2.1.0

New Features

• Restored WebSocket support. The WSClient / WSServer classes and the rayforce.network.websocket module are back (they were removed in 2.0.0). The server is an async Python implementation, built on the websockets library, that evaluates Rayforce queries in-process. Install with the websocket extra (pip install rayforce-py[websocket]). See the WebSocket guide.
• Added the PyArrow plugin. from_arrow() converts a pyarrow.Table into a Rayforce Table, mirroring the Polars / Pandas plugins. A column of strings maps to String by default, or to Symbol via the opt-in strings_as_symbols=True flag. Requires the parquet extra (pip install rayforce-py[parquet]).

Bug fixes

• Fixed mis-scaled Parquet timestamps. load_parquet() now reads microsecond- and millisecond-precision timestamp columns at the correct precision; previously non-nanosecond units were mis-scaled. Parquet reading now shares the PyArrow plugin's conversion path.

2026-06-22 | 🔗 PyPI | 🔗 GitHub

2.0.2

2.0.2

New Features

• Added the ungroup verb. Table.ungroup() flattens nested LIST columns
  into row form — each list cell expands to one row, with atom columns replicated
  per element (the inverse of a by-grouping). Also available as a deferred step
  on select queries: t.select(...).by(...).ungroup().execute().

Bug fixes

• Fixed a bug when the Timestamp type didn't account for TZ offset when using from_csv handler

2026-06-18 | 🔗 PyPI | 🔗 GitHub

2.0.1

Bug Fixes

Fix Type error when attempting to insert/upsert into an empty table.

2026-06-17 | 🔗 PyPI | 🔗 GitHub

2.0.0

First release against the Rayforce v2 C core. This is a major release with
breaking changes to the type system, the network layer, and the plugin surface.
The 1.0.x line continues to be maintained against the v1 core.

Breaking Changes

• Built against the Rayforce v2 C core. The bundled engine, build pipeline
  (make app), and binary protocol all target v2. Code written against the v1
  core may need updates — see the items below.

• Removed the C8 / Char type. v2 has no standalone character type; a
  single character is now a length-1 String.
  C8 is no longer importable from rayforce.

• Removed WebSocket support. The WSClient / WSServer classes and the
  entire rayforce.network.websocket module have been removed. Use
  TCP IPC (TCPClient / TCPServer) for networked queries.

• Renamed the KDB+ plugin. rayforce.plugins.raykx is now
  rayforce.plugins.kdb; import KDBEngine
  from the new path.

• Renumbered scalar type codes to match the v2 core. Atoms use the negative
  of these values (see the Data Types overview):

  Type	v1 code	v2 code
  Symbol	6	12
  F64	10	7
  Date	7	8
  Time	8	9
  Timestamp	9	10
  String	—	13
  F32	—	6

New Features

• Added the F32 (32-bit floating-point) type. Arithmetic on F32 promotes
  to F64.
• Added the RayforceLimitError exception class (core EC_LIMIT).

Notes

• String is now a first-class type (no longer modeled as a vector of C8).

2026-06-12 | 🔗 PyPI | 🔗 GitHub

2.0.0a1

First alpha against the Rayforce v2 C core. The 1.0.x line continues to be maintained against v1 core.

2026-04-28 | 🔗 PyPI | 🔗 GitHub

1.0.0

• Project has came out of beta. Stable release

2026-04-20 | 🔗 PyPI | 🔗 GitHub

0.6.3

Bug Fixes

• Integer null sentinel handling: I16, I32, and I64 scalars now correctly recognize null sentinels (0Nh, 0Ni, 0Nj) and return None from to_python(). This fixes null comparisons in operations where unmatched rows returned raw sentinel values (e.g., -9223372036854775808) instead of None.

2026-04-15 | 🔗 PyPI | 🔗 GitHub

0.6.2

Bug Fixes

• Now library passes correct contiguous numpy arrays within from_numpy function
• F64 vectors can now be used in median calculations

2026-03-15 | 🔗 PyPI | 🔗 GitHub

0.6.1

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

0.6.0

New Features

• Vector.from_numpy() classmethod: Create vectors from NumPy arrays via bulk memory copy. Supports numeric types (int16, int32, int64, float64, uint8, bool), string arrays, and temporal types (datetime64 → Timestamp/Date, timedelta64 → Time). Handles epoch adjustment between NumPy (1970-01-01) and Rayforce (2000-01-01) automatically.

• Vector.to_numpy() method: Export vector data to a NumPy array via bulk memory copy.

• Vector.to_list() method: Export vector data to a Python list via bulk memory copy from the underlying C buffer.

• Table.from_dict() classmethod: Create tables from a dictionary of NumPy arrays, Python lists, or Vectors.

• Table.to_dict() method: Export table data to a Python dictionary of lists via bulk memory copy.

• Table.to_numpy() method: Export table data to a 2D NumPy array via bulk memory copy.

• select("*", col=value) fix: Using "*" with computed columns in select() now correctly preserves all existing columns

Dependencies

• NumPy (>=2.0.0) is now a required dependency.

2026-02-21 | 🔗 PyPI | 🔗 GitHub