Python Developer interview questions

Python has several features that make it ideal for development including its simplicity, readability, and extensive standard library. It supports multiple programming paradigms, has a dynamic type system, and is suitable for rapid prototyping.

Package dependencies in a Python project are typically managed using virtual environments and tools like pip. The virtualenv or venv module can be used to create isolated environments, and a requirements.txt file can be used to specify all the required packages and their versions.

Python's libraries like pandas and numpy are excellent for data manipulation. For instance, I have used pandas to clean and preprocess a large dataset by handling missing values, filtering rows, and transforming columns to prepare the data for analysis.

To ensure code quality, I use a combination of linting tools like pylint or flake8, writing unit tests using unittest or pytest frameworks, and adhering to PEP 8 style guidelines. Code reviews and continuous integration also play a vital role.

PEP 8 is the Python Enhancement Proposal which provides guidelines and best practices on how to write Python code. It's important as it improves the readability of the code and ensures a consistent coding style among Python developers.

I have experience using Django, a high-level Python Web framework that encourages rapid development and clean, pragmatic design. It handles much of the complexity of web development, allowing developers to focus on writing their applications.

The main difference between a list and a tuple in Python is that lists are mutable, meaning their contents can change, whereas tuples are immutable, meaning once a tuple is created, its contents cannot change.

To optimize Python code, you can use efficient data structures, leverage list comprehensions, use built-in functions and libraries, avoid unnecessary computations, and consider using tools like Cython or PyPy to accelerate execution.

A decorator in Python is a function that adds functionality to another function or method without modifying its structure. I have used decorators to log the execution time of functions and to enforce access control in web applications.

One challenging project involved developing an automated data processing pipeline. The challenges included handling a variety of data formats and optimizing processing speed. I used pandas for data manipulation, set up a robust exception handling mechanism, and leveraged multiprocessing to enhance performance.