SciDuck
A thin DuckDB layer for managing versioned scientific data.
Each variable is stored in its own table. Variables are associated with a hierarchical dataset schema (e.g. subject -> session -> trial) and can be saved at any level of that hierarchy. Multiple versions of each variable are supported natively.
All data -- including arrays -- is stored in queryable DuckDB types (LIST, nested LIST, JSON) so the database can be inspected with DBeaver or any DuckDB-compatible viewer.
Usage
from sciduckdb import SciDuck
duck = SciDuck("data.duckdb", dataset_schema=["subject", "session"])
duck.save("MyVar", data, subject="S01", session=1)
loaded = duck.load("MyVar", subject="S01", session=1)
Features
- Three save modes: DataFrame with schema columns (Mode A), single entry via kwargs (Mode B), or dict mapping tuples to values (Mode C)
- Automatic type inference: Maps Python/numpy types to DuckDB types
- Round-trip restoration: Metadata tracks original types for lossless load
- Version management: Automatic version numbering, duplicate hash detection
- Variable groups: Organize variables into named groups
- Schema validation: Validates dataset schema consistency across sessions
Note: all schema key values are coerced to strings before storage.
Datetimes
A datetime value (datetime.datetime, pd.Timestamp, np.datetime64, NaT) is
stored as two columns: <col> TIMESTAMP_NS holding the UTC instant (or a
naive value's wall clock), and <col>__tz VARCHAR holding that value's zone
("Europe/Berlin", "UTC", "+02:00"; NULL = naive). Zones are per value, so
one column may hold several zones and naive values. Loads give back each value
in its own zone. The __tz column exists from the column's first save and is
never listed in the variable's data columns. In raw SQL, filter on
<col>__tz IS NULL to tell naive rows apart. Owner: sciduckdb.datetimes;
design: docs/claude/datetime-roundtrip.md.
1 maintainer
Metadata
1 maintainer
Release files for sciduckdb 0.1.56
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