t-digest is a data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means, designed by Ted Dunning.
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.
This package provides tested, performant, thread-safe Python 3 CFFI bindings to an adapted implementation of t-digest by Usman Masood originally written for redis-tdigest.
You can install this package using pip or the included setup.py script:
# Using pip
pip install tdigest-cffi
# Using setup.py
python setup.py install
from tdigest import TDigest, RawTDigest
# Thread-safe instance with default compression factor
digest = TDigest()
# Raw instance with default compression factor
digest = RawTDigest()
# Thread-safe instance with a custom compression factor
digest = TDigest(compression=500)
# Digest compression
compression = digest.compression
# Digest weight
weight = digest.weight
# Centroid count
centroid_count = digest.centroid_count
# Compression count
compression_count = digest.compression_count
# Insertion with unit weight
digest.insert(1000)
# Insertion with custom weight
digest.insert(1000, 2)
# 99th percentile calculation
quantile = digest.quantile(0.99)
percentile = digest.percentile(99)
# Cumulative distribution function
cdf = digest.cdf(1000) # P(X <= 1000)
# Centroid extraction
for centroid in digest.centroids():
print(centroid.mean, centroid.weight)
# Digest merging
other = TDigest()
other.insert(42)
digest.merge(other)BSD 3-Clause License
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