Data Science Notes Part-2 | Functional Programming in Python
Table of contents-
Chapter 1: Introduction to Functional Programming
-Functional programming paradigms
-Advantages and applications in Python
-Understanding immutability and side effects
Chapter 2: Built-in Functions for Iterables
-Exploring map(), filter(), and reduce() functions
-Practical examples and use cases
-Performance considerations and best practices
Chapter 3: Lambda Functions
-Syntax and structure of lambda functions
-Lambda functions vs. named functions
-Common use cases and practical examples
Chapter 4: Closures
-Understanding lexical scoping and nested functions
-Creating and using closures in Python
-Applications and advantages
Chapter 5: Function Decorators
-Introduction to decorators
-Decorator syntax and usage
-Practical examples and advanced decorator patterns
Chapter 6: itertools Module
-Overview of the itertools module
-Key functions: count(), cycle(), repeat(), etc.
-Advanced iterator manipulation techniques
Chapter 7: functools Module
-Understanding the functools module
-Commonly used functions: partial(), reduce(), etc.
-Advanced techniques for function manipulation
Chapter 8: operator Module
-Overview and applications of the operator module
-Commonly used functions: add(), mul(), itemgetter(), etc.
-Efficient data manipulation with operator functions
Chapter 9: Exception Handling
-Understanding exceptions in Python
-try-except blocks and error handling
-Best practices for exception handling
Chapter 10: Regular Expressions
-Introduction to regular expressions
-Syntax and patterns
-Practical examples for text processing and pattern matching
Chapter 11: Putting It All Together
-Case studies and real-world applications
-Integration of functional programming concepts
-Best practices and advanced techniques
Chapter 12: Interview Questions and Answers
-Common interview questions on functional programming
-Detailed explanations and solutions