merge: Integrate modern-fetch-api branch

Merged claude/modern-fetch-api with include_key support for to_arrays().
Resolved docstring conflict by keeping NumPy style with new functionality.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: dimitri-yatsenko

src/datajoint/expression.py

@@ -908,12 +908,14 @@ def to_arrays(
             If no attrs: structured array (recarray) of all columns.
             If single attr: 1D array of values.
             If multiple attrs: tuple of arrays.
+            With include_key=True: (keys, *arrays) where keys is list[dict].
 
         Examples
         --------
         >>> table.to_arrays()              # Structured array of all columns
         >>> table.to_arrays('x', 'y')      # Tuple of two arrays
         >>> x = table.to_arrays('x')       # Single array
+        >>> keys, a, b = table.to_arrays('a', 'b', include_key=True)  # With keys
         """
         from functools import partial
 
@@ -931,6 +933,10 @@ def to_arrays(
             projected = expr.proj(*fetch_attrs)
             dicts = projected.to_dicts(squeeze=squeeze, download_path=download_path)
 
+            # Extract keys if requested
+            if include_key:
+                keys = [{k: d[k] for k in expr.primary_key} for d in dicts]
+
             # Extract arrays for requested attributes
             result_arrays = []
             for attr in attrs:
@@ -943,6 +949,8 @@ def to_arrays(
                     arr = np.array(values, dtype=object)
                 result_arrays.append(arr)
 
+            if include_key:
+                return (keys, *result_arrays)
             return result_arrays[0] if len(attrs) == 1 else tuple(result_arrays)
         else:
             # Fetch all columns as structured array

src/datajoint/version.py

@@ -1,4 +1,4 @@
 # version bump auto managed by Github Actions:
 # label_prs.yaml(prep), release.yaml(bump), post_release.yaml(edit)
 # manually set this version will be eventually overwritten by the above actions
-__version__ = "2.0.0a12"
+__version__ = "2.0.0a13"

tests/integration/test_fetch.py

@@ -344,3 +344,56 @@ def test_lazy_iteration(lang, languages):
     first = next(iter_obj)
     assert isinstance(first, dict)
     assert "name" in first and "language" in first
+
+
+def test_to_arrays_include_key(lang, languages):
+    """Test to_arrays with include_key=True returns keys as list of dicts"""
+    # Fetch with include_key=True
+    keys, names, langs = lang.to_arrays("name", "language", include_key=True, order_by="KEY")
+
+    # keys should be a list of dicts with primary key columns
+    assert isinstance(keys, list)
+    assert all(isinstance(k, dict) for k in keys)
+    assert all(set(k.keys()) == {"name", "language"} for k in keys)
+
+    # names and langs should be numpy arrays
+    assert isinstance(names, np.ndarray)
+    assert isinstance(langs, np.ndarray)
+
+    # Length should match
+    assert len(keys) == len(names) == len(langs) == len(languages)
+
+    # Keys should match the data
+    for key, name, language in zip(keys, names, langs):
+        assert key["name"] == name
+        assert key["language"] == language
+
+    # Keys should be usable for restrictions
+    first_key = keys[0]
+    restricted = lang & first_key
+    assert len(restricted) == 1
+    assert restricted.fetch1("name") == first_key["name"]
+
+
+def test_to_arrays_include_key_single_attr(subject):
+    """Test to_arrays include_key with single attribute"""
+    keys, species = subject.to_arrays("species", include_key=True)
+
+    assert isinstance(keys, list)
+    assert isinstance(species, np.ndarray)
+    assert len(keys) == len(species)
+
+    # Verify keys have only primary key columns
+    assert all("subject_id" in k for k in keys)
+
+
+def test_to_arrays_without_include_key(lang):
+    """Test that to_arrays without include_key doesn't return keys"""
+    result = lang.to_arrays("name", "language")
+
+    # Should return tuple of arrays, not (keys, ...)
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    names, langs = result
+    assert isinstance(names, np.ndarray)
+    assert isinstance(langs, np.ndarray)