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:
equalnot_equalgreatergreater_equallessless_equal
Occurrence and search:
countstartswithendswithfindrfindindexrindex
Information and predicates:
str_lenisalphaisalnumisdigitisdecimalisnumericisspaceislowerisupperistitle
Additional np.char operation:
compare_chararrays
Supported APIs And Dtypes
np.charfixed-width byte strings:Snp.charfixed-width Unicode strings:Unp.stringsfixed-width byte strings:Snp.stringsfixed-width Unicode strings:Unp.stringsvariable-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,<1.27or>=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
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| charex_numba-0.5.2.tar.gz | 54.5 kB | Details |
Built distributions (wheels)
Total release size: 1.6 MB
Release files / charex_numba-0.5.2.tar.gz
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