T-Digest data structure

## Project description

# tdigest
### Efficient percentile estimation of streaming or distributed data
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This is a Python implementation of Ted Dunning's [t-digest](https://github.com/tdunning/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](http://dataorigami.net/blogs/napkin-folding/19055451-percentile-and-quantile-estimation-of-big-data-the-t-digest)

### Installation
*tdigest* is compatible with both Python 2 and Python 3.


pip install tdigest


### Usage

#### Update the digest sequentially


from tdigest import TDigest
from numpy.random import random

digest = TDigest()
for x in range(5000):
digest.update(random())

print(digest.percentile(15)) # about 0.15, as 0.15 is the 15th percentile of the Uniform(0,1) distribution


#### Update the digest in batches


another_digest = TDigest()
another_digest.batch_update(random(5000))
print(another_digest.percentile(15))


#### Sum two digests to create a new digest


sum_digest = digest + another_digest


### API

TDigest.

- update(x, w=1): update the tdigest with value x and weight w.
- batch_update(x, w=1): update the tdigest with values in array x and weight w.
- compress(): perform a compression on the underlying data structure that will shrink the memory footprint of it, without hurting accuracy. Good to perform after adding many values.
- percentile(p): return the pth percentile. Example: p=50 is the median.
- quantile(q): return the CDF the value q is at.
- trimmed_mean(p1, p2): return the mean of data set without the values below and above the p1 and p2 percentile respectively.

## Project details

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