The LEGB scoping rule, first-class functions, *args,**kwargs(key-word args),mutable-default-argument pitfall

Python Functions, Scope, "args", "*kwargs" & Common Pitfalls

Python has a few concepts that look complicated at first, but become very easy once we understand what Python is actually doing.

This guide covers:

• LEGB Scoping Rule
• Local, Enclosing, Global and Built-in scopes
• "nonlocal"
• First-Class Functions
• "*args"
• "**kwargs"
• Packing and Unpacking
• Mutable Default Argument Pitfall
• Safe ways to use default arguments

1. LEGB Scoping Rule

What is Scope?

Scope means the area of a program where a variable can be accessed.

For example:

def greet():
name = "Deepika"
print(name)

greet()

Here, "name" is created inside "greet()", so it belongs to the local scope of that function.

Python follows a specific rule to find a variable.

This rule is called LEGB.

L → Local
E → Enclosing
G → Global
B → Built-in

Python searches for a variable in this order:

Local
↓
Enclosing
↓
Global
↓
Built-in

Python stops searching as soon as it finds the variable.

L → Local Scope

A variable created inside a function is usually a local variable.

Example

def greet():
name = "Deepika"
print(name)

greet()

Here:

name = "Deepika"

is local to "greet()".

It cannot normally be accessed outside the function:

def greet():
name = "Deepika"

print(name)

This produces a "NameError" because "name" only exists inside "greet()".

Simple definition

«Local scope is the scope inside the current function.»

1. E → Enclosing Scope

The enclosing scope appears when one function is defined inside another function.

Example

def outer():

name = "Deepika"

def inner():
    print(name)

inner()

outer()

"name" is not inside "inner()".

It is inside "outer()".

Therefore, from the point of view of "inner()", "name" is in the enclosing scope.

outer()
│
├── name = "Deepika"
│
└── inner()
│
└── print(name)

Simple definition

«Enclosing scope is the scope of an outer function surrounding the current inner function.»

This concept is especially important when learning closures.

1. G → Global Scope

A variable created outside all functions is generally in the global scope.

Example

name = "Deepika"

def greet():
print(name)

greet()

Python cannot find "name" inside "greet()", so it looks in the global scope and finds it.

Local → not found
Enclosing → not found
Global → found

Output:

Deepika

Simple definition

«Global scope is the scope outside functions and classes at the module level.»

1. B → Built-in Scope

Python has many names that are already available to us.

Examples:

print()
len()
sum()
max()
min()
type()

These belong to Python's built-in scope.

Example

numbers = [10, 20, 30]

print(len(numbers))

Python looks for "len":

Local → not found
Enclosing → not found
Global → not found
Built-in → found

Output:

3

Simple definition

«Built-in scope contains names provided by Python itself.»

1. Complete LEGB Example

x = "Global"

def outer():

x = "Enclosing"

def inner():

    x = "Local"

    print(x)

inner()

outer()

Output:

Local

Why?

Because Python searches:

Local → found!

It stops there.

It does not continue searching the enclosing or global scope.

Another LEGB Example

x = "Global"

def outer():

x = "Enclosing"

def inner():
    print(x)

inner()

outer()

Output:

Enclosing

Why?

Local → not found
Enclosing → found!

1. "nonlocal"

"nonlocal" is used when an inner function wants to modify a variable belonging to an enclosing function.

Example

def outer():

count = 0

def inner():
    nonlocal count
    count += 1

inner()

print(count)

outer()

Output:

1

Without "nonlocal", Python would treat an assignment such as:

count += 1

as involving a local "count" inside "inner()".

Simple definition

«"nonlocal" tells Python that a variable belongs to an enclosing function's scope, not the current local scope.»

When is "nonlocal" useful?

It is commonly used with:

• Nested functions
• Closures
• Functions that need to remember and update state

1. First-Class Functions

One of the most important things to understand about Python is:

«Functions are objects too.»

Because functions are objects, they can be treated like other values.

They can be:

• Assigned to variables
• Passed as arguments
• Returned from functions
• Stored in lists, dictionaries, etc.

This is called first-class functions.

1. Assigning a Function to a Variable

def greet():
print("Hello!")

x = greet

x()

Output:

Hello!

Here:

x = greet

means "x" refers to the function.

Notice the difference:

greet

means the function itself.

greet()

means call the function.

1. Passing a Function as an Argument

Because functions are first-class objects, we can pass them to another function.

def greet():
print("Hello!")

def execute(function):
function()

execute(greet)

Output:

Hello!

Here:

execute(greet)

passes the "greet" function to "execute()".

Inside "execute()":

function()

calls that function.

1. Returning a Function

A function can also return another function.

def outer():

def inner():
    print("Hello!")

return inner

x = outer()

x()

Output:

Hello!

Here:

x = outer()

stores the returned "inner" function in "x".

This idea is important for understanding closures and decorators.

1. When Are First-Class Functions Useful?

First-class functions are useful when we want to:

• Pass behavior to another function
• Create callbacks
• Build decorators
• Create closures
• Choose a function dynamically
• Store multiple functions and execute them later

Simple idea

Instead of only passing data:

process(10)

we can also pass behavior:

process(greet)

1. "*args"

Sometimes we don't know how many positional arguments a function will receive.

For example:

def add(a, b):
return a + b

This only accepts two arguments:

add(10, 20)

But this won't work:

add(10, 20, 30, 40)

We can use "*args" when we want to accept any number of positional arguments.

Syntax

def function_name(*args):
...

Example

def show(*args):
print(args)

show(10, 20, 30)

Output:

(10, 20, 30)

"args" contains the positional arguments inside a tuple.

Conceptually:

args = (10, 20, 30)

1. Example Using "*args"

def add(*args):

total = 0

for number in args:
    total += number

return total

print(add(10, 20))
print(add(10, 20, 30))
print(add(10, 20, 30, 40))

Output:

30
60
100

Simple definition

«"*args" allows a function to accept any number of positional arguments and collects them into a tuple.»

1. Is "args" a Special Keyword?

No.

The "*" is important.

The name can technically be anything:

def show(*numbers):
print(numbers)

But Python programmers normally use:

*args

because it is the standard convention.

1. "**kwargs"

Now let's talk about keyword arguments.

Consider:

def student(name, age):
print(name)
print(age)

We can call it using keyword arguments:

student(name="Deepika", age=21)

But what if we don't know how many keyword arguments will be provided?

We can use:

**kwargs

Syntax

def function_name(**kwargs):
...

Example

def student(**kwargs):
print(kwargs)

student(name="Deepika", age=21, city="Bangalore")

Output:

{'name': 'Deepika', 'age': 21, 'city': 'Bangalore'}

"kwargs" contains the keyword arguments inside a dictionary.

Conceptually:

kwargs = {
"name": "Deepika",
"age": 21,
"city": "Bangalore"
}

1. Simple Definition of "**kwargs"

«"**kwargs" allows a function to accept any number of keyword arguments and collects them into a dictionary.»

1. "args" vs "*kwargs"

Feature| "args"| "kwargs"
Accepts| Positional arguments| Keyword arguments
Stores data as| Tuple| Dictionary
Example| "10, 20, 30"| "name="Deepika""
Symbol| ""| "**"

The easiest thing to remember:

*args
↓
positional arguments
↓
tuple

**kwargs
↓
keyword arguments
↓
dictionary

1. Using "args" and "*kwargs" Together

We can use both in the same function.

def demo(*args, **kwargs):

print(args)
print(kwargs)

demo(
10,
20,
30,
name="Deepika",
age=21
)

Output:

(10, 20, 30)

{'name': 'Deepika', 'age': 21}

Python separates them:

10, 20, 30
↓
*args
↓
tuple

and:

name="Deepika"
age=21
↓
**kwargs
↓
dictionary

1. Packing and Unpacking

The "" and "*" symbols can also be used for unpacking.

Packing

When defining a function:

def demo(*args):
print(args)

"*args" collects multiple arguments into one tuple.

This is called packing.

Unpacking

Suppose we have:

numbers = [10, 20, 30]

We can unpack them:

def add(a, b, c):
return a + b + c

print(add(*numbers))

This is equivalent to:

add(10, 20, 30)

The "*" takes the elements from the list and sends them as separate positional arguments.

1. Dictionary Unpacking with "**"

Suppose:

student = {
"name": "Deepika",
"age": 21
}

We can do:

def show(name, age):
print(name)
print(age)

show(**student)

This is equivalent to:

show(name="Deepika", age=21)

So:

• → unpack sequence into positional arguments

** → unpack dictionary into keyword arguments

1. Mutable Default Argument Pitfall

Now let's look at one of Python's famous pitfalls.

A default argument is a value given to a parameter when the caller doesn't provide one.

Example:

def greet(name="Deepika"):
print("Hello", name)

greet()

Output:

Hello Deepika

1. What Is a Mutable Object?

A mutable object is an object whose contents can be changed.

Common mutable types include:

list
dict
set

Example:

numbers = []

numbers.append(10)

print(numbers)

Output:

[10]

The list was modified.

1. The Problem with Mutable Default Arguments

Consider this function:

def add_item(item, items=[]):

items.append(item)

return items

Now:

print(add_item("Apple"))
print(add_item("Banana"))
print(add_item("Mango"))

Output:

['Apple']
['Apple', 'Banana']
['Apple', 'Banana', 'Mango']

This can be surprising.

Why does this happen?

Because the default list:

items=[]

is created when the function is defined, not every time the function is called.

The same default list can therefore be reused between calls.

Conceptually:

Function
|
└── default list
|
├── Call 1 → ['Apple']
|
├── Call 2 → ['Apple', 'Banana']
|
└── Call 3 → ['Apple', 'Banana', 'Mango']

1. Why Is This Called a Pitfall?

Break the phrase into three parts:

Mutable

The object can be changed.

[]

is mutable.

Default argument

This is the default parameter:

items=[]

Pitfall

The same mutable object can be reused across function calls, causing unexpected results.

Simple definition

«The mutable default argument pitfall occurs when a mutable object such as a list, dictionary, or set is used as a default parameter and is modified, causing its changes to persist between function calls.»

1. The Safe Way: Use "None"

Instead of:

def add_item(item, items=[]):

use:

def add_item(item, items=None):

if items is None:
    items = []

items.append(item)

return items

Now:

print(add_item("Apple"))
print(add_item("Banana"))
print(add_item("Mango"))

Output:

['Apple']
['Banana']
['Mango']

A new list is created whenever "items" is "None".

1. Why Does "None" Fix the Problem?

"None" is used as a signal:

items=None

means:

«"No list was provided."»

Then:

if items is None:
items = []

creates a fresh list.

So instead of:

Call 1 ─┐
Call 2 ─┼──→ SAME LIST
Call 3 ─┘

we get:

Call 1 → NEW LIST
Call 2 → NEW LIST
Call 3 → NEW LIST

1. Mutable Default Dictionaries

The same problem can happen with dictionaries.

Avoid:

def add_user(name, users={}):

users[name] = "active"

return users

Prefer:

def add_user(name, users=None):

if users is None:
    users = {}

users[name] = "active"

return users

1. Mutable Default Sets

The same idea applies to sets.

Avoid:

def add_number(number, numbers=set()):

numbers.add(number)

return numbers

Prefer:

def add_number(number, numbers=None):

if numbers is None:
    numbers = set()

numbers.add(number)

return numbers

1. Are All Default Arguments Dangerous?

No.

Immutable objects such as:

int
float
str
tuple
None
bool

do not have the same mutable-default problem.

For example:

def counter(count=0):
count += 1
return count

print(counter())
print(counter())
print(counter())

Output:

1
1
1

This happens because integers are immutable.

1. The Pattern to Remember

Whenever you want a fresh mutable object for every function call, use "None".

List

def function(data=None):

if data is None:
    data = []

Dictionary

def function(data=None):

if data is None:
    data = {}

Set

def function(data=None):

if data is None:
    data = set()

1. Quick Revision

LEGB

L → Local
E → Enclosing
G → Global
B → Built-in

«Python searches for a variable in this order.»

"nonlocal"

nonlocal variable

«Used inside a nested function to modify a variable from the enclosing function.»

First-Class Functions

«Functions are objects and can be assigned to variables, passed as arguments, returned from functions, and stored in collections.»

Example:

def greet():
print("Hello")

x = greet

x()

"*args"

def function(*args):

«Accepts any number of positional arguments and stores them in a tuple.»

Example:

def show(*args):
print(args)

show(10, 20, 30)

Output:

(10, 20, 30)

"**kwargs"

def function(**kwargs):

«Accepts any number of keyword arguments and stores them in a dictionary.»

Example:

def show(**kwargs):
print(kwargs)

show(name="Deepika", age=21)

Output:

{'name': 'Deepika', 'age': 21}

Mutable Default Argument

Avoid:

def function(items=[]):

when you intend to create a fresh list for every call.

Prefer:

def function(items=None):

if items is None:
    items = []

«The key reason: default arguments are created when the function is defined, so a mutable default object can be reused between calls.»

⭐ The Most Important Things to Remember

LEGB
↓
Where does Python look for a variable?

L → Local
E → Enclosing
G → Global
B → Built-in

First-class functions
↓
Functions can be treated like objects.

*args
↓
many positional arguments
↓
tuple

**kwargs
↓
many keyword arguments
↓
dictionary

Mutable default argument
↓
Avoid [] / {} / set() as defaults
↓
Use None instead

These concepts are especially important because they form the foundation for understanding closures, decorators, callbacks, function arguments, and Python's scope behavior.