3. Background#
This section provides supplementary background information on key concepts, methodologies, tools, and technologies used in PyPackIT.
Discover more about different software development methodologies such as Agile, pull-based, and test-driven development, as well as cloud-native practices like Continuous software engineering, DevOps, and containerization.
Learn more about the GitHub social coding platform, GitHub repositories, and various services including Actions, Codespaces, Discussions, Issues, Pages, pull requests, and repository templates.
Read more on the Python programming language, its rich ecosystem of tools and libraries, best practices, and governance model. Familiarize yourself with terms such as PyPI, PyPA, Pip, PEP, and PSF.
Find out about the Conda ecosystem and learn more about Anaconda.org, Anaconda distribution, Conda, Miniconda, Conda-forge, Miniforge, Mamba, and Micromamba.
Dive deeper into software versioning practices and schemes, including Semantic Versioning and Python version specifiers.
Learn more about JSONPath path expressions and how they can be used to query data from serializable data structures like JSON and YAML.
Check out further information on the YAML data serialization format, such as available data structures and syntax, as well as advanced features like anchors and tags.
Find out about the TOML data serialization format,
which is used in Python projects to define package configuration
and metadata in the pyproject.toml file.
Explore further details on the Jinja templating engine and its features and syntax including expressions, logic statements, macros, filters, variables, conditionals, and loops.
Learn more about the Markdown markup language and its various flavors like CommonMark, GitHub-Flavored Markdown, Pandoc Markdown, and MyST Markdown for writing rich and technical documents.
External Resources
A full list of literature cited throughout the documentation is provided in References. Freely available online resources include:
The Turing Way handbook to reproducible, ethical and collaborative data science.
Python Tutorials & Learning Resources for Scientists by the pyOpenSci community.