Use Cases of Lambda Expressions

1. One-Time Use Functions: For quick operations like sorting or filtering.
2. Higher-Order Functions: Used with functions like map, filter, and reduce.
3. Small Helper Functions: Ideal for concise operations in specific parts of the code.

Limitations of Lambda Expressions

1. Single Expression: Cannot include multiple statements.
2. Readability: Overusing lambdas can make code harder to understand.
3. Debugging: Debugging anonymous functions can be challenging compared to named functions.

When to Use Lambda Expressions

• Use them for short, throwaway functions.
• Avoid them for complex logic or when readability is critical.

Lambda expressions provide a powerful, concise way to define small functions inline, making Python code more flexible and expressive when used appropriately.
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What are Python Libraries?

Python libraries are collections of pre-written code that provide specific functionality, allowing developers to perform complex tasks without having to write code from scratch. They save time, enhance productivity, and reduce the likelihood of errors.

Types of Python Libraries

1. Standard Libraries
   Bundled with Python installations, these libraries cover core functionalities like file I/O, math, and system operations. Examples:

   • math for mathematical functions
   • os for operating system interaction
   • datetime for date and time operations

2. Third-Party Libraries
   Developed by the Python community and available via package managers like pip. Examples:

   • NumPy for numerical computations
   • Pandas for data manipulation
   • Requests for HTTP requests

• Easy to Learn & Readable 📝

  • Python's simple syntax resembles natural language, making it beginner-friendly.
  • Indentation-based structure enhances code readability.

• Extensive Libraries & Frameworks 📚

  • Includes powerful libraries like NumPy, Pandas (for data science), TensorFlow (for AI), Flask & Django (for web development).

• Cross-Platform & Open-Source 🌍

  • Runs on Windows, Mac, Linux, and embedded systems.
  • Free and open-source with strong community support.

• Great for AI & Machine Learning 🤖

  • Python dominates AI/ML fields due to frameworks like Scikit-Learn, PyTorch, and TensorFlow.

• Strong Community Support 👥

  • A vast global community provides tutorials, documentation, and third-party tools.

• Supports Multiple Programming Paradigms 🛠

  • Works with object-oriented, procedural, and functional programming styles.