PyGeoHash
A simple, lightweight, and dependency-free Python library for working with geohashes.
Full docs at pygeohash.mcginniscommawill.com
What is PyGeoHash?
PyGeoHash is a Python module that provides functions for encoding and decoding geohashes to and from latitude and longitude coordinates, along with utilities for performing calculations and approximations with them.
It was originally based on Leonard Norrgård's geohash module, but now adds more functionality while supporting Python 3, and is optimized for performance.
Why PyGeoHash?
- Zero Dependencies: Works with just the Python standard library
- Simple API: Clean, intuitive functions that are easy to understand
- Lightweight: Minimal overhead for your projects
- Python 3 Support: Fully compatible with modern Python
- Robust Implementation: Reliable geohash operations
- Optional Visualization: Visualize geohashes with matplotlib and folium
- Extensively Tested: Comprehensive test suite validated against geohash.org
Installation
# Basic installation
pip install pygeohash
# With visualization support
pip install pygeohash[viz]
# With the cross-library benchmark comparison dependencies
pip install pygeohash[benchmark]
Quick Start
import pygeohash as pgh
# Encode coordinates to geohash
geohash = pgh.encode(latitude=42.6, longitude=-5.6)
print(geohash) # 'ezs42e44yx96'
# Control precision
short_geohash = pgh.encode(latitude=42.6, longitude=-5.6, precision=5)
print(short_geohash) # 'ezs42'
# Decode geohash to coordinates
lat, lng = pgh.decode(geohash="ezs42")
print(lat, lng) # 42.60498046875 -5.60302734375
# Calculate approximate distance between geohashes (in meters)
distance = pgh.geohash_approximate_distance(geohash_1="bcd3u", geohash_2="bc83n")
print(distance) # 625441
# Get adjacent geohash
adjacent = pgh.get_adjacent(geohash="kd3ybyu", direction="right")
print(adjacent) # 'kd3ybyv'
Visualization
PyGeoHash includes optional visualization capabilities:
# Plot a single geohash
from pygeohash.viz import plot_geohash
import matplotlib.pyplot as plt
fig, ax = plot_geohash("9q8yyk", color="red")
plt.show()
# Plot multiple geohashes
from pygeohash.viz import plot_geohashes
geohashes = ["9q8yyk", "9q8yym", "9q8yyj"]
fig, ax = plot_geohashes(geohashes, colors="viridis")
plt.show()
# Create interactive maps with Folium
from pygeohash.viz import folium_map
m = folium_map(center_geohash="9q8yyk")
m.add_geohash("9q8yyk", color="red")
m.add_geohash_grid(precision=6)
m.save("geohash_map.html")
Generating Example Visualizations
To generate example visualizations for the documentation, you can use the provided Makefile command:
# Install visualization dependencies
make install-viz
# Generate visualization examples
make viz-examples
This will create static images and interactive maps in the docs/source/_static/images directory.
Features
- Encode coordinates to geohash strings
- Decode geohash strings back to coordinates
- Calculate approximate distances between geohashes
- Find adjacent geohashes in any direction
- Control precision of geohash encoding
- Bounding box calculations
- Visualize geohashes on static and interactive maps
Testing and Accuracy
PyGeoHash is extensively tested to ensure accuracy in geohash encoding and decoding:
- Comprehensive Test Suite: Includes over 450 test cases covering various precision levels and geographic regions
- Validated Against geohash.org: All test cases are validated against the reference implementation at geohash.org
- Edge Case Coverage: Special attention to edge cases like poles, equator, date line, and precision boundaries
- Roundtrip Consistency: Ensures encode->decode->encode operations produce consistent results
- Regional Coverage: Test cases span all continents and major geographic features
To run the tests:
# Run the standard test suite
make test
# Run tests with coverage
make test-cov
Use Cases
- Location-based services
- Spatial indexing
- Proximity searches
- Geographic data clustering
- Location encoding with privacy considerations
- Geospatial data visualization
Blog Posts
For more insights about PyGeoHash, check out these blog posts:
Contributing
Contributions are welcome! Feel free to submit a Pull Request.
License
This project is licensed under the MIT license. See the LICENSE file for details. Prior to version 3.0.0's rewrite the project was licensed under the GPL-3.0 license.
Acknowledgments
- Originally based on Leonard Norrgård's geohash module (since re-written)
Benchmarks
How PyGeoHash improved over time
Median time per call in nanoseconds — lower is better. Every cell comes from
one measured session: the suite in tests/test_benchmark_comparison.py runs
one discarded warmup pass and then three timed passes, and every measured call
asserts its result; all five pygeohash columns were captured with the same
3.5.0 suite on one machine in one session.
| Operation | pygeohash 3.2.0 † | pygeohash 3.3.0 † | pygeohash 3.3.2 † | pygeohash 3.4.0 | pygeohash 3.5.0 |
|---|---|---|---|---|---|
| encode | 735 (725–743) | 413 (413–417) | 602 (602–721) | 473 (472–476) | 470 (470–474) |
| decode | 4,315 (4,276–4,400) | 944 (899–946) | 1,023 (1,011–1,070) | 616 (614–638) | 584 (579–1,011) ¹ |
| bounding box | 4,267 (4,260–4,308) | 1,516 (1,456–1,520) | 2,455 (2,394–2,512) | 1,192 (1,175–1,222) | 1,067 (1,045–1,087) |
| is_valid_geohash | 1,703 (1,639–1,710) | 1,672 (1,638–1,674) | 1,691 (1,681–1,703) | 490 (484–498) | 505 (499–510) |
| get_adjacent | 2,580 (2,552–2,593) | 2,582 (2,572–2,613) | 5,924 (5,848–6,022) | 1,384 (1,378–1,393) | 1,382 (1,361–1,385) |
| get_adjacent (border wrap) | 7,188 (7,163–7,209) | 7,134 (7,097–7,156) | 15,858 (15,680–16,266) | 1,810 (1,798–1,821) | 1,773 (1,710–1,774) |
| geohashes_in_box (4-cell, p9) | 87,374 (87,124–88,156) | 40,156 (39,642–40,449) | 110,569 (107,392–110,930) | 14,117 (14,088–14,729) | 13,455 (13,312–13,583) |
| geohashes_in_box (361-cell, p6) | 9,446,023 (9,248,468–9,485,611) | 4,158,630 (4,129,873–4,282,572) | 6,111,628 (5,925,939–6,130,139) | 300,203 (298,570–309,776) | 287,850 (286,010–289,168) |
- All five pygeohash columns were measured with the same 3.5.0 suite on one machine (Linux x86_64, Intel Xeon @ 2.60 GHz, 8 vCPUs) in one session, in release order 3.2.0 → 3.3.0 → 3.3.2 → 3.4.0 → 3.5.0, every build from source on CPython 3.13.14. Per column: one discarded warmup pass, then three timed passes; each cell is the median of the per-run medians with the observed low–high range.
- † Releases 3.2.0, 3.3.0, and 3.3.2 predate #144's deterministic-sorted
geohashes_in_boxcontract, so their columns were measured under a harness copy whose box-output assertions were relaxed to order-insensitive equality; every other assertion ran unchanged. - ¹ The 3.5.0 decode range includes one disclosed transient pass; the quoted median is the suite's median-of-three aggregation, which is robust to it.
Relative to 3.2.0, the 3.5.0 column improves every measured operation: geohashes_in_box on a 361-cell box 9.46 ms → 288 µs (−97%); decode 4,315 → 584 ns (−86%); the 4-cell box 87.4 → 13.5 µs (−85%); the border-wrap adjacency 7,188 → 1,773 ns (−75%); bounding box 4,267 → 1,067 ns (−75%); is_valid_geohash 1,703 → 505 ns (−70%); get_adjacent 2,580 → 1,382 ns (−46%); encode 735 → 470 ns (−36%). The path was not monotonic: 3.3.2 measured as a real regression against 3.3.0 on several operations — get_adjacent +129%, the border wrap +122%, 4-cell box enumeration +175%, encode +46% — with the recoveries landing in 3.4.0. Between 3.4.0 and 3.5.0, decode improves 5.2% and bounding box 10.5%, while encode is unchanged within measurement resolution (−0.5%).
How PyGeoHash compares to other libraries
Median time per call in nanoseconds — lower is better. Every cell comes from the published comparison run of 2026-09-06 — same machine class, CPython 3.13.14, pytest-benchmark 5.3.0, one warmup pass plus three timed passes — a separate run from the session behind the over-time table above. An em-dash (—) marks a genuine API gap: the library does not expose the operation. An em-dash is never a placeholder.
| Operation | geohashr 1.6.0 | pygeohash-fast 0.3.0 | python-geohash 0.9.2 | geohash-tools 0.2.0 | pygeohash 3.5.0 |
|---|---|---|---|---|---|
| encode | 353 (341–361) | 455 (451–455) | 635 (630–644) | 12,014 (11,994–12,183) | 475 (473–576) |
| decode | 231 (228–234) | 545 (543–551) | 791 (781–826) | 8,439 (8,416–8,453) | 554 (553–558) |
| bounding box | 317 (313–318) | — | 929 (927–932) | — | 1,058 (1,043–1,125) |
| is_valid_geohash | — | — | — | — | 382 (379–383) |
| get_adjacent | 372 (360–378) | — | — | 933 (907–933) | 1,397 (1,379–1,406) |
| get_adjacent (border wrap) | 358 (356–366) | — | — | 2,267 (2,253–2,293) | 1,798 (1,784–1,850) |
| geohashes_in_box (4-cell, p9) | — | — | — | — | 13,602 (13,542–13,703) |
| geohashes_in_box (361-cell, p6) | — | — | — | — | 293,134 (291,317–294,377) |
- This table is backed by the separate published run (linked in the paragraph below), not by the session behind the over-time table — the two runs are distinct, and numbers are never mixed across them.
- The published run applies a 20% across-repeat stability gate after warmup and flagged pygeohash's encode spread at 21.6%, over that gate.
On this run, geohashr and pygeohash-fast are faster than pygeohash on
encode and decode, geohashr and geohash-tools are faster on single-cell
adjacency (on the border-wrap variant only geohashr is), and geohashr and
python-geohash are faster on bounding box. No competitor exposes a
standalone validity check (is_valid_geohash) or box enumeration
(geohashes_in_box). The
benchmarks page
also measures two further pure-Python libraries, libgeohash and geolib,
which trail far behind the libraries shown here.
Version history
The tables below compare PyGeoHash 3.0.0 to PyGeoHash 2.1.0 — a record of the v3.0.0 CPython rewrite, not a comparison against other libraries. They were produced with a much older pytest-benchmark and are kept for history only.
Version 3.0.0
| Name (time in ns) | Min | Max | Mean | StdDev | Median | IQR | Outliers | OPS (Kops/s) | Rounds | Iterations |
|---|---|---|---|---|---|---|---|---|---|---|
| test_encode_benchmark | 614.0035 (1.0) | 1,170,750.9984 (1.34) | 1,119.5566 (1.0) | 6,011.2433 (1.35) | 714.0043 (1.0) | 664.4987 (7.30) | 370;1371 | 893.2107 (1.0) | 117772 | 1 |
| test_approximate_distance_benchmark | 901.9859 (1.47) | 872,125.0051 (1.0) | 1,346.1119 (1.20) | 4,467.1457 (1.0) | 1,031.0032 (1.44) | 90.9786 (1.0) | 463;13144 | 742.8803 (0.83) | 71891 | 1 |
| test_decode_benchmark | 2,791.9887 (4.55) | 3,266,092.0115 (3.74) | 4,683.3561 (4.18) | 22,447.8219 (5.03) | 3,102.9922 (4.35) | 2,965.2583 (32.59) | 103;302 | 213.5221 (0.24) | 26769 | 1 |
| test_haversine_distance_benchmark | 3,989.0001 (6.50) | 1,066,400.9897 (1.22) | 5,441.2395 (4.86) | 10,068.4850 (2.25) | 4,475.0050 (6.27) | 330.0083 (3.63) | 429;3534 | 183.7817 (0.21) | 28312 | 1 |
Version 2.1.0
| Name (time in ns) | Min | Max | Mean | StdDev | Median | IQR | Outliers | OPS (Kops/s) | Rounds | Iterations |
|---|---|---|---|---|---|---|---|---|---|---|
| test_approximate_distance_benchmark | 903.0045 (1.0) | 2,239,810.0118 (145.76) | 1,242.2962 (1.0) | 8,910.5317 (2.45) | 1,034.0009 (1.0) | 83.0041 (1.0) | 411;12404 | 804,960.9836 (1.0) | 126872 | 1 |
| test_numba_point_decode_benchmark | 5,554.9899 (6.15) | 28,678.0123 (1.87) | 11,125.8007 (8.96) | 9,922.0335 (2.73) | 6,597.9839 (6.38) | 8,462.7463 (101.96) | 1;1 | 89,881.1717 (0.11) | 5 | 1 |
| test_numba_point_encode_benchmark | 6,829.9996 (7.56) | 15,366.0076 (1.0) | 8,949.4046 (7.20) | 3,633.1020 (1.0) | 7,203.9838 (6.97) | 3,100.7585 (37.36) | 1;1 | 111,739.2773 (0.14) | 5 | 1 |
| test_decode_benchmark | 9,094.0157 (10.07) | 564,207.9923 (36.72) | 23,505.3902 (18.92) | 47,847.8575 (13.17) | 19,235.4928 (18.60) | 10,221.4981 (123.14) | 4;9 | 42,543.4333 (0.05) | 156 | 1 |
| test_encode_benchmark | 16,131.9913 (17.86) | 6,522,867.0137 (424.50) | 43,962.7989 (35.39) | 204,118.7023 (56.18) | 30,353.0251 (29.35) | 5,316.5204 (64.05) | 12;160 | 22,746.5044 (0.03) | 1081 | 1 |
| test_haversine_distance_benchmark | 19,751.9839 (21.87) | 2,229,443.9841 (145.09) | 24,979.8646 (20.11) | 27,057.9632 (7.45) | 21,632.0041 (20.92) | 1,626.0019 (19.59) | 453;3700 | 40,032.2426 (0.05) | 24647 | 1 |
| test_numba_vector_decode_benchmark | 887,114.0017 (982.40) | 1,070,945.9893 (69.70) | 974,148.7971 (784.15) | 72,131.5386 (19.85) | 977,645.9774 (945.50) | 111,056.4845 (>1000.0) | 2;0 | 1,026.5372 (0.00) | 5 | 1 |
| test_numba_vector_encode_benchmark | 6,603,729.9985 (>1000.0) | 9,492,440.0037 (617.76) | 8,344,232.0058 (>1000.0) | 1,069,465.9606 (294.37) | 8,602,735.0044 (>1000.0) | 1,065,492.2444 (>1000.0) | 2;0 | 119.8433 (0.00) | 5 | 1 |
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BLAKE2b-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / pygeohash-3.5.1-cp310-cp310-macosx_10_9_x86_64.whl
| Download URL | pygeohash-3.5.1-cp310-cp310-macosx_10_9_x86_64.whl |
|---|---|
| Size | 46.3 kB |
| Tags | CPython 3.10 macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
b8b53327b2f2086062ee37d670826afe77baa01e5a9fb841b5f3bb65cda18a02
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Yes |
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twine/7.0.0 CPython/3.13.14
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