{"library":"quantile-python","title":"quantile-python","description":"Python Implementation of Graham Cormode and S. Muthukrishnan's Effective Computation of Biased Quantiles over Data Streams in ICDE'05. Version 1.1 is the latest stable release. The library has a low release cadence with no recent updates; it provides an efficient algorithm for computing biased quantiles over data streams.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install quantile-python"],"cli":null},"imports":["from quantile_python import QuantileEstimator","from quantile_python import StreamSummary"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from quantile_python import QuantileEstimator\n\n# Create an estimator with a desired error epsilon (epsilon = 0.001 means about 0.1% error)\nestimator = QuantileEstimator(epsilon=0.001)\nfor value in range(1000):\n    estimator.insert(value)\n\n# Get quantiles: 0.5 (median), 0.9, 0.99\nprint(\"Median:\", estimator.query(0.5))\nprint(\"90th percentile:\", estimator.query(0.9))\nprint(\"99th percentile:\", estimator.query(0.99))","lang":"python","description":"Demonstrates creating a QuantileEstimator, inserting values, and querying quantiles.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}