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LinkedIn Learning

Faster Python Code

via LinkedIn Learning

Overview

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Learn tips to help optimize your Python code. Discover how to pick the right data structures, use caching, integrate performance in your process, and more.

Syllabus

Introduction
  • Welcome
  • What you should know
  • Use Codespaces with this course
1. Tools of the Trade
  • Always profile first
  • General tips
  • Measuring time
  • CPU profiling
  • line_profiler
  • Tracing memory allocations
  • memory_profiler
2. Picking the Right Data Structure
  • Big-O notation
  • bisect
  • deque
  • heapq
  • Beyond the standard library
3. Tricks of the Trade
  • Local caching of names
  • Remove function calls
  • Using __slots__
  • Built-ins
  • Allocate
4. Caching
  • Overview
  • Pre-calculating
  • lru_cache
  • Joblib
5. Cheating
  • When approximation is good enough
  • Cheating example
6. Parallel Computing
  • Amdahl's Law
  • Threads
  • Processes
  • asyncio
7. Beyond Python
  • NumPy
  • Numba
  • Cython
  • PyPy
  • C extensions
8. Adding Optimization to Your Process
  • Why do we need a process?
  • Design and code reviews
  • Benchmarks
  • Monitoring and alerting
Conclusion
  • Next steps

Taught by

Miki Tebeka

Reviews

4.6 rating at LinkedIn Learning based on 119 ratings

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