Add geoseries.distance
#1231
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Add geoseries.distance
#1231
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isVoid
added
feature request
harrism
approved these changes
Jul 26, 2023
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| # Rows with misaligned indices contains nan. Here we scatter the | ||
| # distance values to the correct indices. | ||
| res = full( |
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What does full do? What does res mean? If it's result, no need to abbreviate it.
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It's essentially an allocate and fill with specified value.
thomcom
approved these changes
Jul 28, 2023
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/merge |
rapids-bot bot
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Oct 5, 2023
closes #994 There are primitive benchmark results for `GeoSeries.distance` in #1231. This PR plans to add more benchmark coverage: TODO: - [x] point-point - [x] point-linestring - [x] point-polygon - [x] linestring-linestring - [x] linestring-polygon - [x] polygon-polygon - [x] Geometry complexity dimension - [x] Geometry spatial relationship dimension - [ ] Write the blog Authors: - Michael Wang (https://github.com/isVoid) Approvers: - Mark Harris (https://github.com/harrism) URL: #1277
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Description
closes #759
This PR adds
geoseries.distance, computing distances between two geoseries.Benchmarking distance API is a complicated task. Below I present the benchmark of a simplest case: distance between a pair of point geoseries. The benchmark is run with
geoseries.distance()API. Data setup is not counted. The input consists of 1 point per row and varies between 1K to 10M with a increment step of 10 fold. Benchmarked time is converted to throughput (points / s). In this benchmark, When input is between 10K to 100K points, geopandas performance is better than cuspatial. For larger data sizes, cuspatial can have ~10X increase in throughput to geopandas. The benchmark is ran withalign={True, False}. One of the operands is constructed with a reversed index to the other. When run withalign==True, both cuspatial and geopandas sees performance decrease and is consistent with data sizes.TODO:
Checklist