From Python's Functional Primitives to Parallelism – Certificate of Completion

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This is to certify that

Dan Bader

has successfully completed

From Python's Functional Primitives to Parallelism

a course of study offered by Real Python (realpython.com), an online learning platform and expert community for Python software developers.

Completion Date: Aug. 27, 2019

Verification ID: 8b0cdec4-1ecf-4f76-a731-01dd986a70b9

Course Description

Skill level: advanced

Use a functional approach, immutability, and map, filter, reduce to reach parallelism with multiprocessing and concurrent.futures in clear Python examples.

Section 1: Immutable Data Structures

1. Functional Programming Course Overview

2. What Is Functional Programming?

3. Mutable Data Structures: Lists and Dictionaries

4. Immutable Data Structures: namedtuple

5. Danger Zone: Mixing Mutable and Immutable Data Structures

6. Immutable Data Structures: Tuples

7. Exercise: Build an Immutable Scientist Dataset

Section 2: The filter() Function

1. The filter() Function: Overview

2. What Is the filter() Function?

3. Storing Filtered Data in a Tuple

4. Why Use the filter() Function?

5. Filtering List Comprehensions

6. Exercise: Filter the Nobel Winners

Section 3: The map() Function

1. The map() Function: Overview

2. What Is the map() Function?

3. How to Use the map() Function

4. The map() Function vs Generator Expressions

5. Why Use the map() Function?

6. Exercise: Compute Ages With map()

Section 4: The reduce() Function

1. The reduce() Function: Overview

2. What Is the reduce() Function?

3. Why Use the reduce() Function?

4. Grouping Data With itertools.groupby()

5. Exercise: Group Scientists by Field With reduce()

Section 5: Parallel Processing With multiprocessing

1. Parallel Processing With multiprocessing: Overview

2. Multiprocessing Testbed Overview

3. The multiprocessing Module

4. Measuring Execution Time in the multiprocessing Testbed

5. How to Create a multiprocessing.Pool() Object

6. How to Use multiprocessing.Pool()

7. Parallel Processing With multiprocessing: Conclusion

8. Exercise: Parallelize a Transform With multiprocessing

Section 6: Parallel Processing With concurrent.futures

1. Parallel Processing With concurrent.futures: Overview

2. The concurrent.futures Module

3. How Functional Programing Makes Parallel Processing Simple

4. concurrent.futures vs multiprocessing

5. When to Use concurrent.futures or multiprocessing

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

View Course Details »

Certificate of Completion

This person’s account is verified and was created on Feb. 26, 2019. Real Python certifies their completion of the From Python's Functional Primitives to Parallelism course on Aug. 27, 2019.

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