Python Advance | Chapter 6: Functional Programming Tools in Python by Prakhar Chauhan | Learn Smarter
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Chapter 6: Functional Programming Tools in Python

Functional programming in Python emphasizes treating computation as the evaluation of functions while minimizing mutable data and side effects. Key tools such as first-class functions, higher-order functions, and the functools module enable the construction of clean, efficient code. The chapter also highlights the principles of immutability and pure functions, which contribute to more predictable and testable programming.

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Sections

  • 6

    Functional Programming

    Functional programming in Python emphasizes using functions as first-class citizens, allowing unique programming techniques.

    Learning Practice

  • 6.1

    First-Class Functions And Higher-Order Functions

    This section covers the concept of first-class functions and higher-order functions in Python, illustrating how they can be assigned, passed as arguments, and returned from other functions.

    Learning Practice

  • 6.1.1

    First-Class Functions

    This section covers first-class functions in Python, explaining how functions are treated as first-class citizens and how they can be manipulated like any other object.

    Learning Practice

  • 6.1.2

    Higher-Order Functions

    Higher-order functions in Python accept and return other functions to facilitate functional programming.

    Learning Practice

  • 6.2

    Lambda Functions In Depth

    Lambda functions are anonymous functions in Python that are useful for short, concise code, often used in conjunction with other functional programming tools.

    Learning Practice

  • 6.3

    Using Map(), Filter(), Reduce()

    This section introduces the functional programming tools map(), filter(), and reduce() in Python, which are used to manipulate and process data iterables efficiently.

    Learning Practice

  • 6.4

    Functools Module

    The functools module in Python provides tools for higher-order functions, allowing for function composition and performance optimization.

    Learning Practice

  • 6.4.1

    Partial

    This section delves into the use of the `partial` function from Python's `functools` module, allowing the creation of new functions with fixed arguments.

    Learning Practice

  • 6.4.2

    Lru_cache

    The `lru_cache` is a decorator in Python's `functools` module that enhances function performance by caching results for reuse.

    Learning Practice

  • 6.4.3

    Singledispatch

    The singledispatch functionality in Python allows function overloading based on the types of arguments.

    Learning Practice

  • 6.5

    Immutability And Pure Functions In Python

    This section introduces immutability and pure functions in Python, highlighting their significance in functional programming.

    Learning Practice

  • 6.5.1

    Immutability

    This section explores the concept of immutability and pure functions in Python, emphasizing the importance of using immutable data structures for stable and predictable code.

    Learning Practice

  • 6.5.2

    Pure Functions

    Pure functions are essential in functional programming as they have no side effects and return consistent outputs for the same inputs.

    Learning Practice

References

Untitled document (17).pdf

Class Notes

Memorization

What we have learnt

  • Functional programming trea...
  • First-class functions and h...
  • Immutability and pure funct...

Final Test

Revision Tests