cytoolz

library1.1.0pypypi✓ verified 30d ago

cytoolz provides a high-performance, Cython-implemented version of the functional utilities found in the toolz library. It offers drop-in replacements for many common `toolz` functions, primarily focusing on iteration and composition for speed improvements. The current version is 1.1.0. Release cadence generally follows significant updates or releases of the upstream `toolz` library.

data serialization

pip install cytoolz

github ↗package ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · CYTOOLZ

cytoolz

datapythonv1.1.0

Install

1.9s avg

Import

63ms

Disk

28MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.1.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.066s · 28.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 1.9s · import 0.060s · 31MB

28MB installed

● package 28MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

compose

✓ from cytoolz.functoolz import compose

pipe

✓ from cytoolz.functoolz import pipe

map

✓ from cytoolz.itertoolz import map

groupby

✓ from cytoolz.itertoolz import groupby

get_in

✓ from cytoolz.dicttoolz import get_in

✗ import cytoolz.get_in

Functions are typically exposed via submodules like `functoolz`, `itertoolz`, `dicttoolz`.

This quickstart demonstrates basic usage of `compose`, `pipe`, `map`, and `filter` from `cytoolz.functoolz` and `cytoolz.itertoolz`. These functions provide a functional programming style with potential performance benefits over their pure Python equivalents, especially for iterable operations.

from cytoolz.functoolz import compose, pipe from cytoolz.itertoolz import map, filter def add_one(x): return x + 1 def multiply_by_two(x): return x * 2 def is_even(x): return x % 2 == 0 # Example 1: Compose functions process = compose(multiply_by_two, add_one) result_compose = process(5) # (5 + 1) * 2 = 12 print(f"Compose result: {result_compose}") # Example 2: Pipe data through functions result_pipe = pipe(5, add_one, multiply_by_two) print(f"Pipe result: {result_pipe}") # Example 3: Map and filter with cytoolz iterators numbers = [1, 2, 3, 4, 5, 6] processed_numbers = list(filter(is_even, map(add_one, numbers))) print(f"Processed numbers (map/filter): {processed_numbers}") # [4, 6, 8]

Debug

Known issues

gotchaPerformance benefits are not universal. While `cytoolz` offers significant speedups for many iterative and compositional operations, particularly those involving loops, simple function calls or operations already highly optimized in CPython might not see major gains, and could even incur minor overhead due to C-Python bridge calls. Measure performance in your specific use case.

fix

Profile your application to determine if `cytoolz` provides a meaningful performance improvement for your specific bottlenecks. Don't assume all functions will be faster.

affects: All

gotcha`cytoolz`'s API mirrors `toolz`. `cytoolz` aims to be a drop-in replacement for `toolz` primitives. Therefore, users should consult the `toolz` documentation for specific API details (function signatures, available utilities). Changes in `toolz`'s API (e.g., removals, argument changes) will generally apply to `cytoolz` as well.

fix

Refer to the official `toolz` documentation for the primary source of API information. Monitor `toolz` release notes for breaking changes when upgrading.

affects: All

gotcha`toolz` is a required runtime dependency. Despite `cytoolz` providing Cythonized versions of many `toolz` functions, the `toolz` library itself is an explicit and necessary dependency. Ensure both `cytoolz` and `toolz` are installed in your environment.

fix

Always ensure `pip install cytoolz` is sufficient, as it should pull in `toolz` automatically. If issues arise, explicitly install `toolz` (`pip install toolz`).

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'cytoolz'

This error occurs when the `cytoolz` package is not installed in the Python environment or the environment where the code is being run is not the one where `cytoolz` was installed.

fix

Ensure `cytoolz` is installed by running: `pip install cytoolz`

ERROR: Command errored out with exit status 1: command: ... Failed building wheel for cytoolz

This installation error typically indicates that your system lacks the necessary C compiler or Python development headers to compile the Cython components of `cytoolz` during installation. It can also occur with specific Python versions (e.g., Python 3.10 initially had issues) or when `Cython` itself is not pre-installed.

fix

First, ensure you have a C compiler (like build-essential on Linux, Xcode command line tools on macOS, or Visual C++ build tools on Windows). Then, try installing `Cython` separately before `cytoolz`: `pip install cython` followed by `pip install cytoolz`.

ModuleNotFoundError: No module named 'cytoolz.itertoolz'

This error often arises in deployment scenarios (e.g., PyInstaller, Zappa, AWS Lambda) or when `cytoolz` is bundled in a zip file, because the Python interpreter cannot properly locate and load the Cython-compiled submodules like `itertoolz` or `functoolz`.

fix

When using tools like PyInstaller, you might need to add hidden imports (e.g., `--hidden-import=cytoolz.itertoolz`). For deployment to environments like AWS Lambda or when using zip files, ensure that the compiled `.so` or `.pyd` files for the Cython modules are correctly packaged and accessible in the Python path, often by building the deployment package in a compatible environment (e.g., a Docker container simulating the target Lambda environment).

'cytoolz.functoolz.Compose' object has no attribute '__module__'

This `AttributeError` can occur when code expects the `Compose` object (returned by `cytoolz.functoolz.compose`) to have a `__module__` attribute, which might be present in the pure Python `toolz` equivalent but not consistently exposed by the Cython implementation.

fix

If this error originates from a third-party library, check if there's an updated version that's compatible with `cytoolz`. If you have control over the code, you might need to adapt it not to rely on the `__module__` attribute of `Compose` objects from `cytoolz` or implement a workaround to provide this attribute if absolutely necessary.

Upgrade

Version history

1.1.0latest on PyPI · released Oct 19, 2025

Audit

Dependencies

toolzrequiredcytoolz is a Cythonized implementation of toolz primitives and explicitly lists toolz as a runtime dependency. Some utilities are still provided by the toolz library.

Agent activity

20 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/pytoolz/cytoolz ↗

packagehttps://pypi.org/project/cytoolz/ ↗

docshttps://toolz.readthedocs.io/en/latest/ ↗

changeloghttps://github.com/pytoolz/cytoolz/releases ↗