NetworkX aims to be the reference library for network science algorithms in Python. We accomplish this by:
being easy to use and install. We are careful in taking on new dependencies, and sometimes cull existing ones, or make them optional. All functions in our API have thorough docstrings clarifying expected inputs and outputs.
providing a consistent API. Conceptually identical arguments have the same name and position in a function signature.
ensuring correctness. Test coverage is close to 100% and code is reviewed by at least two core developers before being included in the library.
caring for users’ data. We have a functional API and don’t modify input data unless explicitly directed to do so.
promoting education in network science, with extensive pedagogical documentation.
We are inclusive (Code of Conduct). We welcome and mentor newcomers who are making their first contribution.
We are open source and community-driven (NXEP 1 — Governance and Decision Making).
We focus on graph data structures and algorithms for network science applications.
We prefer pure Python implementations using native data structures (especially dicts) due to their consistent, intuitive interface and amazing performance capabilities. We include interfaces to other data structures, especially NumPy arrays and SciPy sparse matrices for algorithms that more naturally use arrays and matrices or where time or space requirements are significantly lower. Sometimes we provide two algorithms for the same result, one using each data structure, when pedagogy or space/time trade-offs justify such multiplicity.
We value simple, readable implementations over getting every last ounce of performance. Readable code that is easy to understand, for newcomers and maintainers alike, makes it easier to contribute new code as well as prevent bugs. This means that we will prefer a 20% slowdown if it reduces lines of code two-fold, for example.
We value education and documentation. All functions should have NumPy-style docstrings, preferably with examples, as well as gallery examples that showcase how that function is used in a scientific application.
This document is modified from the scikit-image mission and values document.
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