BUG: Pyarrow 2.0.0 broke test_timezone_aware_index 6/7 tests

[phofl]

Pyarrow 2.0.0 was released yesterday and broke the test test_timezone_aware_index in 6 of 7 cases. Can be seen through our CI. Location of test is pandas/tests/io/test_parquet.py

Link to ci:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=45611&view=logs&j=b1c7b65e-b3ce-541a-7fd5-29b4ba56ce18&t=46ecc253-e38f-5abc-2ea7-addca6b44d0a&l=22

cc @jreback

[jreback]

thanks @phofl
yeah i think we need to fix these tests and then pinpyarrow <=1.0 on most branches (let this float on as many branches as you can though).

hopefuly its just our tests and not an actual break in pyarrow.

cc @jorisvandenbossche

[jorisvandenbossche]

Yes, this is only a problem in our testing machinery, not an actual bug (there is a small change in behaviour in pyarrow, but not one that should be breaking for users, I think).
I put up #37303 to temporary skip the test until we fix it properly.

[jorisvandenbossche]

So first, to explain what changed in pyarrow 2.0.

If you have a DataFrame using datetime.timezone fixed offset timezone, and write this to parquet, and read it back, this timezone might get converted into a pytz.FixedOffset:

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
import datetime

dt = datetime.datetime.now(datetime.timezone.min)
values = 5 * [dt]
df = pd.DataFrame({"column": values}, index=values)
df.to_parquet("data.parquet")

With pyarrow 1.0, this only happens for columns, and not for the index (but note that until recently, it actually errored for the index, which was fixed in #36004):

In [7]: pa.__version__
Out[7]: '1.0.1'

In [8]: result = pd.read_parquet("data.parquet")

In [9]: result.index.dtype
Out[9]: datetime64[ns, UTC-23:59]

In [10]: result.column.dtype
Out[10]: datetime64[ns, pytz.FixedOffset(-1439)]

What we changed in pyarrow 2.0, is that we now consistently use pytz.FixedOffset:

In [2]: pa.__version__
Out[2]: '2.0.0.dev528+gaec1f97e7'

In [3]: result = pd.read_parquet("data.parquet")

In [4]: result.index.dtype
Out[4]: datetime64[ns, pytz.FixedOffset(-1439)]

In [5]: result.column.dtype
Out[5]: datetime64[ns, pytz.FixedOffset(-1439)]

(this could also be consistently datetime.timezone, but then pytz fixed timezone would not be preserved. So there will always be some mismatch, as long as pyarrow does not record the original timezone class, which I am not sure we should do).

In practice, those datetime values are equal, there timezone have the offset, etc, but it's our testing machinery that cannot handle those different but equivalent timezones.

[jorisvandenbossche]

To focus on the pandas part, let's illustrate the problem.

We create two Series objects with the same values but datetime.timezone vs pytz.FixedOffset fixed offset timezone:

import datetime
import pytz

dt1 = datetime.datetime.now(datetime.timezone.min)
dt2 = dt1.astimezone(pytz.FixedOffset(dt1.utcoffset().total_seconds() / 60))
s1 = pd.Series([dt1])
s2 = pd.Series([dt2])

In [2]: s1
Out[2]: 
0   2020-10-20 09:49:37.628339-23:59
dtype: datetime64[ns, UTC-23:59]

In [3]: s2
Out[3]: 
0   2020-10-20 09:49:37.628339-23:59
dtype: datetime64[ns, pytz.FixedOffset(-1439)]

Those evaluate as unequal, but with check_dtype=False, we can overcome this:

In [4]: tm.assert_series_equal(s1, s2)
...
AssertionError: Attributes of Series are different

Attribute "dtype" are different
[left]:  datetime64[ns, UTC-23:59]
[right]: datetime64[ns, pytz.FixedOffset(-1439)]

In [5]: tm.assert_series_equal(s1, s2, check_dtype=False)

However, for Index objects, there is no check_dtype keyword that works in a similar way:

In [6]: tm.assert_index_equal(pd.Index(s1), pd.Index(s2))
...
~/scipy/pandas/pandas/_testing.py in _check_types(l, r, obj)
    717             # Skip exact dtype checking when `check_categorical` is False
    718             if check_categorical:
--> 719                 assert_attr_equal("dtype", l, r, obj=obj)
    720 
    721             # allow string-like to have different inferred_types

    [... skipping hidden 2 frame]

AssertionError: Index are different

Attribute "dtype" are different
[left]:  datetime64[ns, UTC-23:59]
[right]: datetime64[ns, pytz.FixedOffset(-1439)]

In [8]: tm.assert_index_equal(pd.Index(s1), pd.Index(s2), exact=False)
...
AssertionError: Index are different

Index values are different (0.0 %)
[left]:  DatetimeIndex(['2020-10-20 09:49:37.628339-23:59'], dtype='datetime64[ns, UTC-23:59]', freq=None)
[right]: DatetimeIndex(['2020-10-20 09:49:37.628339-23:59'], dtype='datetime64[ns, pytz.FixedOffset(-1439)]', freq=None)

(and you can see here why check_categorical indirectly can help, as what @jbrockmendel does in #37296

[jbrockmendel]

this is no longer failing on the CI, has it been resolved @jorisvandenbossche ?