Functional Programming in Python

Map, Filter, and Reduce

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LearnStep 1 of 3

Functional Data Processing Tools

Learning Objectives

•Apply the map function to transform sequences of data efficiently
•Use the filter function to extract elements matching specific criteria
•Aggregate data streams into single values using functools.reduce
•Evaluate when to replace map and filter with list comprehensions for improved readability

Lesson Outline

LearnStep 1/3

Functional Data Processing Tools

Python offers powerful functional programming tools for processing iterables: map, filter, and reduce. While these are staples in functional languages, Python often provides more 'Pythonic' alternatives like list comprehensions. Understanding both allows you to choose the right tool for the job.

1. Map

map(function, iterable) applies a function to every item in an iterable. It returns an iterator (in Python 3).

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2. Filter

filter(function, iterable) constructs an iterator from elements of the iterable for which the function returns true.

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3. Reduce

reduce(function, iterable) applies a function of two arguments cumulatively to the items of a sequence, reducing the sequence to a single value. It resides in the functools module.

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4. The Case for Comprehensions

In modern Python, list comprehensions are often preferred over map and filter using lambda because they are more readable.

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However, map and filter can be faster when using built-in functions (like str or int) rather than a custom lambda, and they are lazy by default (memory efficient).