Python

Simple and Readable Syntax

Python has clear syntax close to natural language, making it easy to read, write and maintain code.

Extensive Standard Library

Includes an extensive standard library that provides tools for common tasks without the need for additional code.

Rich Ecosystem

PyPI (Python Package Index) contains over 350,000 packages that extend functionality for any need.

Cross-platform

Python runs on Windows, macOS, Linux and other operating systems without code modifications.

Ideal for Beginners

Its simple and clear syntax makes it the perfect language to learn programming.

Versatility

Used in web development, data analysis, AI, automation, games, desktop applications and more.

Dynamic Typing

Variables don't need type declaration, the interpreter automatically determines the data type.

Automatic Memory Management

The garbage collector automatically frees unused memory, simplifying development.

Object-Oriented Programming

Complete support for OOP with built-in inheritance, encapsulation and polymorphism.

First-Class Functions

Functions are objects that can be passed as arguments and returned by other functions.

List Comprehensions

Concise syntax for creating lists based on other lists or iterables in an elegant way.

Decorators

Pattern that allows modifying or extending function behavior without changing their code.

Django

High-level web framework that encourages rapid development and clean, pragmatic design.

Flask

Lightweight and flexible web microframework that allows creating simple and complex web applications.

NumPy

Fundamental library for scientific computing with multidimensional arrays and mathematical functions.

Pandas

Library for structured data manipulation and analysis, ideal for data science.

TensorFlow/PyTorch

Leading libraries for machine learning and deep learning with neural networks.

Requests

Elegant and simple HTTP library for making web requests and consuming APIs.

📝 Code Style

• • Follow PEP 8 (style guide)
• • Use descriptive names
• • Document functions and classes
• • Keep lines short (max 79 chars)
• • Use spaces instead of tabs

⚡ Performance

• • Use list comprehensions
• • Avoid unnecessary loops
• • Use generators for large data
• • Optimize imports
• • Use profiling to identify bottlenecks

🔒 Security

• • Validate user inputs
• • Use virtual environments
• • Keep dependencies updated
• • Use secure hashing for passwords
• • Implement robust authentication

🧪 Testing

• • Use unittest or pytest
• • Write unit tests
• • Implement continuous integration
• • Use mocks for dependencies
• • Maintain high code coverage

Web Development

Web applications with Django, Flask or FastAPI, RESTful APIs and scalable microservices.

Data Analysis

Data processing, visualization with matplotlib/seaborn and statistical analysis with pandas.

Artificial Intelligence

Machine learning, deep learning, natural language processing and computer vision.

Automation

Scripts to automate repetitive tasks, web scraping and file processing.

Desktop Applications

Graphical interfaces with tkinter, PyQt or Kivy for cross-platform applications.

DevOps and SysAdmin

Infrastructure automation, server management and CI/CD tools.

Is Python slow?

Not necessarily. Python is slower than C++ or Java, but it's fast enough for most applications. For performance-critical tasks, you can use optimized libraries like NumPy or Cython.

How long does it take to learn Python?

For basic concepts: 2-4 weeks. For intermediate level: 2-3 months. For advanced level: 6-12 months with constant practice and real projects.

Python 2 vs Python 3?

Python 2 is no longer supported since 2020. Python 3 is the current and recommended version with significant improvements in syntax, performance and features.

Which web framework to choose?

Django for large and complex projects. Flask for simple and flexible applications. FastAPI for modern and high-performance APIs.

Is Python good for beginners?

Yes, excellent. Python is considered the best language for beginners due to its clear syntax, extensive documentation and active community that facilitates learning.

What's the difference between pip and conda?

pip is Python's standard package manager. conda is a more powerful package and environment manager, especially useful for data science and machine learning.