llama90 commented on issue #38559:
URL: https://github.com/apache/arrow/issues/38559#issuecomment-1790681605
Here's what I found.
In the given code, the steps are necessary because `pyarrow` cannot
automatically infer the type of the user-defined `Point` objects within the
dataset.
When creating an Arrow array, the data type needs to be compatible with
Arrow's type system.
Therefore, the `structured_array` is created to convert the `Point` objects
into a format that `pyarrow` can understand, which is a list of dictionaries in
this case.
Then, when calling `pa.array`, the type argument is used to explicitly
specify the schema of the Arrow array, ensuring that the data is correctly
typed as a structured array with fields `x` and `y` of type int64.
```python
import pyarrow as pa
class Point:
def __init__(self, iterable):
self._data = iterable
@property
def data(self) -> dict:
return self._data
if __name__ == '__main__':
pt1 = Point({"x": 1, "y": 2, })
pt2 = Point({"x": 3, "y": 4, })
pt3 = Point({"x": 5, "y": 6, })
pt4 = Point({"x": 7, "y": 8, })
dataset = [pt1, pt2, pt3, pt4]
structured_array = [pt.data for pt in dataset]
arrow_array = pa.array(structured_array, type=pa.struct([('x',
pa.int64()), ('y', pa.int64())]))
chunked_array = pa.chunked_array([arrow_array])
print(chunked_array)
--- output
[
-- is_valid: all not null
-- child 0 type: int64
[
1,
3,
5,
7
]
-- child 1 type: int64
[
2,
4,
6,
8
]
]
```
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