egolearner commented on code in PR #47199:
URL: https://github.com/apache/arrow/pull/47199#discussion_r2242686563
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python/pyarrow/tests/parquet/common.py:
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@@ -121,6 +121,11 @@ def _test_dataframe(size=10000, seed=0):
return df
+def _test_table(size=10000, seed=0):
+ df = _test_dataframe(size, seed)
+ return pa.Table.from_pandas(df, preserve_index=False)
Review Comment:
Thanks for your review @rok
I have added `_test_dict` function as data generation logic for both
`_test_dataframe` and `_test_table`. PTAL
> It might even be good to have fallback logic in _test_table for cases
numpy is not available. This logic could use stdlib's random or some testing
utility we have available in arrow c++.
Maybe we can deal this in another issue? It seems `numpy` is still a must
for a lot of test cases.
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