CITATION.cff

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cff-version: 1.2.0
title: Performance Profiling & Optimisation (Python)
message: >-
  Please cite this lesson using the information in this file
  when you refer to it in publications, and/or if you
  re-use, adapt, or expand on the content in your own
  training material. To cite the Workbench software itself,
  please refer to the websites for the individual
  components:
  https://carpentries.github.io/sandpaper/authors.html#citation,
  https://carpentries.github.io/pegboard/authors.html#citation,
  https://carpentries.github.io/varnish/authors.html#citation
type: dataset
authors:
  - given-names: Robert
    family-names: Chisholm
    email: robert.chisholm@sheffield.ac.uk
    affiliation: University of Sheffield
    orcid: 'https://orcid.org/0000-0003-3379-9042'
  - given-names: Jost
    family-names: Migenda
    email: jost.migenda@kcl.ac.uk
    affiliation: King’s College London
    orcid: 'https://orcid.org/0000-0002-5350-8049'
identifiers:
  - type: doi
    value: 10.5281/zenodo.16902755
    description: This doi cites all versions of the code
repository-code: 'https://github.com/carpentries-incubator/pando-python'
url: 'https://carpentries-incubator.github.io/pando-python/'
repository: 'https://github.com/carpentries-incubator/pando-python'
abstract: >-
  This lesson introduces the basics of profiling and
  optimising Python code. The course is designed to be
  accessible to Python users of all skill levels (beyond
  total beginner). The optimisations presented should be
  considered performance best practices, they are
  demonstrated with small programming patterns that
  demonstrate multiple approaches in code to achieve the
  same result with differing performance.
keywords:
  - open-educational-resources
  - python
  - profiling
  - optimisation
  - programming
license: CC-BY-4.0