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

Occurrence Information

Property Information

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.