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T-Digest data structure

Project description

# tdigest
### Efficient percentile estimation of streaming or distributed data

This is a Python implementation of Ted Dunning's [t-digest]( data structure. The t-digest data structure is designed around computing accurate estimates from either streaming data, or distributed data. These estimates are percentiles, quantiles, trimmed means, etc. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10kB (instead of storing the entire list of data).

See a blog post about it here: [Percentile and Quantile Estimation of Big Data: The t-Digest](

### Usage

from tdigest import TDigest
from numpy.random import random

T1 = TDigest()
for _ in range(5000):

print T1.percentile(0.15) # about 0.15

T2 = TDigest()
print T2.percentile(0.15)

T = T1 + T2
T.percentile(0.3) # about 0.3

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