jorisvandenbossche commented on code in PR #15210:
URL: https://github.com/apache/arrow/pull/15210#discussion_r1064847072
##########
cpp/src/arrow/array/array_list_test.cc:
##########
@@ -509,6 +509,18 @@ class TestListArray : public ::testing::Test {
ASSERT_RAISES(Invalid, ValidateOffsets(2, {0, 7, 4}, values));
}
+ void TestSliced() {
+ auto arr = ArrayFromJSON(list(int16()), "[[1, 2], [3, 4, 5], [6], [7,
8]]");
+
+ auto arr_sliced = arr->Slice(0, 2);
+ auto expected_sliced = ArrayFromJSON(list(int16()), "[[1, 2], [3, 4, 5]]");
+ AssertArraysEqual(*expected_sliced, *arr_sliced);
+
+ auto values = checked_cast<ListArray*>(arr_sliced.get())->values();
+ auto expected_values = ArrayFromJSON(int16(), "[1, 2, 3, 4, 5]");
+ AssertArraysEqual(*expected_values, *values);
Review Comment:
Yes, but your example uses an actual null inside a list, not a "null list".
For example, here the first null is not in the flattened output:
```
In [3]: arr = pa.array([[1, 2], None, [3, None]])
In [4]: arr.flatten()
Out[4]:
<pyarrow.lib.Int64Array object at 0x7f8b1fbcaa40>
[
1,
2,
3,
null
]
```
And I suppose behind that null can be any data (the default when constructed
above is that there is no data behind, so the offset doesn't increment for that
list element):
```
In [5]: arr.offsets
Out[5]:
<pyarrow.lib.Int32Array object at 0x7f8b4f0f5a80>
[
0,
2,
2,
4
]
```
It's a bit tricky to construct manually, but something like:
```
In [10]: arr = pa.ListArray.from_arrays(pa.array([0, 2, 4, 6]), pa.array([1,
2, 99, 99, 3, None]), mask=pa.array([False, True, False]))
In [11]: arr
Out[11]:
<pyarrow.lib.ListArray object at 0x7f8b1fbcba60>
[
[
1,
2
],
null,
[
3,
null
]
]
In [12]: arr.flatten()
Out[12]:
<pyarrow.lib.Int64Array object at 0x7f8b4f065960>
[
1,
2,
3,
null
]
In [13]: arr.values
Out[13]:
<pyarrow.lib.Int64Array object at 0x7f8b4f065780>
[
1,
2,
99,
99,
3,
null
]
In [14]: arr.offsets
Out[14]:
<pyarrow.lib.Int32Array object at 0x7f8b1fbc9300>
[
0,
2,
4,
6
]
```
But, so I am not sure if for this case you actually need to flattened (with
nulls removed) or not for this case of converting to numpy.
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