sergun opened a new issue, #38643:
URL: https://github.com/apache/arrow/issues/38643
### Describe the usage question you have. Please include as many useful
details as possible.
I have pa.Table with neseted column events:
```
id int64
events list<item: struct<tm: timestamp[s], sum: int64>>
```
It is easy to convert it to pandas with pa.Table.to_pandas() method but it
creates pd.DataFrame with column events of object type:
```
id int64
events object
```
And further flattening of the data in pandas is inefficient.
How can I efficiently convert the table in PyArrow to flattened pd.DataFrame
with columns id, tm, sum?
It is possible e.g. in Spark powered by Arrow:
```
df.select("id", explode("events")).select("id", "col.*")
```
And I hope it should be also possible in PyArrow only.
### Component(s)
Python
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