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Exercise: Group Scientists by Field With reduce()

Dan Bader

From Python's Functional Primitives to Parallelism Dan Bader 08:30

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Course Contents

Overview

66%

Functional Programming Course Overview 01:15

What Is Functional Programming? 04:02

Mutable Data Structures: Lists and Dictionaries 03:48

Immutable Data Structures: namedtuple 03:39

Danger Zone: Mixing Mutable and Immutable Data Structures 01:35

Immutable Data Structures: Tuples 02:56

Exercise: Build an Immutable Scientist Dataset 08:30

The filter() Function: Overview 00:43

What Is the filter() Function? 03:17

Storing Filtered Data in a Tuple 02:39

Why Use the filter() Function? 03:46

Filtering List Comprehensions 03:33

Exercise: Filter the Nobel Winners 08:00

The map() Function: Overview 00:53

What Is the map() Function? 02:13

How to Use the map() Function 04:25

The map() Function vs Generator Expressions 02:28

Why Use the map() Function? 01:50

Exercise: Compute Ages With map() 08:30

The reduce() Function: Overview 01:33

What Is the reduce() Function? 04:49

Why Use the reduce() Function? 06:25

Grouping Data With itertools.groupby() 03:20

Exercise: Group Scientists by Field With reduce() 08:30

Parallel Processing With multiprocessing: Overview 00:48

Multiprocessing Testbed Overview 03:40

The multiprocessing Module 01:03

Measuring Execution Time in the multiprocessing Testbed 02:07

How to Create a multiprocessing.Pool() Object 01:40

How to Use multiprocessing.Pool() 05:14

Parallel Processing With multiprocessing: Conclusion 01:35

Exercise: Parallelize a Transform With multiprocessing 08:00

Parallel Processing With concurrent.futures: Overview 01:55

The concurrent.futures Module 02:45

How Functional Programing Makes Parallel Processing Simple 01:38

concurrent.futures vs multiprocessing 01:06

When to Use concurrent.futures or multiprocessing 02:24

Exercise: Run a Parallel map() With concurrent.futures 08:00

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