# pydata/numexpr

Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more

Repository: https://github.com/pydata/numexpr
Canonical: https://ross.abutalabs.com/products/numexpr
Homepage: https://numexpr.readthedocs.io/en/latest/user_guide.html
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-17T20:00:10+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 93, longevity 100
- inputs: {"age_days": 4659, "days_push": 16, "days_rel": 46, "gap_med": 12.5, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2535, forks 226 (observed 2026-08-28T04:06:59.141034+00:00)

## What it is
NumExpr is a fast numerical expression evaluator for NumPy arrays that compiles expressions like '3*a+4*b' into op-codes executed by an integrated virtual machine. It avoids intermediate memory allocations and uses multi-threading (optionally Intel MKL/VML) to speed up large array computations.

## Use cases
- evaluate arithmetic expressions on large numpy arrays faster
- reduce memory usage in pandas query computations
- speed up element-wise math on big arrays with multiple cores
- accelerate numerical expressions in PyTables queries
- compute complex array formulas without temporary allocations

## When to choose
- you work with large NumPy/Pandas arrays and expression evaluation is a bottleneck
- you want multi-core speedups without rewriting vectorized code
- you need lower memory overhead than NumPy's intermediate arrays

## When to avoid
- your arrays are small, where NumPy is equally fast or faster
- you need general-purpose symbolic math or arbitrary Python code evaluation
- your expressions are trivial like 'a + 1' with little expected speedup

## Facets
- artifact type: library
- maturity: stable
- function: math, machine-learning, data-science
- domain: data-science, performance
- platform: python, cross-platform
- tags: numpy, pandas, numerical-computing, expression-evaluator, multithreading, array-computing, algorithms

## Member repositories
- pydata/numexpr (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:59.141034+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:25:14.573306+00:00, confidence not recorded.
  - readme: https://github.com/pydata/numexpr (fetched 2026-08-28T04:06:59.141034+00:00, sha 14f9a9c6c3f8)
- Data as of 2026-08-30T08:39:29.467469+00:00.