geoparquet
Fiona
| geoparquet | Fiona | |
|---|---|---|
| 3 | 3 | |
| 723 | 1,128 | |
| 4.1% | 1.1% | |
| 5.5 | 8.5 | |
| 5 days ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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geoparquet
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Friends don't let friends export to CSV
That's why I'm working on the GeoParquet spec [0]! It gives you both compression-by-default and super fast reads and writes! So it's usually as small as gzipped CSV, if not smaller, while being faster to read and write than GeoPackage.
Try using `GeoDataFrame.to_parquet` and `GeoPandas.read_parquet`
[0]: https://github.com/opengeospatial/geoparquet
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COMTiles (Cloud Optimized Map Tiles) hosted on Amazon S3 and Visualized with MapLibre GL JS
GeoParquet
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Postgres and Parquet in the Data Lke
> "Generating Parquet"
It is also useful for moving data from Postgres to BigQuery! ( batch load )
https://cloud.google.com/bigquery/docs/loading-data-cloud-st...
Thanks for the "ogr2ogr" trick! :-)
I hope the next blog post will be about GeoParquet and storing complex geometries in parquet format :-)
https://github.com/opengeospatial/geoparquet
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?
mbtiles-spec - specification documents for the MBTiles tileset format
gis-programming-roadmap - One stop shop for all your GIS Programming needs
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
rasterio - Rasterio reads and writes geospatial raster datasets
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
gdal - GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
flatgeobuf - A performant binary encoding for geographic data based on flatbuffers
geoserver-rest - Python library for management for geospatial data in GeoServer.
postgres_vectorization_test - Vectorized executor to speed up PostgreSQL
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
parquet_fdw - Parquet foreign data wrapper for PostgreSQL