feat(dataframe): Interop/Export – CSV, Arrow, Feather (PR3)#53
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…Frame Zusätzliche Austauschformate, strikt additiv, 100 % Coverage. CSV ist immer verfügbar (reines pandas), Arrow/Feather sind pyarrow-guarded (Extra [parquet]). - to_csv(path/filename/sidecar, **kwargs) / from_csv(filepath, **kwargs): reines pandas, keine neue Dependency. CSV trägt nur Daten; die qualifizierenden Metadaten reisen im optionalen <sname>.meta.jsonld-Sidecar. Index standardmäßig weggelassen (per index=True überschreibbar). - to_arrow() -> pyarrow.Table mit sdata-Metadaten (metadata/column_metadata/ description als JSON unter dem b"_sdata"-Schema-Key, neben pandas' Metadaten); from_arrow(table) stellt sie wieder her. - to_feather(path/filename/sidecar, **kwargs) / from_feather(filepath): Arrow-IPC über pyarrow.feather; schreibt die to_arrow()-Table (Metadaten bleiben erhalten), ohne path -> Bytes. - Klare ImportError-Meldung ohne pyarrow (wiederverwendet _require_parquet). - Tests: tests/test_sclass_dataframe_interop.py (CSV-Round-Trip + Sidecar + filename-only-Zweig + FileNotFoundError; Arrow/Feather via importorskip, Metadaten im Arrow-Schema, Annotationen über Round-Trip erhalten).
Not up to standards ⛔🔴 Issues
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| Category | Results |
|---|---|
| Documentation | 9 minor |
| Security | 14 high |
🟢 Metrics 37 complexity · 0 duplication
Metric Results Complexity 37 Duplication 0
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Dritter von vier PRs zur Verbesserung von
sdata/sclass/dataframe.py— Interop/Export. Strikt additiv (load-bearing API unverändert), lokale CI grün, 100 % Coverage.Neu
to_csv(path/filename/sidecar, **kwargs)/from_csv(filepath, **kwargs)— reines pandas, keine neue Dependency. CSV trägt nur Daten; die qualifizierenden Metadaten reisen im optionalen<sname>.meta.jsonld-Sidecar. Index standardmäßig weggelassen (perindex=Trueüberschreibbar).to_arrow()→pyarrow.Tablemit sdata-Metadaten (metadata/column_metadata/descriptionals JSON unter demb"_sdata"-Schema-Key, neben pandas' eigenen Metadaten);from_arrow(table)stellt sie wieder her.to_feather(path/filename/sidecar, **kwargs)/from_feather(filepath)— Arrow-IPC überpyarrow.feather; schreibt dieto_arrow()-Table (Metadaten bleiben erhalten); ohnepath→ Bytes.Optional-Dependency
_require_parquet("pyarrow")geschützt → klareImportError-Meldung mit Hinweispip install sdata[parquet].Tests
tests/test_sclass_dataframe_interop.py— CSV-Round-Trip (String + Datei + Sidecar +filename-only-Zweig +FileNotFoundError); Arrow/Feather viaimportorskip("pyarrow"), Metadaten im Arrow-Schema, Annotationen (unit/description) über den Round-Trip erhalten.Lokale CI grün (
ci/local-ci.sh);dataframe.pyund Projekt-Total = 100 % (496 passed, 9 optionale Skips).