jorisvandenbossche commented on a change in pull request #7169:
URL: https://github.com/apache/arrow/pull/7169#discussion_r431368560
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File path: python/pyarrow/tests/test_pandas.py
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@@ -3965,8 +3964,32 @@ def test_timestamp_as_object():
np.datetime64('2050-05-03 15:42', 'ns'),
],
})
+ # Not part of what we're testing, just ensuring that the inputs are what we
+ # expect.
+ assert (df.dateTimeMs.dtype, df.dateTimeNs.dtype) == (
+ # O == object, <M8[ns] == timestamp64[ns]
+ np.dtype("O"), np.dtype("<M8[ns]")
+ )
+ return df
+
+
[email protected]
+def test_timestamp_as_object_parquet(tempdir):
+ # Timestamps can be stored as Parquet and reloaded into Pandas with no loss
+ # of information if the timestamp_as_object option is True.
+ df = make_df_with_timestamps()
table = pa.Table.from_pandas(df)
- pq.write_table(table, 'timeseries.parquet', version="2.0")
- result = pq.read_table('timeseries.parquet')
+ filename = tempdir / "timestamps_from_pandas.parquet"
+ pq.write_table(table, filename, version="2.0")
+ result = pq.read_table(filename)
df2 = result.to_pandas(timestamp_as_object=True)
tm.assert_frame_equal(df, df2, check_like=True)
+
+
+def test_timestamp_as_object():
+ # Timestamps can be converted Arrow and reloaded into Pandas with no loss
+ # of information if the timestamp_as_object option is True.
+ df = make_df_with_timestamps()
+ table = pa.Table.from_pandas(df)
+ df2 = table.to_pandas(timestamp_as_object=True)
+ tm.assert_frame_equal(df, df2, check_like=True)
Review comment:
I would still like to see more explicit tests (eg from this test it is
still not clear to me what is actually being returned is a datetime.datetime
object or not)
Eg something like
```
import datetime
dt = datetime.datetime(2012, 1, 1)
arr = pa.array([dt], type=pa.timestamp('s'))
result = arr.to_pandas(timestamp_as_object=True)
assert result.dtype == object
assert isinstance(result[0], datetime.datetime)
assert result[0] == dt
```
And the above can be done for the different resolutions (except ns) to cover
that as well.
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