liac-arff
A module for read and write ARFF files in Python.
Decision gist · record as of 2026-08-14
Yes, but with caution. The package is stable and dependency-free for reading/writing ARFF files, and it has no known vulnerabilities. However, its abandoned status since 2020 makes it risky for new projects—install only if you need ARFF support for legacy datasets or existing workflows, and consider monitoring for compatibility issues as Python evolves. For new machine learning projects, evaluate whether a maintained alternative or direct pandas/numpy workflows would be more sustainable.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- High install friction: the package has been abandoned since 2020 (2174 days since last release) with no active maintenance.
- While it has no runtime dependencies, the lack of updates and maintenance attention poses a risk for long-term compatibility with newer Python versions or dependency ecosystems.
License · maintenance · safety
MIT License (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.
last release 2020-08-31 (2174 days) · last repo commit 2023-05-31 · 101 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 80,895 downloads/mo, #14,264 on PyPI
Alternatives
Verify before relying
import arff
# Read an ARFF file
data = arff.load(open('dataset.arff', 'rb'))
# Write an ARFF file
with open('output.arff', 'w') as f:
f.write(arff.dumps(data))- Whether the package remains compatible with Python versions beyond 3.8 given its abandoned maintenance status.
- Whether scipy.sparse integration (mentioned in description) is fully functional or requires additional setup.
- Current state of the repository and whether any community forks maintain active development.
What it is and what it does
liac-arff is a lightweight parser for ARFF files, the standard dataset format used by Weka and other machine learning tools. It provides a json-like interface to read ARFF files into Python dictionaries and lists, and to serialize Python data structures back to ARFF format. The module handles NUMERIC, REAL, INTEGER, STRING, and NOMINAL attribute types, supports missing values and unicode, and can work with scipy sparse matrices and SVMLight-style dictionary lists.
The package has no external runtime dependencies and works across Python 2.7 and Python 3.5+. However, it has been abandoned since 2020 with no recent maintenance, which means it may face compatibility issues with future Python releases or ecosystem changes. It remains in production-stable status and is still downloaded regularly, but users should be aware of the maintenance gap when considering it for new projects.
Use it for
- Loading ARFF datasets from Weka experiments into Python for analysis or preprocessing.
- Converting machine learning datasets between ARFF and Python native structures for interoperability.
- Serializing experimental data to ARFF format for use in Weka or other ML tools.
- Batch processing of ARFF files in data pipelines that do not require active maintenance.
- Reading historical datasets stored in ARFF format in academic or research archives.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, but with caution.
The package is stable and dependency-free for reading/writing ARFF files, and it has no known vulnerabilities. However, its abandoned status since 2020 makes it risky for new projects—install only if you need ARFF support for legacy datasets or existing workflows, and consider monitoring for compatibility issues as Python evolves. For new machine learning projects, evaluate whether a maintained alternative or direct pandas/numpy workflows would be more sustainable.
Install
liac-arff on PyPI
Before you install
High install friction: the package has been abandoned since 2020 (2174 days since last release) with no active maintenance. While it has no runtime dependencies, the lack of updates and maintenance attention poses a risk for long-term compatibility with newer Python versions or dependency ecosystems.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.
Quickstart
import arff
# Read an ARFF file
data = arff.load(open('dataset.arff', 'rb'))
# Write an ARFF file
with open('output.arff', 'w') as f:
f.write(arff.dumps(data))Verify before relying
- Whether the package remains compatible with Python versions beyond 3.8 given its abandoned maintenance status.
- Whether scipy.sparse integration (mentioned in description) is fully functional or requires additional setup.
- Current state of the repository and whether any community forks maintain active development.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.* |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,174 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 80,895 / month, #14,264 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: MacOS XEnvironment :: Win32 (MS Windows)Environment :: X11 ApplicationsIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Python Modules |
Evidence: liac-arff-2.5.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “arff file parser”
- liac-arffReads and writes ARFF (Attribute-Relation File Format) files, a…
- INIToolsINITools parses and manipulates INI-style configuration files through…
- iniparseiniparse is an INI file parser that preserves formatting (comments,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also pdbufr · cfgrib · open-radar-data · argilla · ucimlrepo · pytorch-msssim · fiftyone-brain · hana-ml · daft · rfdetr