Numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas
NumExpr is a fast numerical expression evaluator designed for NumPy that enhances performance and reduces memory usage. It achieves this by avoiding memory allocation for intermediate results and utilizing multi-threading capabilities. Users can install NumExpr via pip or conda, with additional support for Intel's MKL for improved performance on certain operations.
- ▪NumExpr accelerates array operations and uses less memory compared to standard Python calculations.
- ▪The performance improvements can range from 0.95x to 15x depending on the complexity of the expressions.
- ▪NumExpr is available for installation via pip and conda, with specific instructions for different operating systems.
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| Original publisher | GitHub |
| Canonical URL | https://github.com/pydata/numexpr |
| Publication time | Tue, 19 May 2026 08:20:13 +0000 |
| Retrieval time | 2026-05-19T08:34:57.460Z |
| Last seen | 2026-05-19T08:34:57.460Z |
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| Summary source text | contentText |
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| Publisher visit | Yes — open original |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
NumExpr: Fast numerical expression evaluator for NumPy Author: David M. Cooke, Francesc Alted, and others. Maintainer:Francesc Alted Contact: [email protected] URL:https://github.com/pydata/numexpr Documentation:http://numexpr.readthedocs.io/en/latest/ GitHub Actions: PyPi: DOI: readthedocs: What is NumExpr? NumExpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like '3*a+4*b') are accelerated and use less memory than doing the same calculation in Python. In addition, its multi-threaded capabilities can make use of all your cores -- which generally results in substantial performance scaling compared to NumPy.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.