A data structure for accurate on-line accumulation of rank-based statistics.
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.
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.push(1000) # Insertion with custom weight digest.push(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.push(42) digest.merge(other)
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