dsjson 0.1.1

A Python package to convert tabular data and metadata into CDISC Dataset-JSON v1.1 format.

📄 README.md

Project: dsjson

A lightweight Python package to convert clinical tabular datasets (e.g., SDTM/ADaM) and metadata into CDISC Dataset-JSON v1.1 format. It supports multiple metadata input formats including CSV, Excel, JSON, and XML (planned).

🔧 Features

• Converts DataFrame + column metadata to Dataset-JSON v1.1
• Supports CSV, Excel, JSON for metadata
• Auto-generates datasetJSONCreationDateTime
• Enforces required top-level metadata
• Clean and minimal API

📦 Installation

# Local installation (dev mode)
pip install -e .

# With test dependencies
pip install -e .[test]

🚀 Quick Start

from dsjson import load_metadata, to_dataset_json
import pandas as pd

# Load data and metadata
rows = pd.read_csv("examples/vs.csv")
columns = load_metadata("examples/columns_vs.csv", file_type="csv")

# Create Dataset-JSON
ds = to_dataset_json(
    data_df=rows,
    columns_df=columns,
    name="VS",
    label="Vital Signs",
    itemGroupOID="IG.VS",
    originator="My CRO",
    sourceSystem_name="Python",
    sourceSystem_version="3.10",
    fileOID="F.VS.001",
    studyOID="S.1234"
)

📁 Supported Input Types

• Column Metadata: .csv, .xlsx, .json, (planned: .xml)
• Data Table: Any Pandas-compatible format

✅ Output Example

{
  "datasetJSONVersion": "1.1",
  "datasetJSONCreationDateTime": "2025-07-19T00:00:00",
  "name": "VS",
  "label": "Vital Signs",
  "itemGroupOID": "IG.VS",
  "columns": [...],
  "rows": [...],
  "records": 100,
  "originator": "My CRO",
  "sourceSystem": {
    "name": "Python",
    "version": "3.13"
  }
}