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_box contract, 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

Development Status
- 3 - Alpha
Intended Audience
- Developers
License
- OSI Approved :: MIT License
Programming Language
- Python :: 3
Typing
- Typed

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MD5	`465bbda14da4af384a3a1cfe894733fa`
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MD5	`0ad6d5b65bec9d8db4ff0f08d837f5f5`
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MD5	`2e845e86096091ee13816c8ef7f693ef`
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MD5	`6cbf5c8af2a67debd5fd9a8499df6124`
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MD5	`6f496e0d8c5422708e5837934dd6c7ca`
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Algorithm	Hash digest
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MD5	`062d210a730d165006ac8f358f45c6db`
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pygeohash 3.5.1

PyGeoHash

What is PyGeoHash?

Why PyGeoHash?

Installation

Quick Start

Visualization

Generating Example Visualizations

Features

Testing and Accuracy

Use Cases

Blog Posts

Contributing

License

Acknowledgments

Benchmarks

How PyGeoHash improved over time

How PyGeoHash compares to other libraries

Version history

Version 3.0.0

Version 2.1.0

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1 maintainer

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Algorithm	Hash digest
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MD5	`eb42a41e36c2ce62125c2baf51a8d7d3`
BLAKE2b-256	`77a5ca8c1db91cd0af89c12fd9a2f5bba2bd1303ccebbff180dde52e9ef9dcbe`

Algorithm	Hash digest
SHA256	`c2e08582bb46d6d80667d2ad8255eae41f73251c0a0e4ee20d5c90bab956a65c`
MD5	`727ff0880771a67ea1c7eefe1a73bc5c`
BLAKE2b-256	`6cfeb414a9c4e459f4d6274496eaa9cfa43e5dbaa4f97ca13b4b72933290377f`

Algorithm	Hash digest
SHA256	`387bbd2383e6e09c97cbbc30f923375bf13dcad822954dc8fb0a52361544f2f7`
MD5	`7bfe42b2e8802678398fd3975b05e9b1`
BLAKE2b-256	`fdb12abe45739800131d00b96b7bf2d2a83eb182bce46f3839471f0e9564e4d3`

Algorithm	Hash digest
SHA256	`da80e608c6a75ac4caf4c8ffe85a48ed0cbfea8915aebf3ae309b9b9c85acf9d`
MD5	`e8e65500057c22c569696b4d20d29b9d`
BLAKE2b-256	`4c57d1d377eaf5dbd51ad2ade4775269d624c98efc28c47eda27f6cd5f7e2864`

Algorithm	Hash digest
SHA256	`6de7017e3efcd1480ad2d80891737a2e7da81a1740025af426a1872c20a86ef5`
MD5	`a594a8f0214d01abba89b52341b074d3`
BLAKE2b-256	`64eab9b23241b22ca39c939dcf7702062d6a56da985b628002c77196598ce099`

Algorithm	Hash digest
SHA256	`b204e483e3f766186c0929c3350c9b3f68150352c0f8a175a698ad29173a154c`
MD5	`60da1fee3295c128e76ccf3d2e4e82b5`
BLAKE2b-256	`540431bd63bf066943c27b684e2249a8eafd51d0408153acd1cd30e4175e97ed`

Algorithm	Hash digest
SHA256	`2d2b6a4013d375a4d5be0b9621b787f825000992e7e2f7dfc5ae21981d1a2ca4`
MD5	`465b1c7bc464c2e82a120ea17c466151`
BLAKE2b-256	`56623aa5bb2a1092945dfc496b0eb88d9841567fefd80fc45486d8d026d0dfb7`

Algorithm	Hash digest
SHA256	`f617af8df1e5425a9a8bdd905bf2a00962c097289e60309906d9715d8cd09a51`
MD5	`aff2b04b897a8e8511f32c2c35fb105a`
BLAKE2b-256	`560169ab078c30115e609ff5050b2d04369ea91ab94eddd0754cb54dba4226df`

Algorithm	Hash digest
SHA256	`ea27d508c9aae46e974a10677ae04cb7ab808ae7d844acec9a8a6ef92ba5017f`
MD5	`671757d12846b0115464cd3e3ac1f479`
BLAKE2b-256	`20165f8b5c50c33f17a1cc76f5af1d6c47fecbb2f4f085c646df7c92fa24fc94`

Algorithm	Hash digest
SHA256	`b8f0665aed441a312633fb53d20f841f9bde7049cf512245c55c4ecaa7f838f7`
MD5	`14c1f9116919ea2ab4421b742458f4fb`
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