1. What is Python and makes it popular among programmers?
Ans:
Python is a high level versatile programming language known for its simplicity and readability. Its clear syntax makes it easy for beginners to learn while still being powerful for professionals. Python supports multiple programming styles, including object-oriented and functional programming and comes with a vast standard library. Its popularity stems from its wide use in areas like web development, automation, data science, artificial intelligence and more, as well as its strong community support and platform independence.
2. What is PEP 8 and why should you follow it?
Ans:
The approved style guide for PEP 8 is writing Python code, providing standards for formatting and structuring programs. Following PEP 8 ensures that code is readable, consistent and professional, making collaboration within teams smoother. Adhering to these guidelines reduces errors and improves maintainability, which is especially important in large or long-term projects.
3. How do lists and tuples differ in Python?
Ans:
Both tuples and lists are used to hold multiple pieces of data, but they differ in flexibility. Lists are mutable, allowing changes such as adding, removing or updating elements. Tuples, on the other hand, are immutable, meaning their values cannot be changed once created. Lists are ideal for data that may need modification, while tuples are suitable for data should remain constant and protected.
4. How is memory managed in Python programs?
Ans:
Python automatically manages memory through a combination of reference counting and a built in garbage collector. Objects are allocated memory created and are automatically removed no longer in use. Python also organizes objects into generations to optimize memory management and improve program efficiency, minimizing manual intervention by developers.
5. What are the commonly used built-in data types in Python?
Ans:
Numerous built-in data types are available in Python to manage different kinds of information efficiently. These include numeric types for numbers, sequence types like lists and tuples for ordered data, text types for strings, sets for unique collections, mapping types like dictionaries for key-value storage and Boolean types for true/false values. These types make data storage, organization and manipulation straightforward.
6. How does Python pass arguments to functions?
Ans:
Python passes arguments to functions using a method called object reference. If a flexible object is provided changes made within the function can affect the original data. However for fixed or unchangeable objects the original data remains intact. This approach allows Python to handle different types of objects efficiently while maintaining predictable behavior.
7. What is the Global Interpreter Lock in Python?
Ans:
Only one thread may run Python thanks to a technique called the Global Interpreter Lock or GIL instructions at a time, even on multi-core processors. While it simplifies memory management and ensures thread safety, it limits true parallel execution for CPU-intensive tasks. However, for operations that mainly involve input/output or networking, the GIL has minimal impact.
8. What is the difference between a module and a package in Python?
Ans:
A module is a single Python file contains reusable code such as functions, classes or variables. A package, on the other hand, is a collection of modules organized together, which helps structure large projects efficiently. Modules allow code reuse in smaller scopes, while packages provide a way to manage and organize complex, multi-file projects.
9. What are some popular built-in libraries in Python?
Ans:
Python comes with many built-in libraries that simplify common tasks. Libraries exist for performing mathematical calculations, managing dates and times, interacting with the operating system, handling system specific operations and working with data formats like JSON. These libraries offer pre-built functionality for common programming requirements, saving time and effort.
10. How can you manage memory usage effectively in Python?
Ans:
Effective memory management in Python involves writing clean, efficient code and avoiding unnecessary data creation. The garbage collector automatically removes items that aren't being used but developers can further optimize memory by reusing variables and using tools to monitor memory usage. Adopting practices such as these helps maintain performance and prevents memory-related issues in larger programs.
