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Introduction to Python Programming

Introduction to Python Programming

Category: IT Fundamentals & Programming

Welcome to your first step into the exciting world of Python! This 'Introduction to Python Programming' module on UdaanPath is designed to get you up and running quickly. We'll demystify what Python is, why it's the language of choice for …

Advanced Functions: Arguments, Scope & Lambdas

Advanced Functions: Arguments, Scope & Lambdas

Mastering Flexible Function Design and Variable Control in UdaanPath

Introduction: Unleashing Function Power

You've successfully grasped the basics of functions – defining them, passing parameters, and returning values. Now, get ready to dive deeper! Python functions are far more flexible and powerful than you might initially think.

In this chapter, we'll explore advanced ways to handle arguments, understand precisely how Python resolves variable names using the **LEGB rule**, and introduce **lambda functions** for writing concise, anonymous code blocks. Mastering these concepts will allow you to write more robust, adaptable, and Pythonic functions for your UdaanPath projects.

Core Concepts: Beyond the Basics

1. Advanced Argument Handling

While positional and keyword arguments are common, Python offers even more flexible ways to define function signatures.

Mutable Default Argument Pitfall:

A common mistake is using mutable objects (like lists or dictionaries) as default argument values. Default arguments are evaluated *only once* when the function is defined, not every time it's called.


def add_item_bad(item, item_list=[]): # DANGER! Default list created once.
    item_list.append(item)
    return item_list

print("--- Mutable Default Argument Pitfall ---")
list1 = add_item_bad("Apple")
print(f"List 1: {list1}") # Expected: ['Apple']

list2 = add_item_bad("Banana")
print(f"List 2: {list2}") # UNEXPECTED: ['Apple', 'Banana']!

list3 = add_item_bad("Orange", []) # Correct usage: provide new list
print(f"List 3: {list3}")

print("\n--- Correct Way to Handle Mutable Defaults ---")
def add_item_good(item, item_list=None): # Use None as default
    if item_list is None: # Check if a list was NOT provided
        item_list = [] # Create a new list ONLY if needed
    item_list.append(item)
    return item_list

list_a = add_item_good("Laptop")
print(f"List A: {list_a}")

list_b = add_item_good("Keyboard")
print(f"List B: {list_b}") # EXPECTED: ['Keyboard'] - no interaction with list_a
                    
--- Mutable Default Argument Pitfall ---
List 1: ['Apple']
List 2: ['Apple', 'Banana']
List 3: ['Orange']

--- Correct Way to Handle Mutable Defaults ---
List A: ['Laptop']
List B: ['Keyboard']
Interview Tip: This is a very common interview question! Always use `None` as a default for mutable arguments and create the mutable object inside the function if `None` is passed.
Arbitrary Positional Arguments: `*args`

Sometimes you don't know in advance how many positional arguments a function will receive. The `*args` syntax allows a function to accept a variable number of non-keyword arguments. These arguments are then packed into a **tuple**.


def sum_all_numbers(*numbers):
    """Sums all numbers passed as positional arguments."""
    total = 0
    for num in numbers: # 'numbers' is a tuple
        total += num
    return total

print(f"Sum of (1, 2, 3): {sum_all_numbers(1, 2, 3)}")
print(f"Sum of (10, 20, 30, 40): {sum_all_numbers(10, 20, 30, 40)}")
print(f"Sum of (): {sum_all_numbers()}")
                    
Sum of (1, 2, 3): 6
Sum of (10, 20, 30, 40): 100
Sum of (): 0
Arbitrary Keyword Arguments: `**kwargs`

If you need a function to accept a variable number of keyword arguments, use `**kwargs`. These arguments are packed into a **dictionary**, where keys are the argument names and values are their corresponding values.


def display_udaan_profile(**details):
    """Displays user profile details from keyword arguments."""
    print("--- User Profile ---")
    if not details:
        print("No details provided.")
        return
    for key, value in details.items():
        print(f"{key.replace('_', ' ').title()}: {value}")

display_udaan_profile(name="Alok Sharma", role="Data Scientist", experience_years=5)
print("")
display_udaan_profile(course="Python ML", batch="Summer 2025")
print("")
display_udaan_profile()
                    
--- User Profile ---
Name: Alok Sharma
Role: Data Scientist
Experience Years: 5

--- User Profile ---
Course: Python ML
Batch: Summer 2025

--- User Profile ---
No details provided.
Argument Order in Function Definition:

When combining different types of arguments, Python has a strict order:
Positional-only -> Positional or Keyword -> `*args` -> Keyword-only -> `**kwargs`


def complex_function(a, b=10, *args, c, d=20, **kwargs):
    print(f"a: {a}")
    print(f"b (default): {b}")
    print(f"args: {args}")
    print(f"c (keyword-only): {c}")
    print(f"d (keyword-only default): {d}")
    print(f"kwargs: {kwargs}")

print("--- Calling complex_function ---")
complex_function(1, 2, 3, 4, c=5, e=6, f=7)
# a=1 (positional)
# b=2 (positional override default)
# *args=(3, 4)
# c=5 (keyword-only, MUST be keyword)
# d=20 (uses default)
# **kwargs={'e': 6, 'f': 7}

print("\n--- Another call ---")
complex_function(100, c=50, x="hello")
# a=100
# b=10 (uses default)
# *args=()
# c=50
# d=20 (uses default)
# **kwargs={'x': 'hello'}
                    
--- Calling complex_function ---
a: 1
b (default): 2
args: (3, 4)
c (keyword-only): 5
d (keyword-only default): 20
kwargs: {'e': 6, 'f': 7}

--- Another call ---
a: 100
b (default): 10
args: ()
c (keyword-only): 50
d (keyword-only default): 20
kwargs: {'x': 'hello'}

2. Variable Scope: The LEGB Rule

When Python tries to resolve a variable name, it follows a specific order, known as the **LEGB rule**:

  1. L - Local: Is the variable defined within the current function?
  2. E - Enclosing Function Locals: Is the variable defined in an enclosing (outer) function? (This applies to nested functions).
  3. G - Global (Module): Is the variable defined at the top-level of the current module (file)?
  4. B - Built-in: Is the variable a built-in Python name (like `print`, `len`, `sum`, `True`, `False`, `None`)?

Python searches for the variable in this order. As soon as it finds the name, it stops.

Example of LEGB:

# Global (G) scope
global_var = "I am global"

def outer_function():
    # Enclosing (E) scope for inner_function
    enclosing_var = "I am in outer_function"

    def inner_function():
        # Local (L) scope for inner_function
        local_var = "I am local to inner_function"
        print(f"  Inside inner_function: local_var = {local_var}")
        print(f"  Inside inner_function: enclosing_var = {enclosing_var}") # Accesses Enclosing
        print(f"  Inside inner_function: global_var = {global_var}")     # Accesses Global
        print(f"  Inside inner_function: len (Built-in) = {len('test')}") # Accesses Built-in

    inner_function()
    # print(local_var) # ERROR: local_var is not defined in outer_function's scope!

print("--- LEGB Rule Demo ---")
outer_function()
print(f"Outside functions: global_var = {global_var}")
                    
--- LEGB Rule Demo ---
  Inside inner_function: local_var = I am local to inner_function
  Inside inner_function: enclosing_var = I am in outer_function
  Inside inner_function: global_var = I am global
  Inside inner_function: len (Built-in) = 4
Outside functions: global_var = I am global
The `nonlocal` Keyword:

You've seen `global` for modifying global variables. For modifying variables in an **enclosing (but not global) scope** within nested functions, Python provides the `nonlocal` keyword.


def counter():
    count = 0 # Enclosing scope variable

    def increment():
        nonlocal count # Declare that 'count' refers to the 'count' in outer scope
        count += 1
        print(f"  Count inside increment: {count}")
    
    increment()
    increment()
    return count

print("--- Nonlocal Keyword Demo ---")
final_count = counter()
print(f"Final count from counter: {final_count}")
                    
--- Nonlocal Keyword Demo ---
  Count inside increment: 1
  Count inside increment: 2
Final count from counter: 2

3. Lambda Functions (Anonymous Functions)

A **lambda function** is a small anonymous function. It can take any number of arguments, but can only have one expression. They are typically used for short, simple operations where defining a full `def` function would be overkill.

  • Syntax: `lambda arguments: expression`
  • They are anonymous (no name).
  • Can have multiple arguments, but only one expression.
  • Implicitly returns the result of the expression.

# Simple lambda function
add = lambda x, y: x + y
print(f"Lambda add(5, 3): {add(5, 3)}")

# Lambda to check if a number is even
is_even = lambda num: num % 2 == 0
print(f"Is 4 even? {is_even(4)}")
print(f"Is 7 even? {is_even(7)}")
                    
Lambda add(5, 3): 8
Is 4 even? True
Is 7 even? False
Lambda with Higher-Order Functions:

Lambdas are often used as arguments to higher-order functions (functions that take other functions as arguments), like `map()`, `filter()`, and `sorted()`.


# Using lambda with map()
# map(function, iterable) applies function to each item and returns an iterator
numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x**2, numbers))
print(f"Squared numbers (map): {squared_numbers}")

# Using lambda with filter()
# filter(function, iterable) returns an iterator for items where function returns True
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(f"Even numbers (filter): {even_numbers}")

# Using lambda with sorted()
# sorted(iterable, key=function) sorts based on the return value of the key function
students = [
    {"name": "Alice", "score": 88},
    {"name": "Bob", "score": 92},
    {"name": "Charlie", "score": 85}
]
sorted_students_by_score = sorted(students, key=lambda s: s["score"])
print(f"Students sorted by score: {sorted_students_by_score}")

# Sort by name (case-insensitive)
sorted_students_by_name = sorted(students, key=lambda s: s["name"].lower())
print(f"Students sorted by name: {sorted_students_by_name}")
                    
Squared numbers (map): [1, 4, 9, 16, 25]
Even numbers (filter): [2, 4]
Students sorted by score: [{'name': 'Charlie', 'score': 85}, {'name': 'Alice', 'score': 88}, {'name': 'Bob', 'score': 92}]
Students sorted by name: [{'name': 'Alice', 'score': 88}, {'name': 'Bob', 'score': 92}, {'name': 'Charlie', 'score': 85}]

Key Takeaways & Best Practices

  • Flexible Arguments: Use `*args` to accept any number of positional arguments (packed into a tuple), and `**kwargs` for any number of keyword arguments (packed into a dictionary).
  • Argument Order: Remember the specific order when defining functions with mixed argument types: positional, default, `*args`, keyword-only, `**kwargs`.
  • Mutable Defaults: Avoid mutable objects as default argument values (e.g., `list=[]`). Use `None` instead and initialize the object inside the function.
  • LEGB Rule: Understand how Python resolves variable names (Local -> Enclosing -> Global -> Built-in).
  • `nonlocal` vs. `global`: `global` modifies variables at the module level. `nonlocal` modifies variables in an enclosing (non-global) function scope.
  • Lambda Functions: Use lambdas for simple, single-expression anonymous functions, especially when passed as arguments to other functions (`map`, `filter`, `sorted`, etc.).
  • Readability First: While powerful, don't overuse advanced features if they make your code less readable. Simple `def` functions are often clearer than complex lambdas.

Interview Tip: `*args`, `**kwargs`, the mutable default argument pitfall, and the LEGB rule are very common advanced function topics in technical interviews. Be ready to explain and provide examples for each.

Mini-Challenge: UdaanPath Event Logger!

Let's create a flexible logging function for UdaanPath events. Create a Python script named `event_logger.py`.

  • Define a function `log_event(event_type, *messages, **metadata)`.
    - `event_type` (string): Required, e.g., "INFO", "WARNING", "ERROR".
    - `*messages`: Accepts any number of additional string messages.
    - `**metadata`: Accepts any number of additional keyword arguments (e.g., `user_id=123`, `timestamp="2025-07-23"`).
  • Inside the function:
    - Print the `event_type` in uppercase.
    - If `messages` are provided, print each message on a new line, prefixed with "- ".
    - If `metadata` is provided, print "Metadata:" and then each key-value pair from `metadata` on a new line, formatted as " - Key: Value".
  • Call `log_event` with:
    - `log_event("INFO", "Application started.")`
    - `log_event("WARNING", "Low disk space.", "Please clear old files.", server_name="Web_01", disk_percent=90)`
    - `log_event("ERROR", "Database connection failed.", timestamp="2025-07-23 11:30:00", error_code=500)`
  • **Bonus Lambda Task:** Create a list of dictionaries, where each dictionary represents a `UdaanPath` student with keys `name` and `grade`. Use a `lambda` function with `sorted()` to sort the list of students by their `grade` in descending order. Print the sorted list.

Run your `event_logger.py` script and witness the flexibility of advanced function arguments!

File: event_logger.py (Your turn to code!)

# def log_event(event_type, *messages, **metadata):
#    # Your logging logic here

# Call the function for different scenarios

# Bonus Lambda Task:
# students = [{"name": "Alice", "grade": 88}, {"name": "Bob", "grade": 92}, {"name": "Charlie", "grade": 85}]
# sorted_students = sorted(...) # Use lambda here
# print(f"Sorted students: {sorted_students}")
                
Expected Terminal Output (Example Scenario):
$ python event_logger.py
INFO
- Application started.
WARNING
- Low disk space.
- Please clear old files.
Metadata:
  - Server Name: Web_01
  - Disk Percent: 90
ERROR
- Database connection failed.
Metadata:
  - Timestamp: 2025-07-23 11:30:00
  - Error Code: 500
Sorted students: [{'name': 'Bob', 'grade': 92}, {'name': 'Alice', 'grade': 88}, {'name': 'Charlie', 'grade': 85}]

Module Summary: Crafting Flexible and Controlled Functions

You've now moved beyond basic function usage to command advanced features. You've learned to design functions that accept flexible numbers of arguments using `*args` and `**kwargs`, understood the critical LEGB rule for variable scope, and harnessed the conciseness of `lambda` functions.

These advanced function concepts are vital for writing adaptable, bug-resistant, and elegant Python code. With this knowledge, your functions are no longer just reusable blocks; they are dynamic tools, ready for complex challenges, a skill set truly valued at UdaanPath.

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