Faster Python Code

Focused View

Miki Tebeka

2:05:43

248 View

01 - Introduction

01 - Welcome.mp4

00:54

02 - What you should know.mp4

01:18

03 - Use Codespaces with this course.mp4

00:52

02 - 1. Tools of the Trade

01 - Always profile first.mp4

01:33

02 - General tips.mp4

01:40

03 - Measuring time.mp4

04:55

04 - CPU profiling.mp4

06:54

05 - line profiler.mp4

05:07

06 - Tracing memory allocations.mp4

03:53

07 - memory profiler.mp4

02:23

03 - 2. Picking the Right Data Structure

01 - Big-O notation.mp4

02:30

02 - bisect.mp4

03:18

03 - deque.mp4

03:00

04 - heapq.mp4

03:52

05 - Beyond the standard library.mp4

05:15

04 - 3. Tricks of the Trade

01 - Local caching of names.mp4

03:23

02 - Remove function calls.mp4

03:16

03 - Using slots .mp4

03:24

04 - Built-ins.mp4

03:02

05 - Allocate.mp4

03:06

05 - 4. Caching

01 - Overview.mp4

02:46

02 - Pre-calculating.mp4

02:37

03 - lru cache.mp4

03:03

04 - Joblib.mp4

03:26

06 - 5. Cheating

01 - When approximation is good enough.mp4

01:16

02 - Cheating example.mp4

04:21

07 - 6. Parallel Computing

01 - Amdahls Law.mp4

04:13

02 - Threads.mp4

02:56

03 - Processes.mp4

02:59

04 - asyncio.mp4

05:16

08 - 7. Beyond Python

01 - NumPy.mp4

03:55

02 - Numba.mp4

03:50

03 - Cython.mp4

04:38

04 - PyPy.mp4

02:05

05 - C extensions.mp4

03:44

09 - 8. Adding Optimization to Your Process

01 - Why do we need a process.mp4

01:11

02 - Design and code reviews.mp4

02:51

03 - Benchmarks.mp4

03:15

04 - Monitoring and alerting.mp4

02:41

10 - Conclusion

01 - Next steps.mp4

01:05

Description

By optimizing your Python code, you can ensure that your code uses fewer resources and runs faster than it did previously. In this advanced course, explore tips and techniques that can help you optimize your code to make it more efficient. Instructor Miki Tebeka covers general tools of the trade, including how to leverage the tools Python provides for measuring time, and how to use line_profiler to get line-by-line profiling information. Miki also shares how to pick the right data structures, how approximation algorithms can speed up your code, and how to use NumPy for fast numeric computation. He wraps up the course with a discussion of how to integrate performance in your process.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Miki Tebeka

Instructor's Courses

Teaching effective hands-on workshops all of the world. Consultant solving hard problem with the right tools (Java and C++ are *not* the right tools ;). Book author, LinkedIn learning Author, open source contributor and convention organizer, meetup co host and coding for fun in my spare time. Specialties: Python & Scientific Python (Expert), Go (Expert), C/C++, Clojure, JavaScript, bash, ... Information retrieval - tokenization, summarization, clustering, search ... Concurrency - Multi process, multi threaded, Hadoop ... Web development - REST APIs, jQuery, JavaScript, CSS, (X)HTML Assemblers, Linkers, Debugger, Simulators SCM tools (git, Mercurial, Perforce, subversion, CVS, ClearCase) Linux, OS X and Windows Functional Programming, OOD, OOP Databases - SQL (BigQuery, PostgreSQL, MySQL, Oracle) and NoSQL (Redis, MongoDB, CouchDB)

Linkedin Learning

View courses Linkedin Learning

LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.