AlenkaF commented on code in PR #49271:
URL: https://github.com/apache/arrow/pull/49271#discussion_r2923544413
##########
python/pyarrow/tests/test_pandas.py:
##########
@@ -3096,10 +3100,10 @@ def _check(v):
result = arr.to_pandas()
tm.assert_series_equal(pd.Series(result), pd.Series(v))
+ base = pd.Categorical(['a', 'b', 'c'])
arrays = [
- pd.Categorical(['a', 'b', 'c'], categories=['a', 'b']),
- pd.Categorical(['a', 'b', 'c'], categories=['a', 'b'],
- ordered=True)
+ base.set_categories(['a', 'b']),
+ base.set_categories(['a', 'b']).as_ordered(),
Review Comment:
I think it would be simpler to do:
```python
pd.Categorical(['a', 'b', None], categories=['a', 'b'])
```
This should be the same use case as reported here
https://github.com/apache/arrow/issues/19704.
##########
python/pyarrow/tests/test_pandas.py:
##########
@@ -3069,15 +3069,19 @@ def test_category(self):
v2 = [4, 5, 6, 7, 8]
v3 = [b'foo', None, b'bar', b'qux', np.nan]
+ cat_strings = pd.Categorical(v1 * repeats)
+ cat_strings_with_na = cat_strings.set_categories(['foo', 'bar'])
Review Comment:
We are probably still getting into missing categories being silently
converted to `NaN` here and Pandas is moving away from that AFAIU.
As the idea in the test is to have `NaN` in the constructed categorical
array, we might simply remove this line as the `cat_strings` actually already
includes them:
```python
In [19]: pd.Categorical(v1 * repeats)
...:
Out[19]:
['foo', NaN, 'bar', 'qux', NaN, ..., 'foo', NaN, 'bar', 'qux', NaN]
Length: 25
Categories (3, str): ['bar', 'foo', 'qux']
```
What we can do is to add:
```python
v0 = ['foo', 'bar', 'qux']
```
and use this for `cat_strings`? This way we do not have to look for a
workaround where we use deprecated behavior to construct `NaN` values due to
missing categories.
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