Parquet: Reading timezones leads to incorrect datetimes

[ritchie46]

If we create a pandas dataframe in the 2022 ranges and then read that with arrow2 we are back in the 70's 🕺.

df = pd.DataFrame(data = {"Timestamp": pd.date_range("2022-01-01T00:00+00:00", "2022-01-01T10:00+00:00", freq="H")})

f = io.BytesIO()
df.to_parquet(f)
f.seek(0)
print(pl.read_parquet(f))

shape: (11, 1)
┌─────────────────────────┐
│ Timestamp               │
│ ---                     │
│ datetime[ns]            │
╞═════════════════════════╡
│ 1970-01-19 23:49:55.200 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:49:58.800 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:02.400 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:06     │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ...                     │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:20.400 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:24     │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:27.600 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1970-01-19 23:50:31.200 │
└─────────────────────────┘
Conversion of timezone aware to naive datetimes. TZ information may be lost.

I put a dbg! around the arrow chunks we get to ensure I did not make a conversion error in polars:

[
    Timestamp(Nanosecond, Some("UTC"))[1970-01-19 23:49:55.200 +00:00, 1970-01-19 23:49:58.800 +00:00, 1970-01-19 23:50:02.400 +00:00, 1970-01-19 23:50:06 +00:00, 1970-01-19 23:50:09.600 +00:00, 1970-01-19 23:50:13.200 +00:00, 1970-01-19 23:50:16.800 +00:00, 1970-01-19 23:50:20.400 +00:00, 1970-01-19 23:50:24 +00:00, 1970-01-19 23:50:27.600 +00:00, 1970-01-19 23:50:31.200 +00:00],
]