Introduction
Every developer has that one project that started as a personal solution and unexpectedly found a life of its own. For me, that was FastKML, a library I built in 2012 to “scratch my own itch.” I needed to embed maps into a website, and at the time, KML was the de facto standard for visualizing geospatial data on the web. GeoJSON existed but was still in its infancy and unsupported by OpenLayers, which was then the best tool for embedding maps.
Other Python libraries for KML existed, but most were either limited in scope, lacked Python 3 support, or didn’t meet my performance needs. Performance was crucial, so I built FastKML using lxml instead of the slower XML DOM used by many contemporaries.
As FastKML evolved, it depended on Shapely for geometry handling, an excellent library, but one that required C extensions and added installation complexity. That led to the birth of PyGeoIf, a pure Python implementation of basic geospatial objects. PyGeoIf aimed to serve as a lightweight, dependency-free substitute for Shapely when users didn’t need all of its advanced geometry operations. The API mirrored Shapely’s closely, making migration as simple as replacing
from pygeoif import ...
with
from shapely import ...
Over the years, both projects aged gracefully, but not without technical debt. They bore the marks of an earlier Python era: Python 2/3 compatibility hacks (at the very beginning Python 2.6 was still in use), missing type hints, and occasionally ambiguous function signatures.
Still, they worked. The test coverage exceeded 95%, bugs were rare, and they continued solving real problems for users long after I had moved on to other roles outside GIS. To my surprise, the packages remained popular; downloads were steady, and employers still asked about them. But I knew the code looked dated, and if I had to review it today, it wouldn’t pass.
Fast forward to 2020. The geospatial landscape had changed; GeoJSON had overtaken KML, Python’s ecosystem had matured, and I had learned a great deal about clean code and maintainability. It was time to modernize these legacy projects for the new decade.
The Need for Change
Modernization wasn’t just a matter of adding type hints or updating syntax, it was about bringing two long-lived projects in line with modern development practices. The original codebases had served well for years, but they were increasingly difficult to extend. Function signatures were ambiguous, internal logic was tangled, and adding new features often caused shotgun surgery, requiring edits across multiple unrelated files.
The Shapely API had evolved too, fully embracing PEP 8 naming conventions and adopting more expressive methods. To remain compatible, PyGeoIf needed to evolve alongside it. Meanwhile, Python itself had transformed: type hints, static analysis, and property-based testing were now standard practice rather than novelty.
Drivers of Change
The single most important motivator was the introduction of type hints in Python. Type annotations have revolutionized how Python code is written, reviewed, and maintained, enhancing readability and catching subtle bugs early through tools like mypy.
The first step was static analysis with tools like mypy, which immediately flagged legacy Python 2 compatibility hacks, ambiguous function signatures, and missing type hints. Extending the tests in tandem ensured that each refactor preserved correctness.
Beyond that, the desire for clearer APIs, more maintainable structures, and modern testing techniques pushed the modernization effort forward. I wanted code that not only worked but was readable, testable, and future-proof.
A Tale of Two Refactors
For PyGeoIf, version 0.7 had been released in 2017. Four years later, in September 2021, I published the first beta of the 1.0 series: fully type-annotated, statically checked with mypy, and tested using property-based testing with Hypothesis and improved tests with mutation testing with MutMut. By September 2022, version 1.0 was stable, and by October 2025, it had matured to version 1.5.
For FastKML, after a long silence since version 0.11 in 2015, I released version 0.12 in September 2021, incorporating long-neglected pull requests and minor improvements. A month later came FastKML 1.0 alpha 1 on PyPI. What I thought would be a quick release became an 18-iteration journey spanning three years, culminating in version 1.0 in November 2024; finally the library I had envisioned years earlier.
Reflecting on Contributions and Community Support
Over the past few years of developing PyGeoIf and FastKML, the journey has been shaped not only by personal effort but also by the support and engagement of the open-source community. One striking example of this has been Hacktoberfest contributions which consistently provided motivation and tangible progress.
These contributions may seem small individually, but collectively they have kept the momentum going. Seeing community members engage with the projects during Hacktoberfest has been a continuing source of encouragement, reminding me that every bit of contribution helps make the software more robust, maintainable, and welcoming to others.
The positive impact goes beyond the specific changes. Hacktoberfest contributions have:
- Encouraged ongoing improvement by motivating incremental updates.
- Highlighted the value of community participation in maintaining and modernizing open-source projects.
- Reinforced a sense of shared purpose, showing that even small efforts can collectively advance a project.
This ongoing collaboration has made the development process more rewarding and sustainable, reinforcing a simple but powerful lesson: in open-source, community engagement isn’t just about code, it’s about inspiration and momentum.
Hacktoberfest contributions aren’t just code, they’re encouragement. They spark incremental improvements, highlight the value of shared effort, and inspire continued development. Seeing others invest their time and ideas in these projects has been a constant source of motivation to keep improving, testing, and refining.
Hacktoberfest and the Power of Community
Developing PyGeoIf and FastKML has been a journey of learning, coding, and refining, but it’s the community contributions, especially during Hacktoberfest, that have truly kept the momentum alive.
October has consistently brought a wave of engagement: pre-commit hooks, bug fixes, minor enhancements, and automated improvements. Each contribution, no matter how small, reinforced the sense of progress and reminded me that open-source thrives on collaboration.
Looking forward, this collaborative energy continues to fuel future features and refinements. Hacktoberfest has proven that even small contributions can make a big difference, both in the code and in the spirit of the community.
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