docs(rfc): RFC 0004 — DataFrame und Blob (Content-Layer vs. Multi-Format)#73
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…mat) Analyse (Status: Proposed), ob DataFrame auf Blob erben soll. Befund: nein — das Blob-Modell (ein fixer Content-Blob) passt schlecht auf die lebende, in viele Formate kodierbare pandas-Tabelle. Optionen A (Vererbung, verworfen: to_dict-Bruch, Sync-Problem, Hash-Mehrdeutigkeit), B (Integritäts-Mixin), C (Komposition DataFrame.as_blob(fmt)), D (Status quo). Empfehlung: Komposition (C) + optional Mixin (B), keine Vererbung. Klärt die "Grundlage" als gemeinsamen Layer statt zwingender Basisklasse. In die mkdocs-RFC-Navigation aufgenommen.
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Analyse-RFC (Status: Proposed, kein Code) als Abschluss von RFC 0003: soll
DataFrameaufBloberben?Befund
Nein. Das
Blob-Modell (ein fixer Content-Blob) passt schlecht auf dasDataFrame-Modell (eine lebende pandas-Tabelle, on-demand in viele Formate kodierbar — Parquet/Arrow/Feather/CSV/dict/JSON-LD/HDF5/Data Package).Optionen
DataFrame(Blob)— verworfen: bricht dasto_dict-Layout (data.parquet_bytes/column_metadatavs.data.content), Sync-Problem (lebende df), mehrdeutige Hash-Semantik.verify-Layer fürBlobundDataFrame, additiv.DataFrame.as_blob(fmt="parquet") -> Blob— Tabelle bei Bedarf als Blob in jedem Format ausgeben; null Bruchrisiko.Empfehlung
Komposition (C) als primärer Weg, optional ergänzt um den Mixin (B); keine Vererbung. Klärt die „Grundlage aller Datenformate" als gemeinsamen Layer (single-content erbt, mehrformatig komponiert), nicht als zwingende Basisklasse. Inkl. Testplan + offene Punkte (Prüfsummen-Determinismus bei Parquet, Default-Format).
In die mkdocs-RFC-Navigation aufgenommen;
mkdocs build --strictexit 0.