{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Implements Ted Dunning's t-digest data structure for efficient percentile and quantile estimation from streaming or distributed data, with support for merging digests and compact serialization.","skillfed_tags":["streaming-statistics","distributed-computing"],"use_cases":["Computing percentiles on large streaming datasets without storing all raw values in memory.","Aggregating statistics across distributed systems by merging multiple t-digests from different nodes.","Estimating quantiles and trimmed means in map-reduce pipelines where data is too large to centralize.","Serializing statistical summaries of datasets for transmission or storage with minimal overhead.","Calculating medians and percentile ranges for real-time monitoring or analytics applications."],"what_it_does":"tdigest is a Python implementation of Ted Dunning's t-digest algorithm, a probabilistic data structure designed to compute accurate percentiles, quantiles, and trimmed means from streaming or distributed datasets. Rather than storing all raw data points, it maintains a compact summary using centroids, allowing it to serialize to under 10kB and merge results from multiple data sources\u2014making it particularly useful in map-reduce and distributed computing contexts.\n\nThe package provides methods to update the digest sequentially or in batches, query percentiles and cumulative distribution functions, compress the internal structure to reduce memory, and serialize/deserialize to and from Python dictionaries for storage or transmission. It depends on accumulation-tree and pyudorandom for its core operations.","worth_installing":"Yes, if you need percentile estimation on streaming or distributed data and can tolerate an abandoned package. The algorithm is well-established and the implementation is stable; no known vulnerabilities exist. However, expect no bug fixes or updates\u2014verify that accumulation-tree and pyudorandom remain compatible with your environment, and test accuracy for your specific use case before relying on it in production."},"id":"tdigest","links":{"html":"https://skillfed.io/packages/tdigest","md":"https://skillfed.io/packages/tdigest.md","pypi":"https://pypi.org/project/tdigest/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2019-05-07","license_spdx":null,"license_treatment":"permissive","name":"tdigest","python_support":"unspecified","summary":"T-Digest data structure"},"popularity":{"monthly_downloads":336793,"position":7456,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.2.2"}