.append(item)
return my_list Now calling the function without my_list will correctly give you a fresh list each time.
Other tips:
- Always consider whether a mutable default makes sense for your function.
- If you do want shared state, make it explicit — don't rely on this hidden behavior.
- Document the intended behavior clearly, especially if you're writing a library or API.
In short, Python evaluates default arguments once, which is fine for immutable types like numbers or strings, but leads to surprises with mutable ones. Avoid the trap by using None as a placeholder and initializing the object inside the function.
That's basically how it works — not complicated, but definitely something to watch out for.
The above is the detailed content of How does Python handle mutable default arguments in functions, and why can this be problematic?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undress AI Tool
Undress images for free
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
Polymorphism in python classes
Jul 05, 2025 am 02:58 AM
Polymorphism is a core concept in Python object-oriented programming, referring to "one interface, multiple implementations", allowing for unified processing of different types of objects. 1. Polymorphism is implemented through method rewriting. Subclasses can redefine parent class methods. For example, the spoke() method of Animal class has different implementations in Dog and Cat subclasses. 2. The practical uses of polymorphism include simplifying the code structure and enhancing scalability, such as calling the draw() method uniformly in the graphical drawing program, or handling the common behavior of different characters in game development. 3. Python implementation polymorphism needs to satisfy: the parent class defines a method, and the child class overrides the method, but does not require inheritance of the same parent class. As long as the object implements the same method, this is called the "duck type". 4. Things to note include the maintenance
Explain Python generators and iterators.
Jul 05, 2025 am 02:55 AM
Iterators are objects that implement __iter__() and __next__() methods. The generator is a simplified version of iterators, which automatically implement these methods through the yield keyword. 1. The iterator returns an element every time he calls next() and throws a StopIteration exception when there are no more elements. 2. The generator uses function definition to generate data on demand, saving memory and supporting infinite sequences. 3. Use iterators when processing existing sets, use a generator when dynamically generating big data or lazy evaluation, such as loading line by line when reading large files. Note: Iterable objects such as lists are not iterators. They need to be recreated after the iterator reaches its end, and the generator can only traverse it once.
What are Python type hints?
Jul 07, 2025 am 02:55 AM
TypehintsinPythonsolvetheproblemofambiguityandpotentialbugsindynamicallytypedcodebyallowingdeveloperstospecifyexpectedtypes.Theyenhancereadability,enableearlybugdetection,andimprovetoolingsupport.Typehintsareaddedusingacolon(:)forvariablesandparamete
How to iterate over two lists at once Python
Jul 09, 2025 am 01:13 AM
A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.
Explain Python assertions.
Jul 07, 2025 am 12:14 AM
Assert is an assertion tool used in Python for debugging, and throws an AssertionError when the condition is not met. Its syntax is assert condition plus optional error information, which is suitable for internal logic verification such as parameter checking, status confirmation, etc., but cannot be used for security or user input checking, and should be used in conjunction with clear prompt information. It is only available for auxiliary debugging in the development stage rather than substituting exception handling.
What are python iterators?
Jul 08, 2025 am 02:56 AM
InPython,iteratorsareobjectsthatallowloopingthroughcollectionsbyimplementing__iter__()and__next__().1)Iteratorsworkviatheiteratorprotocol,using__iter__()toreturntheiteratorand__next__()toretrievethenextitemuntilStopIterationisraised.2)Aniterable(like
How to make an object a generator in Python?
Jul 07, 2025 am 02:53 AM
To make an object a generator, you need to generate values ??on demand by defining a function containing yield, implementing iterable classes that implement \_\_iter\_ and \_next\_ methods, or using generator expressions. 1. Define a function containing yield, return the generator object when called and generate values ??successively; 2. Implement the \_\_iter\_\_ and \_\_next\_\_\_ in a custom class to control iterative logic; 3. Use generator expressions to quickly create a lightweight generator, suitable for simple transformations or filtering. These methods avoid loading all data into memory, thereby improving memory efficiency.
How to call Python from C ?
Jul 08, 2025 am 12:40 AM
To call Python code in C, you must first initialize the interpreter, and then you can achieve interaction by executing strings, files, or calling specific functions. 1. Initialize the interpreter with Py_Initialize() and close it with Py_Finalize(); 2. Execute string code or PyRun_SimpleFile with PyRun_SimpleFile; 3. Import modules through PyImport_ImportModule, get the function through PyObject_GetAttrString, construct parameters of Py_BuildValue, call the function and process return
