[Submission]: pyDHIS2 – A Modern Python SDK for DHIS2 with Async I/O, Pandas Integration, and WHO-DQR Validation

[HzaCode]

Submitting Author: (@HzaCode)
All current maintainers: (@HzaCode)
Package Name: pyDHIS2 (PyPI: pydhis2)
One-Line Description of Package: A modern, resilient DHIS2 Python SDK with async I/O, Pandas integration, WHO-DQR data-quality review, and reproducible CLI workflows for LMIC scenarios.
Repository Link: https://github.com/HzaCode/pyDHIS2
Version submitted: 0.2.0
EiC: **@yeelauren **
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Code of Conduct & Commitment to Maintain Package

• I agree to abide by [pyOpenSci's Code of Conduct][PyOpenSciCodeOfConduct].
• I have read and will commit to package maintenance as per the [pyOpenSci Policies Guidelines][Commitment].

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Description

pyDHIS2 (PyPI: pydhis2) is a modern DHIS2 client SDK designed for public-health and research workflows. It provides asynchronous and synchronous clients, streaming pagination, rate-limited retries, Pandas DataFrame outputs, and CLI commands for common tasks such as analytics extraction, tracker events management, and WHO-DQR data-quality review with HTML/JSON reporting.
The package also includes cookiecutter-style project templates for reproducible pipelines (configure → fetch → validate → export).

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Scope

Selected categories

• Data retrieval
• Data extraction
• Data processing/munging
• Data validation and testing
• Workflow automation
• Database interoperability
• Data deposition
• Data visualization
• Citation management and bibliometrics
• Scientific software wrappers

Explanation

• Target audience & applications: Public-health informatics teams, ministries of health, NGO/donor M&E analysts, and researchers who use DHIS2 for analytics, event tracking, or national indicator pipelines. The SDK enables reproducible data extraction, validation, and reporting for large-scale scientific and operational workflows.
• Related packages & differentiation: Existing Python libraries like dhis2.py are thin HTTP wrappers; R packages like khisr or PEPFAR’s datimutils focus on tidyverse-style analytics. pyDHIS2 extends far beyond, offering async/streaming I/O, structured DataFrame outputs, a command-line interface, and built-in WHO-DQR checks for end-to-end reproducibility.
• Pre-submission inquiry: None yet (to be opened if required).

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Domain Specific

• Geospatial
• Education

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Community Partnerships

• Astropy
• Pangeo

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Category justification (1–2 sentences each)

• Data retrieval / extraction: Provides typed clients for DHIS2 endpoints (Analytics, DataValueSets, Tracker events, Metadata) with pagination and resilient retries.
• Data processing/munging: Outputs Pandas DataFrames and supports efficient export to Parquet, CSV, and other analysis formats.
• Data validation and testing: Implements WHO-DQR data-quality evaluation as reusable CLI tools generating structured JSON and HTML summaries.
• Workflow automation: CLI and project templates orchestrate complete data pipelines for periodic DHIS2 synchronization and validation.
• Database interoperability: Offers read/write helpers to manage data-value and tracker payloads, ensuring interoperability with national DHIS2 databases.

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Technical checks

This package:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI-approved license (Apache-2.0).
• contains a README with install and quick-start instructions.
• includes documentation and code examples for all main functions.
• provides a tutorial demonstrating essential features.
• has a comprehensive test suite.
• implements continuous integration via GitHub Actions.

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Publication Options

• Do you wish to automatically submit to the Journal of Open Source Software (JOSS)?

JOSS Checks (only if above is selected)

• The package has an obvious research application.
• The package is not a minor utility or thin client.
• The repository contains a paper.md following JOSS requirements.
• The package is archived with a DOI (e.g., Zenodo).

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Reviewer interaction preference

• Yes, I am OK with reviewers opening issues and pull requests in my repository directly.

Confirm:

• I have read the [author guide](https://www.pyopensci.org/software-peer-review/how-to/author-guide.html).
• I expect to maintain this package for at least 2 years and will ensure continuity of maintenance.