dsjframe 0.9.1

Accurately reading, writing, and validating Dataset-JSON (.json, .ndjson, and .dsjc) with Apache Arrow.

dsjframe

dsjframe is a Python library for accurately reading, writing, and validating Dataset-JSON with Apache Arrow. It supports plain JSON, NDJSON, and DSJC (.json, .ndjson, and .dsjc).

Installation

Install the base package:

pip install dsjframe

Install the optional dependencies to support additional file formats:

pip install dsjframe[file-support]

Python 3.10 or newer is required.

Quick Start

import dsjframe
# Read
table = dsjframe.read_dataset("adsl.json")

# Write
metadata = {
    "datasetJSONVersion": "1.1.0",
    "label": "Subject Level Analysis Dataset",
}

dsjframe.write_dataset(table, "adsl.ndjson", metadata)

# validate
report = dsjframe.validate_dataset("adsl.dsjc")

See example.ipynb for a longer walkthrough.

Metadata for Writing

Dataset-JSON allows extensions, but dsjframe targets the standard structure by default. Unexpected metadata fields are treated as errors.

When writing, metadata is merged in this priority order:

1. Explicit metadata
2. define.xml
3. Embedded Arrow schema metadata
4. readstat_meta
5. Library defaults such as datasetJSONVersion and itemGroupOID

datasetJSONCreationDateTime is filled automatically. In many cases, you can omit most or all of metadata if define.xml, embedded schema metadata, or readstat_meta already provide the required fields. When define.xml is used, metaDataRef is set to define.xml automatically. If you need a different path or reference value, set it explicitly in metadata.

When writing from a pyarrow.Table or pandas DataFrame, column dataType is derived from the actual column type in the frame except for text-backed temporal columns declared as date, time, or datetime. Provided column metadata is still used for fields such as label, length, displayFormat, and keySequence, but it does not override the real data type. Compatible targetDataType values are preserved where allowed, decimal exports are normalized to "targetDataType": "decimal", and targetDataType: "integer" is ignored for text-backed temporal columns so partial dates and other ISO 8601 text values remain text-backed.

Missing Values

dsjframe follows Arrow conventions and represents missing values as null. In practice, especially when Dataset-JSON is used as an XPT replacement, character missing values are often written as "" instead of null. Because Arrow is better aligned with nulls for missing data, empty strings in string-like columns are converted to null by default when reading. If you need to preserve the distinction between null and an empty string, you can disable that conversion with an option.

More Than Metadata Validation

Because Dataset-JSON is a text format, its readability and editability are often treated as advantages. Those same properties can also make type drift, malformed values, or accidental edits harder to catch.

To address that, dsjframe includes strict validation for both metadata and row data.

In addition to JSON Schema validation for metadata, dsjframe also checks combinations of dataType and targetDataType, row structure, value conversion, record counts, and consistency between file content and file extension. String-backed temporal values are also checked as ISO 8601 text. Reduced-precision partial dates such as YYYY and YYYY-MM are accepted for temporal text values.

DSJC Support

DSJC is treated as gzip-compressed NDJSON.

The current implementation follows the available examples rather than the still-evolving compressed Dataset-JSON v1.1 wording. The specification may change, so DSJC behavior may need to change with it.

API Reference

read_dataset(source, *, as_pandas=False, out_metadata=False, empty_to_null=True)

Read Dataset-JSON, NDJSON, or DSJC from a path, bytes object, or file-like object. It returns a pyarrow.Table by default, or a pandas DataFrame when as_pandas=True. Set out_metadata=True to also receive pyreadstat-compatible metadata. By default, empty strings in string-like columns are converted to null on read; pass empty_to_null=False to keep them as empty strings.

write_dataset(frame, destination, metadata=None, *, output_format=None, define_xml=None, readstat_meta=None, compression_level=6, json_indent=2)

Write a pyarrow.Table or pandas DataFrame as JSON, NDJSON, or DSJC. The output format is inferred from the file suffix unless you pass output_format explicitly. Metadata can come from metadata, define.xml, Arrow schema metadata, or readstat_meta.

detect_format(source)

Inspect a source and return a lightweight report describing the detected format. This is useful when you want to check the input before reading or validating it.

validate_dataset(source, *, validate_metadata=True, validate_data=True)

Validate a dataset and return a diagnostic report. You can validate only metadata, or validate both metadata and row data.

build_metadata(*, frame=None, metadata=None, define_xml=None, readstat_meta=None)

Merge metadata from the available sources and return a validated Dataset-JSON metadata dictionary. Use this when you want to inspect or prepare export metadata before writing rows.

build_schema(source_or_dataset)

Build a pyarrow.Schema from a Dataset-JSON source or from a Dataset-JSON payload dictionary. Use this when you need the inferred Arrow schema without loading the dataset rows.

Common Errors

Case	Example message
Unsupported input source	unsupported input source
Empty file or payload	empty input payload
Format detection failed	could not determine dataset format
Invalid DSJC payload	invalid DSJC payload
Missing required metadata	missing required field: label
JSON Schema validation failed	validation failed at records: ...
Decimal column missing required targetDataType	targetDataType is required for dataType decimal
Invalid targetDataType and dataType combination	targetDataType is not allowed for dataType float
Invalid ISO 8601 temporal text value during read or validation	failed to convert value for column AESTDTC
Row shape does not match columns	row does not match columns schema
records does not match the actual row count	records does not match actual row count
Value conversion failed	failed to convert value for column TRTSDT
Unsupported Dataset-JSON dataType	unsupported column type: binary
pandas output requested without pandas installed	pandas support is not installed

Common Export Errors

Case	Example message
metadata is not a dictionary	metadata must be a dictionary
Required export metadata is missing	missing required export metadata: label
Unexpected metadata key	unexpected metadata fields: unexpected
Invalid datasetJSONCreationDateTime	datasetJSONCreationDateTime has invalid format
Invalid datasetJSONVersion	datasetJSONVersion must be 1.1.x
Incomplete sourceSystem object	sourceSystem requires name and version
Unexpected column metadata key	unexpected column metadata fields: badField
Unsupported targetDataType	unsupported targetDataType: float
Invalid ISO 8601 temporal text value during export	failed to convert value for column AESTDTC
Decimal value exceeds configured length	decimal value exceeds configured length
Unsupported Arrow or pandas type	unsupported Arrow type
Invalid Define-XML syntax	failed to parse define.xml
Missing itemGroupOID in Define-XML	itemGroupOID not found in define.xml

Development

See AGENTS.md and CHANGELOG.md.

License

This project is licensed under the AGPL.

Code from this repository that is provided to an AI system, and code produced from that input, is treated as a derivative work. Redistributing an AI-based reimplementation without preserving this license is considered copyright infringement.

This project is developed and maintained independently.
To help keep it maintained, consider supporting it through sponsorship or by engaging me for contract work.
Contact: [e-mail].

tests/data/official_example and dsjframe/schema are taken from https://github.com/cdisc-org/DataExchange-DatasetJson (Copyright (c) 2022 cdisc) under the MIT license.