We run peer review of scientific Python software. Learn more.

Python package structure information#

This section provides guidance on your Python package’s structure, code formats and style. It also reviews the various packaging tools that you can use to support building and publishing your package.

If you are confused by Python packaging, you are not alone! The good news is there are some great modern packaging tools that ensure that you’re following best practices. Here, we review tool features and suggest tools that might be best fitted for your workflow.

✨ 1. Package file structure ✨

src layout, flat layout and where should tests folders live? No matter what your level of packaging knowledge is, this page will help you decide upon a package structure that follows modern python best practices.

Python Package Structure for Scientific Python Projects
✨ 2. Learn about building your package ✨

To publish your Python package on PyPI, you will need to first build it. The act of “building” refers to the process of placing your package code and metadata into a format that can be published on PyPI. Learn more about building your Python package.

Python Package Structure for Scientific Python Projects
✨ 4. Add metadata ✨

Learn how to add project metadata to your Python package to support both filtering on PyPI and also the metadata that a package installer needs to build and install your package.

Use a pyproject.toml file for your package configuration & metadata
✨ 3. What Python package tool should you use? ✨

Learn more about the suite of packaging tools out there. And learn which tool might be best for you.

Python Packaging Tools
✨ 4. Publish to PyPI and Conda ✨

If you have a pure Python package, it’s a straight forward process to publish to both PyPI and then a Conda channel such as conda-forge. Learn more here.

Python Packaging Tools
✨ 5. Setup package versioning ✨

Semver (numeric versioning) and Calver (versioning using the date) are 2 common ways to version a package. Which one should you pick? Learn more here.

Creating New Versions of Your Python Package
✨ 6. Code style & linters ✨

Black, blue, flake8, Ruff - which tools can help you ensure your package follows best practices for code format? Learn more about the options and why this is important here.

Python Package Code Style, Format and Linters
Figure showing a decision tree with the various packaging tool front-end and back-end options.

Diagram showing the various front-end build tools that you can select from. See the packaging tools page to learn more about each tool.#


If you are considering submitting a package for peer review, have a look at the bare-minimum editor checks that pyOpenSci performs before a review begins. These checks are useful to explore for both authors planning to submit a package to us for review and for anyone who is just getting started with creating a Python package.

What you will learn here#

In this section of our Python packaging guide, we:

  • Provide an overview of the options available to you when packaging your tool.

  • Suggest tools and approaches that both meet your needs and also support existing standards.

  • Suggest tools and approaches that will allow you to expand upon a workflow that may begin as a pure Python tool and evolve into a tool that requires addition layers of complexity in the packaging build.

  • Align our suggestions with the most current, accepted PEPs (Python Enhancement Protocols) and the Scientific Python community SPECs.

  • In an effort to maintain consistency within our community, we also align with existing best practices being implemented by developers of core Scientific Python packages such as Numpy, SciPy and others.

Guidelines for pyOpenSci’s packaging recommendations#

The flexibility of the Python programming language lends itself to a diverse range of tool options for creating a Python package. Python is so flexible that it is one of the few languages that can be used to wrap around other languages. The ability of Python to wrap other languages is one the reasons you will often hear Python described as a “glue” language

If you are building a pure Python package, then your packaging setup can be simple. However, some scientific packages have complex requirements as they may need to support extensions or tools written in other languages such as C or C++.

To support the many different uses of Python, there are many ways to create a Python package. In this guide, we suggest packaging approaches and tools based on:

  1. What we think will be best and easiest to adopt for those who are newer to packaging.

  2. Tools that we think are well maintained and documented.

  3. A shared goal of standardizing packaging approaches across this (scientific) Python ecosystem.

Here, we also try to align our suggestions with the most current, accepted Python community and scientific community.

Suggestions in this guide are not pyOpenSci review requirements

The suggestions for package layout in this section are made with the intent of being helpful; they are not specific requirements for your package to be reviewed and accepted into our pyOpenSci open source ecosystem.

Please check out our package scope page and review requirements in our author guide if you are looking for pyOpenSci’s Python package review requirements!