Copilot commented on code in PR #50203:
URL: https://github.com/apache/arrow/pull/50203#discussion_r3426986690
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python/pyarrow/tests/test_array.py:
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@@ -2924,6 +2924,24 @@ def test_array_from_invalid_dim_raises():
pa.array(arr0d)
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+def test_fixed_size_list_from_multidim_ndarray():
+ # GH-49644: a fixed-size list can be built from multi-dimensional ndarray
+ # elements by flattening them in C order.
+ arr = pa.array([np.array([[1, 2, 3]]), np.array([[4, 5, 6]])],
+ type=pa.list_(pa.int64(), 3))
Review Comment:
These `np.array(...)` calls rely on NumPy’s default integer dtype, which can
vary by platform/architecture. To keep the test deterministic, explicitly set
`dtype=np.int64` for the NumPy arrays used to build a `pa.int64()` fixed-size
list.
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python/pyarrow/tests/test_array.py:
##########
@@ -2924,6 +2924,24 @@ def test_array_from_invalid_dim_raises():
pa.array(arr0d)
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+def test_fixed_size_list_from_multidim_ndarray():
+ # GH-49644: a fixed-size list can be built from multi-dimensional ndarray
+ # elements by flattening them in C order.
+ arr = pa.array([np.array([[1, 2, 3]]), np.array([[4, 5, 6]])],
+ type=pa.list_(pa.int64(), 3))
+ assert arr.type == pa.list_(pa.int64(), 3)
+ assert arr.to_pylist() == [[1, 2, 3], [4, 5, 6]]
Review Comment:
The test currently only exercises a (1, 3) shaped input, where C-order
flattening is effectively the same as reading the row. To validate the new
multi-dimensional flattening behavior more robustly, add an assertion using a
non-trivial 2D shape (e.g., (2, 2) into a fixed-size list of 4) to confirm the
produced list follows C-order flattening.
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