rolling-quantiles
node-faststats
| rolling-quantiles | node-faststats | |
|---|---|---|
| 2 | 1 | |
| 133 | 191 | |
| - | - | |
| 2.0 | 0.0 | |
| 12 months ago | over 3 years ago | |
| C | JavaScript | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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rolling-quantiles
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How percentile approximation works (and why it's more useful than averages)
Not sure about the parent post, but I also wrote a Python package inspired by a past interview question! It uses a pair of heaps to keep track of items in a time-series window and find exact quantile estimates.
https://github.com/marmarelis/rolling-quantiles
- Show HN: Fast Rolling Quantiles for Python
node-faststats
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How percentile approximation works (and why it's more useful than averages)
This is exactly the algorithm we developed at LogNormal (now part of Akamai) 10 years ago for doing fast, low-memory percentiles on large datasets.
It's implemented in this Node library: https://github.com/bluesmoon/node-faststats
Side note: I wish everyone would stop using the term Average to refer to the Arithmetic mean. "Average" just means some statistic used to summarize a dataset. It could be the Arithmetic Mean, Median, Mode(s), Geometric Mean, Harmonic Mean, or any of a bunch of other statistics. We're stuck with AVG because that's the function used by early databases and Lotus 123.
What are some alternatives?
t-digest - A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means
timescale-analytics - Extension for more hyperfunctions, fully compatible with TimescaleDB and PostgreSQL 📈
LiveStats - Online Statistical Algorithms for Python
Folly - An open-source C++ library developed and used at Facebook.