sergun commented on issue #38643:
URL: https://github.com/apache/arrow/issues/38643#issuecomment-1808896791
> Exploding is currently something that isn't provided out of the box, see
#27923 for an issue on this topic and some example workarounds (using existing
pyarrow compute functions to achieve the same effect).
>
> Once you exploded the list over multiple rows, you can flatten the table
with the struct type into a table with a top-level column for each struct field
with the `flatten()` method:
>
> ```
> >>> table = pa.table({"id": [1, 1, 2], "events": [{"tm":
pd.Timestamp("2012-01-01"), "sum": 10}] * 3})
> >>> table.to_pandas()
> id events
> 0 1 {'sum': 10, 'tm': 2012-01-01 00:00:00}
> 1 1 {'sum': 10, 'tm': 2012-01-01 00:00:00}
> 2 2 {'sum': 10, 'tm': 2012-01-01 00:00:00}
>
> >>> table.flatten().to_pandas()
> id events.sum events.tm
> 0 1 10 2012-01-01
> 1 1 10 2012-01-01
> 2 2 10 2012-01-01
> ```
Thx a lot!
#27923 + pa.table.flatten() solves the issue
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