*args vs **kwargs in Python – What’s the Difference and When to Use Them?

Python is a language that values simplicity and flexibility, and nowhere is that more evident than in how it handles function arguments. If you’ve ever come across *args and **kwargs and wondered, “What’s going on here?” — you’re in the right place.

Let’s break it down with plain English, some real examples, and use cases you’ll actually run into in real projects.

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What Are *args and **kwargs?

In Python functions:

• *args lets you pass a variable number of positional arguments.
• **kwargs lets you pass a variable number of keyword arguments.

They sound similar, but they behave a bit differently.

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*args: Flexible Positional Arguments

Imagine you’re building a shopping cart function:

def add_to_cart(*items):
    for item in items:
        print(f"Adding {item} to cart")

add_to_cart("Apples", "Bananas", "Oranges")

Output:

Adding Apples to cart
Adding Bananas to cart
Adding Oranges to cart

Here, *items collects all positional arguments into a tuple. You don’t have to know how many items in advance — super handy!

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**kwargs: Flexible Keyword Arguments

Now let’s say you want to accept different product details like name and price:

def show_product_details(**details):
    for key, value in details.items():
        print(f"{key}: {value}")

show_product_details(name="Laptop", price=1200, warranty="2 years")

Output:

name: Laptop
price: 1200
warranty: 2 years

Here, **details grabs all keyword arguments and packs them into a dictionary. It’s great for flexible configurations or optional settings.

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Can You Use Both Together?

Yes, and it’s super powerful.

def complete_order(*items, **order_info):
    print("Items:", items)
    print("Order Info:", order_info)

complete_order("Laptop", "Mouse", order_id=123, user="nagesh")

Output:

Items: ('Laptop', 'Mouse')
Order Info: {'order_id': 123, 'user': 'nagesh'}

Just remember: *args comes before **kwargs in the function definition.

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Why Should You Care?

These are must-know tools for:

• Writing reusable, clean functions
• Creating decorators
• Building APIs or utilities that adapt to different use cases
• Handling flexible data in data engineering or ML pipelines

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🚫 A Few Common Pitfalls

• Don’t name args or kwargs anything special — they’re just convention. *things and **stuff work too.
• Be cautious when mixing them with regular parameters — keep the order:
  def fun(positional, *args, keyword_default=val, **kwargs)

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🎯 Real-World Use Case in Data Engineering

Let’s say you’re writing a logging function for your ETL job:

def log_event(event_type, *args, **kwargs):
    print(f"[{event_type}] -", *args)
    if kwargs:
        print("Metadata:", kwargs)

log_event("START", "Job XYZ", timestamp="2025-04-06", user="admin")

Boom! You’ve built a flexible logger you can use across your pipeline.

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