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Functional Programming in Python

Dan Bader
Dan Bader 32 Lessons 1h 25m advanced python
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In this course, you’ll learn how to approach functional programming in Python. You’ll start with the absolute basics of Functional Programming (FP). After that, you’ll see hands-on examples for common FP patterns available, like using immutable data structures and the filter(), map(), and reduce() functions. You’ll end the course with actionable tips for parallelizing your code to make it run faster.

You’ll cover:

  1. What functional programming is
  2. How you can use immutable data structures to represent your data
  3. How to use filter(), map(), and reduce()
  4. How to do parallel processing with multiprocessing and concurrent.futures

What’s Included:

Related Learning Paths:

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Section 1: Immutable Data Structures

6 Lessons 17m

  1. 1. Functional Programming Course Overview01:15
  2. 2. What Is Functional Programming?04:02
  3. 3. Mutable Data Structures: Lists and Dictionaries03:48
  4. 4. Immutable Data Structures: namedtuple03:39
  5. 5. Danger Zone: Mixing Mutable and Immutable Data Structures01:35
  6. 6. Immutable Data Structures: Tuples02:56

Section 2: The filter() Function

5 Lessons 13m

  1. 1. The filter() Function: Overview00:43
  2. 2. What Is the filter() Function?03:17
  3. 3. Storing Filtered Data in a Tuple02:39
  4. 4. Why Use the filter() Function?03:46
  5. 5. Filtering List Comprehensions03:33

Section 3: The map() Function

5 Lessons 11m

  1. 1. The map() Function: Overview00:53
  2. 2. What Is the map() Function?02:13
  3. 3. How to Use the map() Function04:25
  4. 4. The map() Function vs Generator Expressions02:28
  5. 5. Why Use the map() Function?01:50

Section 4: The reduce() Function

4 Lessons 16m

  1. 1. The reduce() Function: Overview01:33
  2. 2. What Is the reduce() Function?04:49
  3. 3. Why Use the reduce() Function?06:25
  4. 4. Grouping Data With itertools.groupby()03:20

Section 5: Parallel Processing With multiprocessing

7 Lessons 16m

  1. 1. Parallel Processing With multiprocessing: Overview00:48
  2. 2. Multiprocessing Testbed Overview03:40
  3. 3. The multiprocessing Module01:03
  4. 4. Measuring Execution Time in the multiprocessing Testbed02:07
  5. 5. How to Create a multiprocessing.Pool() Object01:40
  6. 6. How to Use multiprocessing.Pool()05:14
  7. 7. Parallel Processing With multiprocessing: Conclusion01:35

Section 6: Parallel Processing With concurrent.futures

5 Lessons 9m

  1. 1. Parallel Processing With concurrent.futures: Overview01:55
  2. 2. The concurrent.futures Module02:45
  3. 3. How Functional Programing Makes Parallel Processing Simple01:38
  4. 4. concurrent.futures vs multiprocessing01:06
  5. 5. When to Use concurrent.futures or multiprocessing02:24
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About Dan Bader

Dan Bader is the owner and editor in chief of Real Python and the main developer of the realpython.com learning platform. Dan has been writing code for more than 20 years and holds a master's degree in computer science.

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Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The team members who worked on this tutorial are:

Joanna

Tappan

Participant Comments

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samuelebright on Jan. 30, 2022

Thank you Dan for this course. I’m looking forward to storing data from Excel sheets in immutable data structures and then using some of the strategies from the videos to manipulate the data for use in my programs.

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squeakyboots on April 27, 2021

Thanks so much for this course! I’m excited to try applying this to my own API calls when something might be running more slowly than I’d like.

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paulagm12 on Nov. 15, 2020

Great course! It is explained in a very clear way and I have learnt a lot of new and useful things to put into practise in my programming. I really appreciate all the effort put into this.

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Ghani on Oct. 14, 2020

This functional programming course is really excellent! Although I need to revise it and chew it again, I learned plenty of ways I can make my code more efficient. Thanks Dan.

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milangnjatovic on April 28, 2020

Great course and even greater presentation. Keep it up.

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bennjuguna0 on April 13, 2020

Honestly thought it would be harder than this. Many thanks to you for the awesome tutorials.

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George Yeboah on April 10, 2020

Good tutorial I really enjoyed watching it and picking up some cool techniques from it Great work keep it up

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zorion on April 8, 2020

Awesome, thanks Dan! I finally understand what GIL blocks. It was always a black box for me, I knew that there was something wrong in Python parallelism but I didn’t know that it was restricted to threads while computing. Good to know, Good to know!

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tinachoudhary on April 6, 2020

Thanks a lot for the tutorial, it is explained well.

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Axel FAUVEL on March 27, 2020

Thanks a lot for this course, very well explained :)

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Donna van Wyk on Sept. 24, 2019

Wow. I’ve been learning about python a lot of diff ways. Your “simple” explanation of a lambda here finally made it click. I am so excited to go thru all of the courses and really get somewhere.

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Shay Elmualem on Aug. 3, 2019

This is great stuff Dan, thanks! I normally, out of habit, just go with the “manual” approach of iterating/looping over and just getting/saving the data I need, I really need to start using these built-ins more! very cool.

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senatoduro8 on July 25, 2019

Thanks Dan. This tutorial is great! It did help me learn new was to use the map, reduce and apply functions creatively.

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konk on July 5, 2019

Good tutorial. I’d read about python multiprocessing/threading, but had not yet implemented it. Seeing it in action in this tutorial - wow - quite easy to get going. No reason not to implement when it will help.

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