grizz
A light library to preprocess data with polars
Decision gist · record as of 2026-08-14
Yes, if you are already using polars and want a lightweight, composable approach to data transformation. The low install friction, active maintenance, and permissive license make it low-risk. However, be aware that the API is unstable in beta—pin your version and review release notes before upgrading, as breaking changes are expected.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel.
- Actively maintained with recent commits; however, the package is in beta stage and the fact sheet notes that API stability is not guaranteed between releases, so breaking changes are possible.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive and allows commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2024-11-08 (644 days) · last repo commit 2026-08-11
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,162 downloads/mo, #13,471 on PyPI
Alternatives
Verify before relying
pip install grizz
import polars as pl
from grizz.transformer import Cast
transformer = Cast(columns=["col1", "col3"], dtype=pl.Int32)
frame = pl.DataFrame({"col1": [1, 2, 3], "col3": ["1", "2", "3"]})
out = transformer.transform(frame)- Whether custom ingestors and transformers can be easily extended beyond the built-in set
- Performance characteristics when handling large DataFrames or complex transformation chains
- Availability and completeness of documentation for all transformer and ingestor types
What it is and what it does
grizz is a lightweight library for ingesting and transforming data within polars DataFrames. It uses an object-oriented design where ingestors load data and transformers reshape it, and these building blocks can be combined into pipelines. The library wraps common operations like column casting, filtering, and type conversion into reusable transformer objects that can be chained together.
The package depends on polars for DataFrame operations, plus coola, iden, and objectory for internal utilities. It supports Python 3.9 through 3.13 and is currently in beta—the documentation explicitly warns that the API is not stable and may change significantly before a 1.0 release. Optional dependencies like clickhouse-connect, pyarrow, and tqdm extend functionality for specific use cases.
Use it for
- Cast multiple DataFrame columns to a target type in a single composable step
- Build reusable data preprocessing pipelines by combining ingestors and transformers
- Extend the library with custom ingestors to load data from domain-specific sources
- Preprocess tabular data for machine learning workflows using polars as the backend
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using polars and want a lightweight, composable approach to data transformation.
The low install friction, active maintenance, and permissive license make it low-risk. However, be aware that the API is unstable in beta—pin your version and review release notes before upgrading, as breaking changes are expected.
Install
grizz on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained with recent commits; however, the package is in beta stage and the fact sheet notes that API stability is not guaranteed between releases, so breaking changes are possible.
License in practice
BSD-3-Clause is permissive and allows commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install grizz
import polars as pl
from grizz.transformer import Cast
transformer = Cast(columns=["col1", "col3"], dtype=pl.Int32)
frame = pl.DataFrame({"col1": [1, 2, 3], "col3": ["1", "2", "3"]})
out = transformer.transform(frame)Verify before relying
- Whether custom ingestors and transformers can be easily extended beyond the built-in set
- Performance characteristics when handling large DataFrames or complex transformation chains
- Availability and completeness of documentation for all transformer and ingestor types
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagescoolaidenobjectorypolars |
| Maintenance | Actively maintained 644 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,162 / month, #13,471 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: grizz-0.1.1-py3-none-any.whl
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See also polars-runtime-compat · polars · polars-runtime-64 · patito · polars-runtime-32 · dataframely · narwhals · polars-cloud · itables