Fairspec Python

Updated May 16, 2026

This guide will help you get started with Fairspec Python. If you are new to the core framework's technologies, please take a look at the Fairspec standard and Polars DataFrames documentation.

Runtimes

Fairspec Python requires:

Python 3.12+

Installation

The framework can be installed as one package:

pip install fairspec

You can cherry-pick from individual packages:

pip install fairspec-metadata fairspec-table

Fairspec Python is built with type safety in mind. It uses Python type hints to provide type definitions for all packages and to enforce type safety throughout the framework. It's highly recommended to use a type-aware editor such as VS Code with Pylance or PyCharm to work with the project.

Examples

Loading a Dataset from Zenodo merging system Zenodo metadata into a user dataset and validating its metadata:

from fairspec import load_dataset

dataset = load_dataset("https://zenodo.org/records/10053903")

print(dataset)
# {
#   "id": "https://doi.org/10.5281/zenodo.10053903",
#   ...
# }

Validating an in-memory dataset descriptor:

from fairspec import validate_dataset

report = validate_dataset({"resources": "bad"})

print(report.valid)
# False
print(report.errors)
# [
#   {
#     "type": "metadata",
#     "message": "must have type array",
#     "jsonPointer": "/resources",
#   }
# ]

Loading a dataset from a remote descriptor and saving it locally as a zip archive, and then using it as a local dataset:

from fairspec import (
    load_dataset,
    load_dataset_from_zip,
    save_dataset_to_zip,
    get_temp_file_path,
)

archive_path = get_temp_file_path()
source_dataset = load_dataset(
    "https://raw.githubusercontent.com/roll/currency-codes/refs/heads/master/datapackage.json",
)

save_dataset_to_zip(source_dataset, archive_path=archive_path)
target_dataset = load_dataset_from_zip(archive_path)
print(target_dataset)

Reading a CSV table:

from fairspec import load_table, Resource
from fairspec_metadata import CsvFileDialect

table = load_table(Resource(data="data.csv"))

# Load with custom format
table = load_table(Resource(
    data="data.csv",
    fileDialect=CsvFileDialect(
        delimiter=";",
        headerRows=[1],
    ),
))

Reference

Note that fairspec and fairspec-library packages re-export most of the functionality.