Map, Filter, and Reduce Functions in Python #32

In this session you will learn or explore about the Map, Filter, and Reduce function in Python with numerous example.

Functional programming is a programming paradigm that focuses on writing code using pure functions and treating them as first-class citizens. I

Map, Filter, and Reduce: These are built-in functions that are commonly used in functional programming.

Map: The map() function applies a given function to each item in an iterable and returns an iterator of the results.

Filter: The filter() function creates an iterator of elements from an iterable for which a given function returns true.

Reduce: The reduce() function applies a function of two arguments cumulatively to the items of an iterable, reducing it to a single value.

Functional programming encourages writing code that is modular, reusable, and easier to test. It promotes the use of higher-level abstractions, such as functions and data transformations, to solve problems. By focusing on immutability and purity, functional programming aims to reduce bugs, improve code readability, and facilitate parallel and concurrent programming.

These concepts are very helpful in the data transformation. You will learn lots of example using these method with Lambda function.

Following topics included in video:
1.) What is map filter reduce in Python?
2.) Map function with Lambda
3.) Filter function with Lambda
4.) Reduce function with Lambda
5.) Map within filter function
6.) Filter within map function
7.) Map() and Filter() within Reduce()
8.) What is the difference between filter map and reduce in Python?
9.) Nested use of Lambda function within Map, Reduce and Filter
10.) Examples....

[rizwan]

list(filter(lambda x: ‘x’ in x or ‘t’ in x, ai.split()))