Python is a fundamental language for Data Engineering, widely used in data processing, ETL pipelines, and big data frameworks like PySpark. To help you ace your Data Engineering interviews, I’m starting a Python Q&A series where we will cover commonly asked questions along with detailed explanations.

Why Python for Data Engineering?

Python is popular in Data Engineering due to:

• Ease of use: Simple syntax makes it easy to write and maintain code.
• Libraries & Ecosystem: Pandas, NumPy, PySpark, and Airflow are essential for data processing.
• Scalability: Python integrates well with big data technologies like Hadoop, Spark, and AWS Glue.
• Automation: Python is widely used for building data pipelines and automation workflows.

Common Python Interview Questions for Data Engineers:

Q1: What is the difference between deepcopy and shallow copy?

Ans: The difference between deepcopy and shallow copy in Python lies in how they handle nested objects.

Shallow Copy (copy.copy()) :

• Creates a new object but does not create copies of nested objects.
• Changes to mutable nested objects (like lists or dictionaries) in the original will reflect in the copied object.

import copy

list1 = [[1, 2], [3, 4]]
shallow_copy = copy.copy(list1)

list1[0][0] = 99 # Modify original list

print(shallow_copy) # [[99, 2], [3, 4]] (Nested object is affected)

Deep Copy (copy.deepcopy()):

• Recursively copies all objects, including nested ones.
• Changes in the original object do not affect the copied object.

import copy

list1 = [[1, 2], [3, 4]]
deep_copy = copy.deepcopy(list1)

list1[0][0] = 99 # Modify original list

print(deep_copy) # [[1, 2], [3, 4]] (Not affected)

Key Difference:

• Shallow Copy (copy.copy()): Only copies references for nested objects, so changes in the original will reflect in the copy.
• Deep Copy (copy.deepcopy()): Creates a completely independent copy, including nested objects.

Use shallow copy when working with immutable objects and deep copy when you need a fully independent duplicate of a complex object.

Q2: Explain **kwargs and *args in Python?

Ans: In Python, *args and **kwargs are used in function definitions to handle a variable number of arguments.

*args (Non-Keyword Arguments)

• Allows you to pass any number of positional arguments to a function.
• Inside the function, args is treated as a tuple.
• Useful when you don’t know beforehand how many arguments will be passed.

def add_numbers(*args):
    return sum(args)

print(add_numbers(1, 2, 3, 4)) # Output: 10

**kwargs (Keyword Arguments)

• Allows you to pass any number of named (keyword) arguments to a function.
• Inside the function, kwargs is treated as a dictionary.
• Useful when you need to handle dynamic named parameters.

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

print_info(name="John", age=30, job="Engineer")

Output:

name: John
age: 30
job: Engineer

When to Use *args and **kwargs?

• Use *args when your function needs to accept multiple positional arguments.
• Use **kwargs when your function needs to accept multiple keyword arguments.

You can combine both in the same function:

def example_function(a, b, *args, **kwargs):
   print(f"a: {a}, b: {b}")
   print("args:", args)
   print("kwargs:", kwargs)

example_function(1, 2, 3, 4, name="Alice", age=25)

Output:

a: 1, b: 2
args: (3, 4)
kwargs: {‘name’: ‘Alice’, ‘age’: 25}

Stay tuned for more interview questions and explanations!

💬 Have a specific Python interview question you’d like me to cover? Drop it in the comments!