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charex

Use NumPy string functions inside Numba-compiled code.

charex lets @njit functions call common NumPy string operations such as comparisons, find, startswith, endswith, str_len, and string predicates. It works with fixed-width NumPy string arrays and, on NumPy 2.x, variable-width StringDType arrays.

Installation

The PyPI distribution is named charex-numba; the import package is charex:

python -m pip install charex-numba

Quick Start

import charex
import numpy as np
from numba import njit


@njit
def count_long(values, min_length):
    # charex enables this NumPy string operation inside nopython mode.
    lengths = np.strings.str_len(values)
    return np.count_nonzero(lengths >= min_length)

On NumPy 1.x, use the same pattern with np.char and fixed-width S or U arrays.

Behavior

NumPy behavior is the contract. Supported operations aim to match NumPy's return values, output shapes, dtypes, exception behavior, broadcasting, and input immutability.

Supported Operations

Comparisons:

  • equal
  • not_equal
  • greater
  • greater_equal
  • less
  • less_equal

Occurrence and search:

  • count
  • startswith
  • endswith
  • find
  • rfind
  • index
  • rindex

Information and predicates:

  • str_len
  • isalpha
  • isalnum
  • isdigit
  • isdecimal
  • isnumeric
  • isspace
  • islower
  • isupper
  • istitle

Additional np.char operation:

  • compare_chararrays

Supported APIs And Dtypes

  • np.char fixed-width byte strings: S
  • np.char fixed-width Unicode strings: U
  • np.strings fixed-width byte strings: S
  • np.strings fixed-width Unicode strings: U
  • np.strings variable-width Unicode strings: StringDType

NumPy stores S and U arrays in fixed-size records. StringDType is variable-width and stores string payloads separately from the array's fixed-size metadata records. charex supports both storage models.

The native charex._stringdtype extension is required for StringDType support and is built by the package install.

Shapes

Supported inputs include scalars, 0-D arrays, 1-D arrays, N-D arrays, and broadcast-compatible shapes for both fixed-width S/U and variable-width StringDType. Array inputs may be contiguous, read-only, positively strided, negatively strided, zero-stride, or empty views.

StringDType() and StringDType(na_object=...) variants are supported for the listed np.strings operations with NumPy-matching operation-specific null behavior.

np.char and np.strings are not treated as aliases. For example, np.char comparison semantics strip trailing whitespace/NULs, while np.strings comparison semantics do not.

Byte inputs to Unicode-only predicates such as isdecimal and isnumeric follow NumPy and raise unsupported-loop errors.

Not Supported

charex does not yet implement transformation or output-producing string operations such as replace, case conversion, strip, pad, join, split, encode, or decode. Object arrays and object-scalar bridges are also outside the current nopython string path.

Performance

On the current Numba 0.65.1 matrix, charex ranges from 1.02x to 6.51x NumPy speed across 135 fixed-width and StringDType cases, with a 1.60x median.

Benchmark artifacts are in docs/benchmarks/numba-v-0.65.1.

Comparison Operators

comparison-operators-bytes.png comparison-operators-strings.png stringdtype-comparison.png

Occurrence Information

char-occurrence-bytes.png char-occurrence-strings.png stringdtype-occurrence.png

Property Information

char-properties-bytes.png char-properties-strings.png char-numerics-strings.png stringdtype-properties.png stringdtype-numerics.png

The previous Numba 0.59 matrix is archived under benchmarks/numba-v-0.59.

Compatibility

charex targets Numba 0.65.1 and the NumPy ranges tested by that Numba release:

  • Python >=3.10,<3.15
  • Numba >=0.65.1,<0.66
  • NumPy >=1.22,<1.27 or >=2.0,<2.5
  • llvmlite 0.47.x

np.strings is available on NumPy 2.x only. On NumPy 1.x, charex registers the np.char overloads and skips np.strings.

Development

Install test dependencies:

python -m pip install -e ".[test]"

Run tests:

pytest -q

Run the representative behavior audit:

python docs/exploration/string_array_shape_audit.py --methods representative --api all --dtype all

Run the benchmark smoke test:

python benchmarks/benchmark.py --size 50000 --repeat 5

Install benchmark plotting dependencies and write CSV/PNG output:

python -m pip install -e ".[bench]"
python benchmarks/benchmark.py --size 50000 --repeat 5 --plot

Regenerate the full benchmark matrix from the repository root:

python -m pip install -e ".[bench]"
# Use a fresh NUMBA_CACHE_DIR for release matrices
CACHE_DIR=$(mktemp -d /tmp/charex-numba-cache.XXXXXX)
NUMBA_CACHE_DIR="$CACHE_DIR" PYTHONPATH=. \
  python benchmarks/matrix.py --size 250000 --repeat 15

CI runs Python 3.10-3.14 across representative NumPy 1.x and 2.x jobs with Numba 0.65.1. The benchmark matrix above was generated on Python 3.12.8, NumPy 2.4.6, Numba 0.65.1, and llvmlite 0.47.0.

Metadata

Release files for charex-numba 0.5.2

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charex_numba-0.5.2-cp314-cp314-win_amd64.whl CPython 3.14 CPython 3.14 Windows x86-64 Details
charex_numba-0.5.2-cp314-cp314-win32.whl CPython 3.14 CPython 3.14 Windows x86-32 Details
charex_numba-0.5.2-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.14 CPython 3.14 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
charex_numba-0.5.2-cp314-cp314-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
charex_numba-0.5.2-cp314-cp314-macosx_10_15_x86_64.whl CPython 3.14 CPython 3.14 macOS 10.15+ x86-64 Details
charex_numba-0.5.2-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
charex_numba-0.5.2-cp313-cp313-win32.whl CPython 3.13 CPython 3.13 Windows x86-32 Details
charex_numba-0.5.2-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
charex_numba-0.5.2-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
charex_numba-0.5.2-cp313-cp313-macosx_10_13_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.13+ x86-64 Details
charex_numba-0.5.2-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
charex_numba-0.5.2-cp312-cp312-win32.whl CPython 3.12 CPython 3.12 Windows x86-32 Details
charex_numba-0.5.2-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details
charex_numba-0.5.2-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
charex_numba-0.5.2-cp312-cp312-macosx_10_13_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.13+ x86-64 Details
charex_numba-0.5.2-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
charex_numba-0.5.2-cp311-cp311-win32.whl CPython 3.11 CPython 3.11 Windows x86-32 Details
charex_numba-0.5.2-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64, Linux glibc 2.5+ x86-64 Details
charex_numba-0.5.2-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
charex_numba-0.5.2-cp311-cp311-macosx_10_9_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.9+ x86-64 Details
charex_numba-0.5.2-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
charex_numba-0.5.2-cp310-cp310-win32.whl CPython 3.10 CPython 3.10 Windows x86-32 Details
charex_numba-0.5.2-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.5+ x86-64 Details
charex_numba-0.5.2-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
charex_numba-0.5.2-cp310-cp310-macosx_10_9_x86_64.whl CPython 3.10 CPython 3.10 macOS 10.9+ x86-64 Details

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