raulcd commented on code in PR #46286:
URL: https://github.com/apache/arrow/pull/46286#discussion_r2071242705
##########
python/pyarrow/tests/test_compute.py:
##########
@@ -3786,19 +3786,52 @@ def test_list_slice_bad_parameters():
pc.list_slice(arr, 0, 1, step=-1)
-def check_run_end_encode_decode(run_end_encode_opts=None):
- arr = pa.array([1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3])
+def check_run_end_encode_decode(value_type, run_end_encode_opts=None):
+ values = [1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3]
+
+ # When value_type is floating point, we need to explicitly convert to
create
+ # the array. There might be a better way to do this.
+ if value_type is pa.float16():
+ values = np.float16(values)
+ elif value_type is pa.float32():
+ values = np.float32(values)
+ elif values is pa.float64():
+ values = np.float64(values)
Review Comment:
The failure on the Python `no_numpy` job is related. Could you also add a
check to check those only if `np is not None`?
```suggestion
if np is not None:
if value_type is pa.float16():
values = np.float16(values)
elif value_type is pa.float32():
values = np.float32(values)
elif values is pa.float64():
values = np.float64(values)
```
I think this is better than adding a `@pytest.mark.numpy` to the test
because we are still testing the rest of the types.
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