Bump blosc2 from 3.0.0b4 to 4.3.0

[dependabot]

Bumps blosc2 from 3.0.0b4 to 4.3.0.

Release notes

Sourced from blosc2's releases.

Release 4.3.0

Changes from 4.2.0 to 4.3.0

CTable: N-dimensional (ndarray) columns

• Multidimensional columns: CTable columns can now hold NDArray-backed cells, allowing each row of a column to contain a full n-dimensional compressed array. This enables use cases such as embedding vectors, image patches, time-series windows, or any other multidimensional per-row payload.
• CSV and DataFrame import/export: Multidimensional column data can be imported and exported via CSV and pandas DataFrames, with automatic detection of array-valued cells.
• Nullable ndarray columns: Multidimensional columns fully support the nullable semantics (null_count, sentinel handling, null_policy) already available for scalar columns.
• from_pandas() improvements: CTable.from_pandas() now creates the correct specialized backing storage for DictionarySpec, ListSpec, VLStringSpec, VLBytesSpec, and other variable-length scalar specifications.
• Improved schema coverage: New CTable timestamp schema type and extended Column.info output with shape, chunks, and blocks descriptors.
• Arg reductions: Added argmin() and argmax() for scalar and ndarray CTable columns, plus row-transformer support for generated columns such as per-row peak-hour or dominant-embedding-dimension features.

CTable: Group-by and filtered aggregation

• CTable.group_by(): The primary group-by interface. Call t.group_by("city", sort=True).agg({"qty": "mean"}) to produce a new :class:CTable with aggregated results. Single-key and multi-key groupings are supported, along with convenience methods such as .size(), .count(), .sum(), .mean(), .min() and .max():

  .. code-block:: python

  by_city = t.group_by("city", sort=True)
  by_city.size()  # COUNT(*)
  by_city.sum("sales")  # SUM(sales) per city
  by_city.agg({"sales": ["sum", "mean"]})  # SUM(sales), AVG(sales) per city
  

• Performance accelerators: Dedicated Cython fast paths deliver significant speedups: ~25× for float32/64 group-by keys, ~8× for integer and dictionary-code keys, and a general-purpose hash table for arbitrary float keys.

• Filtered aggregate pushdown: The where= parameter is now accepted in aggregation methods, pushing the filter into the compute engine so that only matching rows are read and reduced.

• Persistent grouped output: Group-by results can be saved directly to persistent storage via the urlpath= parameter.

• blosc2.group_reduce(): New public function that performs group-by reduction over NDArray instances and CTable columns, with Cython-accelerated backends for common key/reduction combinations.

... (truncated)

Changelog

Sourced from blosc2's changelog.

Changes from 4.3.0 to 4.3.1

XXX version-specific blurb XXX

Changes from 4.2.0 to 4.3.0

CTable: N-dimensional (ndarray) columns

• Multidimensional columns: CTable columns can now hold NDArray-backed cells, allowing each row of a column to contain a full n-dimensional compressed array. This enables use cases such as embedding vectors, image patches, time-series windows, or any other multidimensional per-row payload.
• CSV and DataFrame import/export: Multidimensional column data can be imported and exported via CSV and pandas DataFrames, with automatic detection of array-valued cells.
• Nullable ndarray columns: Multidimensional columns fully support the nullable semantics (null_count, sentinel handling, null_policy) already available for scalar columns.
• from_pandas() improvements: CTable.from_pandas() now creates the correct specialized backing storage for DictionarySpec, ListSpec, VLStringSpec, VLBytesSpec, and other variable-length scalar specifications.
• Improved schema coverage: New CTable timestamp schema type and extended Column.info output with shape, chunks, and blocks descriptors.
• Arg reductions: Added argmin() and argmax() for scalar and ndarray CTable columns, plus row-transformer support for generated columns such as per-row peak-hour or dominant-embedding-dimension features.

CTable: Group-by and filtered aggregation

• CTable.group_by(): The primary group-by interface. Call t.group_by("city", sort=True).agg({"qty": "mean"}) to produce a new :class:CTable with aggregated results. Single-key and multi-key groupings are supported, along with convenience methods such as .size(), .count(), .sum(), .mean(), .min() and .max():

  .. code-block:: python

  by_city = t.group_by("city", sort=True)
  by_city.size()  # COUNT(*)
  by_city.sum("sales")  # SUM(sales) per city
  by_city.agg({"sales": ["sum", "mean"]})  # SUM(sales), AVG(sales) per city
  

• Performance accelerators: Dedicated Cython fast paths deliver significant speedups: ~25× for float32/64 group-by keys, ~8× for integer and dictionary-code keys, and a general-purpose hash table for arbitrary float keys.

• Filtered aggregate pushdown: The where= parameter is now accepted in aggregation methods, pushing the filter into the compute engine so that only matching rows are read and reduced.

• Persistent grouped output: Group-by results can be saved directly to persistent storage via the urlpath= parameter.

• blosc2.group_reduce(): New public function that performs group-by reduction over

... (truncated)

Commits

• a682951 Emphasize the bridge between tabular and array workflows that CTable is now
• ee2ca8e Change blosc2.open() default mode from 'a' to 'r'
• 2d858c6 Getting ready for release 4.3.0
• e96f264 New example on n-dimensional columns
• 05bc79f New argmin/argmax aggregation functions as group_by operations
• f4287ac Add examples of new groupby feature for CTable
• 1fb1354 Simpler description of the unicode type in column header
• 1143b6e Update to latest c-blosc2
• b3d514e Merge pull request #637 from Blosc/ndim-cols
• 2a0d19a Better error handling in CTable._csv_ndarray_col_to_array()
• Additional commits viewable in compare view

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Superseded by #64.