Sep
Fiona
Sep | Fiona | |
---|---|---|
2 | 3 | |
693 | 1,132 | |
- | 0.6% | |
8.8 | 8.6 | |
1 day ago | 9 days ago | |
C# | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Sep
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Eli Bendersky: Faster XML Stream Processing in Go
Pretty sure the C performance would have been trivially matched by C# were it to be ported there.
While not XML (which does not see much love from the community to surprise of HN), gigabytes per second of parsing throughput are reached for CSV without sacrificing UX: https://github.com/nietras/Sep?tab=readme-ov-file#packageass...
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Friends don't let friends export to CSV
If you ever need to parse CSV really fast and happen to know C#, there is an incredible vectorized parser for that: https://github.com/nietras/Sep/
Fiona
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Friends don't let friends export to CSV
Your issue is that you're using the default (old) binding to GDAL, based on Fiona [0].
You need to use pyogrio [1], its vectorized counterpart, instead. Make sure you use `engine="pyogrio"` when calling `to_file` [2]. Fiona does a loop in Python, while pyogrio is exclusively compiled. So pyogrio is usually about 10-15x faster than fiona. Soon, in pyogrio version 0.8, it will be another ~2-4x faster than pyogrio is now [3].
[0]: https://github.com/Toblerity/Fiona
[1]: https://github.com/geopandas/pyogrio
[2]: https://geopandas.org/en/stable/docs/reference/api/geopandas...
[3]: https://github.com/geopandas/pyogrio/pull/346
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GIS Developer career path
As has been said the definition of a GIS dev is far from written in stone, but to chime in from a personal standpoint: most of what I do is data wrangling/analysis with shapely/geopandas for vectors (or pygeos / fiona for performance when data volumes get large, but seeing as Shapely 2.0 just got released one can likely skip this part) + rasterio for rasters as well as parallelising these tasks for performance if needed (ray is great for that) and then performing machine learning learning against the data (mostly with sklearn and torch).
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Read xml with CurvePolygon with Geopandas / Fiona (Python)
TypeError 10 refers to (https://github.com/Toblerity/Fiona/blob/master/fiona/_geometry.pyx):
What are some alternatives?
gis-programming-roadmap - One stop shop for all your GIS Programming needs
rasterio - Rasterio reads and writes geospatial raster datasets
gdal - GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
geoserver-rest - Python library for management for geospatial data in GeoServer.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration