Optimisation Conclusion

Overview

Questions

• What has been learnt about writing performant Python?

Objectives

• Review what has been learnt about writing performant Python

This concludes the optimisation portion of the course.

An overview of how Python operates and the most important practices for achieving performant code have been introduced.

Hopefully with the information from this course you will be in a better position to investigate and optimise the performance of your own code.

This course’s website can be used as a reference manual when profiling your own code.

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Key Points

• Data Structures & Algorithms
  • List comprehension should be preferred when constructing lists.
  • Where appropriate, Tuples and Generator functions should be preferred over Python lists.
  • Dictionaries and sets are appropriate for storing a collection of unique data with no intrinsic order for random access.
  • When used appropriately, dictionaries and sets are significantly faster than lists.
  • If searching a list or array is required, it should be sorted and searched using bisect_left() (binary search).
• Minimise Python Written
  • Python is an interpreted language, this adds an additional overhead at runtime to the execution of Python code. Many core Python and NumPy functions are implemented in faster C/C++, free from this overhead.
  • NumPy can take advantage of vectorisation to process arrays, which can greatly improve performance.
  • Pandas’ data tables store columns as arrays, therefore operations applied to columns can take advantage of NumPys vectorisation.
• Newer is Often Faster
  • Where feasible, the latest version of Python and packages should be used as they can include significant free improvements to the performance of your code.
  • There is a risk that updating Python or packages will not be possible to due to version incompatibilities or will require breaking changes to your code.
  • Changes to packages may impact results output by your code, ensure you have a method of validation ready prior to attempting upgrades.
• How the Computer Hardware Affects Performance
  • Sequential accesses to memory (RAM or disk) will be faster than random or scattered accesses.
    • This is not always natively possible in Python without the use of packages such as NumPy and Pandas
  • One large file is preferable to many small files.
  • Memory allocation is not free, avoiding destroying and recreating objects can improve performance.