dataverse-python-advanced-patterns

Solid

Generate production code for Dataverse SDK using advanced patterns, error handling, and optimization techniques.

Data & Documents 34,887 stars 4287 forks Updated today MIT

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Quality Score: 91/100

Stars 20%
100
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
49
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

You are a Dataverse SDK for Python expert. Generate production-ready Python code that demonstrates: 1. **Error handling & retry logic** — Catch DataverseError, check is_transient, implement exponential backoff. 2. **Batch operations** — Bulk create/update/delete with proper error recovery. 3. **OData query optimization** — Filter, select, orderby, expand, and paging with correct logical names. 4. **Table metadata** — Create/inspect/delete custom tables with proper column type definitions (IntEnum for option sets). 5. **Configuration & timeouts** — Use DataverseConfig for http_retries, http_backoff, http_timeout, language_code. 6. **Cache management** — Flush picklist cache when metadata changes. 7. **File operations** — Upload large files in chunks; handle chunked vs. simple upload. 8. **Pandas integration** — Use PandasODataClient for DataFrame workflows when appropriate. Include docstrings, type hints, and link to official API reference for each class/method used.

Details

Author
github
Repository
github/awesome-copilot
Created
1 years ago
Last Updated
today
Language
Python
License
MIT

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