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0.6.1

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@protocolstardust protocolstardust released this 03 Mar 14:18
· 5 commits to master since this release
0.6.1
d7ba027

New Features

  • Fancy indexing for Tables: Table.__getitem__ now supports multiple indexing modes beyond column access:

    • Expression filter: table[Column("age") > 35] — filter rows by condition. Supports & (and) and | (or) for combining expressions.
    • Integer row access: table[0], table[-1] — access a single row by index, returns a Dict.
    • Slicing: table[1:3], table[:5], table[-2:] — row slicing backed by the C-level TAKE operation.
    • Index list: table[[0, 2, 5]] — select specific rows by position.

  • Vector.from_numpy() auto-widening: Unsupported numpy dtypes are now automatically widened to the nearest supported type: float32/float16 → F64, int8 → I16, uint16 → I32, uint32 → I64.

  • Vector.from_numpy() bytes and UUID support: Byte string arrays (dtype='S') are automatically decoded to Symbol vectors. Object arrays of uuid.UUID values are detected and converted to GUID vectors.

  • NaT preservation: NaT (Not-a-Time) values in numpy datetime64 and timedelta64 arrays now survive round-trips through Vector.from_numpy() and Vector.to_numpy().

Bug Fixes

  • Table.to_numpy() with Timestamp columns: Fixed DTypePromotionError when calling to_numpy() on tables containing a mix of incompatible column types (e.g., integers, strings, and timestamps). Mixed-type tables now gracefully fall back to object dtype.

  • Filtering F64 by distinct - fixed

  • Vector.__getitem__ for U8 vectors: Fixed U8 vector elements being returned as B8(True/False) instead of U8(value). Both types are 1-byte, causing the C-level at_idx to misinterpret the type.

  • Vector.from_numpy() with explicit ray_type for temporal arrays: Fixed ValueError: cannot include dtype 'M' in a buffer when passing ray_type=Timestamp, ray_type=Date, or ray_type=Time with datetime64/timedelta64 arrays.

2026-03-03 | 🔗 PyPI | 🔗 GitHub

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