Simple Python package to compute TDigests, implemented in Rust
Project description
TDigest-rs
Simple Python package to compute TDigests, implemented in Rust.
Introduction
TDigest-rs is a Python library with a Rust backend that implements the T-Digest algorithm, enhancing the estimation of quantiles in streaming data. For an in-depth exploration of the T-Digest algorithm, refer to Ted Dunning and Otmar Ertl's paper and the G-Research blog post.
Usage
pip install tdigest-rs
The library contains a single TDigest
class.
Creating a TDigest object
from tdigest_rs import TDigest
# Fit a TDigest from a numpy array (float32 or float64)
arr = np.random.randn(1000)
tdigest = TDigest.from_array(arr=arr, delta=100.0) # delta is optional and defaults to 300.0
print(tdigest.means, tdigest.weights)
# Create directly from means and weights arrays
vals = np.random.randn(1000).astype(np.float32)
weights = np.ones(1000).astype(np.uint32)
tdigest = TDigest.from_means_weights(arr=vals, weights=weights)
Computing quantiles
# Compute a quantile
tdigest.quantile(0.1)
# Compute median
tdigest.median()
# Compute trimmed mean
tdigest.trimmed_mean(lower=0.05, upper=0.95)
Merging TDigests
arr1 = np.random.randn(1000)
arr2 = np.ones(1000)
digest1 = TDigest.from_array(arr=arr1)
digest2 = TDigest.from_array(arr=arr2)
merged_digest = digest1.merge(digest2, delta=100.0) # delta again defaults to 300.0
Serialising TDigests
The TDigest
object can be converted to a dictionary and JSON-serialised and is also pickleable.
# Convert and load to/from a python dict
d = tdigest.to_dict()
loaded_digest = TDigest.from_dict(d)
# Pickle a digest
import pickle
pickle.dumps(tdigest)
Development workflow
pip install hatch
cd bindings/python
# Run linters
hatch run dev:lint
# Run tests
hatch run dev:test
# Run benchmark
hatch run dev:benchmark
# Format code
hatch run dev:format
Contributing
Please read our contributing guide and code of conduct if you'd like to contribute to the project.
Community Guidelines
Please read our code of conduct before participating in or contributing to this project.
Security
Please see our security policy for details on reporting security vulnerabilities.
License
TDigest-rs is licensed under the Apache Software License 2.0 (Apache-2.0)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-win_amd64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-win_amd64.whl
- Upload date:
- Size: 175.5 kB
- Tags: CPython 3.7+, Windows x86-64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7c47fec4aa8f479318e13f25ecfa8f89f2c20cb3700697d87fcc4633ff704694 |
|
MD5 | fbdabe1497dcd9b827af497215c145e1 |
|
BLAKE2b-256 | abe3b889f648baebb9ee7d1129709a8c70059ec3a47a2a80ac5a404c4f1b4e45 |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-musllinux_1_2_x86_64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-musllinux_1_2_x86_64.whl
- Upload date:
- Size: 1.2 MB
- Tags: CPython 3.7+, musllinux: musl 1.2+ x86-64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | aee1c3f1fa2b6969494b7b11d41b3c6b94240748e541be88746d87eff54e8256 |
|
MD5 | f78c78ab28bf0f321ee3b4ec3ef9bfec |
|
BLAKE2b-256 | 66f006d468b1d518de252d10824c52c16c7cce7621db80323a2624b5338715fa |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-musllinux_1_2_aarch64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-musllinux_1_2_aarch64.whl
- Upload date:
- Size: 1.2 MB
- Tags: CPython 3.7+, musllinux: musl 1.2+ ARM64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3fff29326b67cec8e35130e1d8dbab104eea677492ca3e954e9a0676e21db214 |
|
MD5 | 9ba2d733db73fa0418702b7571586b86 |
|
BLAKE2b-256 | d2b8751dc806f20388c0ff28de7283b3a810d6d606269c47864b9d244d8079a8 |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
- Upload date:
- Size: 1.1 MB
- Tags: CPython 3.7+, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 62ee8d5a01226d020a2ab11a956292c38beab3f34d2d9fa7a8fca22c3e5d4bbf |
|
MD5 | 14643512935bfffa8ec5288dac9108f3 |
|
BLAKE2b-256 | 893bfaabc31e51eeae07efe1ad55d1e92daa495a6dddea99371a6896b644e93a |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
- Upload date:
- Size: 1.1 MB
- Tags: CPython 3.7+, manylinux: glibc 2.17+ ARM64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b4174b74aecc1d791e4f0007bcc043c75d7ec46f43bf8fbc6f4a4340e46a8028 |
|
MD5 | 0a4952e9ba3cf4fc4877a40a6a568c75 |
|
BLAKE2b-256 | 0f7c5e48f9b60eca4f2888b4826f365d6af31ca9846f5e7875ec05ca1051bc5e |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-macosx_11_0_arm64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-macosx_11_0_arm64.whl
- Upload date:
- Size: 287.3 kB
- Tags: CPython 3.7+, macOS 11.0+ ARM64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c4ca4f538c3fa204da2580f21f4258f53a16914e7877d6a07d81d809c74529a9 |
|
MD5 | 6dac8e90be2f6e55f7c8d34242b92066 |
|
BLAKE2b-256 | 788b3275dabf1d16409eaa2dc98ef4c9e8eda00a7df2e8e3adc8f612815d2ffb |
File details
Details for the file tdigest_rs-0.1.0-cp37-abi3-macosx_10_12_x86_64.whl
.
File metadata
- Download URL: tdigest_rs-0.1.0-cp37-abi3-macosx_10_12_x86_64.whl
- Upload date:
- Size: 291.9 kB
- Tags: CPython 3.7+, macOS 10.12+ x86-64
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 417d452ac5f86a8eb37c1671b99780b7186830588f17d675bca5f254880c5ed0 |
|
MD5 | d6f60db7c9dcbc62e1181cb17342eeeb |
|
BLAKE2b-256 | b3e212b15d07cf431673f52356a46d1279839cfccf10d897f99b5ac1cdfd9d07 |