Python Interview Series #Day 4

🚀 Day 4: 13+ Python Interview Questions You Must Know

Welcome to Day 4 of the SQL School 15-Day Python Interview Series! Today we’re consolidating your knowledge with a powerful mix of questions covering OOP, data structures, code style, and Python’s core mechanics. Nailing these topics will prove you have a well-rounded and robust understanding of the language.

📺 Watch Day 4: A Rapid Review of Core Concepts

This fast-paced video covers a wide range of essential topics. Watch it to test your recall and identify any areas that need more attention.

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What’s On Deck for Day 4? Key Topics Explained

This session is packed with fundamental concepts that are frequently asked in technical interviews.

🐍 Object-Oriented Programming (OOP) & Classes

• The `__init__` Constructor: It’s the special method that Python calls automatically when you create a new object. Its main job is to initialize the object’s attributes.
• The `self` Keyword: Inside a class method, `self` is a reference to the current instance of the class. You use it to access the attributes and methods of that specific object.
• Access Specifiers: Python uses naming conventions for visibility. Protected attributes start with a single underscore (`_var`), and Private attributes start with a double underscore (`__var`), which enforces name mangling.

📊 Data Structures & String Manipulation

• Arrays vs. Lists: A key distinction! Python arrays are memory-efficient and must contain elements of the same data type. Lists are more flexible, can hold mixed data types, but use more memory.
• List Slicing: A powerful feature for extracting portions of a list. The syntax `my_list[start:stop:step]` allows for complex selections, like getting every other element with `numbers[1::2]`.
• `split()` and `join()` Methods: Essential for string manipulation. `split()` breaks a string into a list based on a delimiter. `join()` does the opposite, combining elements of a list into a single string.

🔧 Code Quality & Best Practices

• PEP 8: The official style guide for Python. Following it ensures your code is readable, consistent, and professional.
• Docstrings: Multi-line strings used as the first statement in a module, class, or function to explain its purpose. They are crucial for good documentation.
• Unit Testing: The practice of testing individual parts (units) of your code in isolation. Python’s built-in `unittest` framework helps you find bugs early and ensure your code works as expected.

💡 Hands-On Challenge: Reformat a CSV String

This challenge tests your mastery of the `split()` and `join()` methods, a common real-world task.

You are given a string of data where items are separated by commas. Write a function that reformats this string so that the items are separated by a space and a hyphen (` – `).

def reformat_string(csv_string):
  """
  Takes a comma-separated string and reformats it
  with a ' - ' delimiter.
  """
  # Step 1: Split the string into a list
  items = csv_string.split(',')
  
  # Step 2: Join the list items with the new delimiter
  reformatted_string = ' - '.join(items)
  return reformatted_string

# --- Test the implementation ---
data_string = "Python,Java,C++,JavaScript,SQL"
new_format = reformat_string(data_string)
print(f"Original: {data_string}")
print(f"Reformatted: {new_format}")
# Expected Output: Python - Java - C++ - JavaScript - SQL
        

🌟 Why Day 5 Builds a Complete Developer Profile

By mastering the topics from Day 5, you demonstrate a holistic understanding of Python development. You’re showing that you can write object-oriented code, handle data, and follow professional standards for testing and styling. This well-rounded knowledge is exactly what hiring managers look for.

Frequently Asked Python Interview Questions (FAQs)

Q1: What is the purpose of the `pass` statement?

The `pass` statement is a null operation—it does nothing. It’s used as a placeholder where syntax requires a statement, but you don’t want any code to execute. This is common in empty functions, classes, or loops that you plan to implement later.

Q2: What is the difference between Pass by Value and Pass by Reference?

In Pass by Value, a copy of the variable is passed to the function, so changes inside the function don’t affect the original. In Pass by Reference, the memory address of the variable is passed, so changes do affect the original. Python uses a mechanism called “Pass by Object Reference,” which acts like pass by reference for mutable objects (like lists) and pass by value for immutable objects (like numbers or strings).

Q3: Why are `.pyc` files used?

When a Python module is imported, the interpreter compiles the source code (.py) into bytecode and saves it as a .pyc` file. The next time the module is imported, Python can skip the compilation step and directly execute the faster bytecode, leading to quicker startup times.

🎯 Day 4 Snapshot: Your Quick Reference

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