spenczar opened a new issue, #35622:
URL: https://github.com/apache/arrow/issues/35622
### Describe the enhancement requested
Today, FixedSizeListArrays seem to demand that `zero_copy_only` be set to
`False` unnecessarily.
For example:
Create a fixed size list array: 3 rows, with fixed-size of 2:
```py
import pyarrow as pa
data = pa.FixedSizeListArray.from_arrays([1, 2, 3, 4, 5, 6], 2)
print(data)
```
```
<pyarrow.lib.FixedSizeListArray object at 0x104abe140>
[
[
1,
2
],
[
3,
4
],
[
5,
6
]
]
```
Calling `to_numpy()` throws an error:
```py
data.to_numpy()
```
```
ArrowInvalid: Needed to copy 1 chunks with 0 nulls, but zero_copy_only was
True
```
But if I work with buffers directly, I can easily get it to work:
```py
nparray = np.frombuffer(data.buffers()[2], dtype=np.int64())
print(nparray)
```
```
array([1, 2, 3, 4, 5, 6])
```
We also know enough to even give it the right ndarray shape:
```py
nparray_shaped = np.frombuffer(data.buffers()[2],
dtype=np.int64()).reshape(len(data), data.type.list_size)
print(nparray_shaped)
```
```
array([[1, 2],
[3, 4],
[5, 6]])
```
---
I propose that `FixedSizeListArray.to_numpy()` should return numpy arrays
with zero copy if the FixedSizeList's type is an integer or floating point
type, since those are safe to convert, and if no nulls are present.
I also propose that it reshape the output to be an ndarray which matches the
FixedSizeList's shape.
### Component(s)
Python
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