{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Computes distance measures between time series using Dynamic Time Warping (DTW) and related algorithms, with both pure Python and optimized C implementations.","skillfed_tags":["time-series-analysis","distance-metrics","clustering"],"use_cases":["Measure similarity between sensor time series or financial price sequences to detect anomalies or patterns.","Build distance matrices for time series clustering using hierarchical or other clustering methods.","Align and compare sequences of different lengths in speech recognition, gesture analysis, or bioinformatics.","Accelerate large-scale time series comparisons using the fast C backend with optional pruning.","Compute warping paths and visualize alignment between two time series for exploratory analysis."],"what_it_does":"dtaidistance is a library for computing distance measures between time series, centered on Dynamic Time Warping (DTW) and related algorithms. It offers a pure Python implementation and a fast C implementation (with Cython as the build-time dependency). The library is designed to work seamlessly with NumPy and Pandas while avoiding unnecessary data copies.\n\nThe package supports a range of DTW variants and optimizations: windowed alignment, pruning strategies, multi-dimensional sequences, barycenter averaging for clustering, and subsequence search. It can compute distances between pairs of series, full distance matrices across sets of series, or partial blocks for distributed computation. The C implementation includes parallelization via OpenMP when available.","worth_installing":"Yes. dtaidistance is actively maintained, has no known vulnerabilities, and offers a well-tested implementation of DTW with both accessibility (pure Python) and performance (C with OpenMP). The Apache-2.0 license is permissive. Install friction is moderate due to C compilation, but pre-built wheels for Python 3.11\u20133.14 on major platforms mitigate this. Suitable for time series analysis, clustering, and similarity tasks."},"id":"dtaidistance","links":{"html":"https://skillfed.io/packages/dtaidistance","md":"https://skillfed.io/packages/dtaidistance.md","pypi":"https://pypi.org/project/dtaidistance/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-12","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dtaidistance","python_support":"supports_current","summary":"Distance measures for time series (Dynamic Time Warping, fast C implementation)"},"popularity":{"monthly_downloads":138728,"position":11319,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.4.0"}