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Distance Python Packages

Python packages with the GitHub topic distance. Sorted by monthly downloads, with stars and forks.
qlustered
deepdiff

DeepDiff: Deep Difference and search of any Python object/data. DeepHash: Hash of any object based on its contents. Delta: Use deltas to reconstruct objects by adding deltas together.

54.1M 3K 291
mapado
haversine

Calculate the distance between 2 points on Earth

3.4M 370 67
orsinium
textdistance

📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

2M 4K 262
seperman
deepdiff6

DeepDiff: Deep Difference and search of any Python object/data. DeepHash: Hash of any object based on its contents. Delta: Use deltas to reconstruct objects by adding deltas together.

279K 3K 291
rapidsai
libraft-cu12

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

220K 1K 252
rapidsai
pylibraft-cu12

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

218K 1K 252
genthalili
searoute

A python package to calculate the shortest sea route between two points on Earth.

112K 160 32
nvidia
libcuvs-cu12

cuVS - a library for vector search and clustering on the GPU

49K 864 241
rapidsai
raft-dask-cu12

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

47K 1K 252
nvidia
cuvs-cu12

cuVS - a library for vector search and clustering on the GPU

32K 864 241
rapidsai
libraft-cu13

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

25K 1K 252
rapidsai
pylibraft-cu13

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

25K 1K 252
cjekel
similaritymeasures

Quantify the difference between two arbitrary curves in space

22K 268 41
wmayner
pyemd

[Deprecated; use POT instead] Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

19K 493 63
nvidia
libcuvs-cu13

cuVS - a library for vector search and clustering on the GPU

19K 864 241
nvidia
cuvs-cu13

cuVS - a library for vector search and clustering on the GPU

10K 864 241
brandonxiang
geojson-utils

Python helper functions for manipulating GeoJSON

8K 88 18
dschwoerer
eudist

eudist is a library to caculate EUclidean DISTances.

5K 1 0
pierre-rouanet
dtw

DTW (Dynamic Time Warping) python module

4K 1K 236
slawek87
geolocation-python

Geolocation is a straightforward Google Maps API designed for Python users. With this app, you can easily access location-based information such as country, city, street, reverse geocode, street number, latitude, longitude, and travel distance and time for a range of origins and destinations.

4K 103 43
rapidsai
raft-dask-cu13

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

2K 1K 252
duboviy
dist

:world_map: Python/C API extension module that computes distance between two coordinates on the world map

2K 13 6
sgoodm
distancerasters

Generate distance raster given set of geometry features

1K 21 4
bast
polygons

Fast points-in-polygon test and distances to polygons.

900 20 3
    • Original data from PyPI, GitHub, and OSV; downloads served through ClickHouse
    • Sources & methodology