BUG: large datetimes incorrect in read_sas

[jbrockmendel]

There are a few tests in tests.io.sas.test_sas7bdat that need attention from someone with access to SAS.

def test_max_sas_date(datapath):
    # GH 20927
    # NB. max datetime in SAS dataset is 31DEC9999:23:59:59.999
    #    but this is read as 29DEC9999:23:59:59.998993 by a buggy
    #    sas7bdat module

We need to read the file "pandas/tests/io/sas/data/max_sas_date.sas7bdat" with SAS and inspect the first entry in the dt_as_dt column to confirm the expected behavior. It is also unclear what precision the result should have: the comment suggests milliseconds, we get microseconds, and pyreadstat rounds to seconds (xref https://github.com/WizardMac/ReadStat/issues/306)

def test_date_time(datapath):
    [...]
    # GH 19732: Timestamps imported from sas will incur floating point errors

Similarly we need to read the file "pandas/tests/io/sas/data/datetime.sas7bdat" with SAS and inspect the first and last entries in the "DateTimeHi" column to ensure we are getting these right.

[ChadFulton]

Opening max_sas_date.sas7bdat in "SAS 9.4 TS Level 1M6" in Linux yields the following table (in "SAS:VIEWTABLE" in "Browse" mode):

	text	dt_as_float	dt_as_dt	date_as_float	date_as_date
1	max	253717747199.999	31DEC9999:23:59:59.999	2936547	9999-12-31
2	normal	1880323199.999	01AUG2019:23:59:59.999	21762	2019-08-01

[ChadFulton]

And the datetime.sas7bdat shows:

	Date1	Date2	DateTime	DateTimeHi	Taiw
1	1677-09-22	22SEP77	21SEP1677:00:12:44	21SEP1677:00:12:43.145225525	0001/01/01
2	1960-01-01	01JAN60	01JAN1960:00:00:00	01JAN1960:00:00:00.000000000	0049/01/01
3	2016-02-29	29FEB16	29FEB2016:23:59:59	29FEB2016:23:59:59.123456001	0105/02/29
4	2262-04-11	11APR62	11APR2262:23:47:16	11APR2262:23:47:16.854774475	0351/04/11

[jbrockmendel]

Thanks @ChadFulton, i'll update the comments in the file to point back here as the Source Of Truth.

[gtumuluri]

I am also having this error where the read_sas simply fails and there is no other option whatsoever to read and salvage the file / good data.

>>> df = pd.read_sas('my_sas_file.sas7bdat')

Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py", line 83, in _convert_datetimes
    return pd.to_datetime(sas_datetimes, unit=unit, origin="1960-01-01")
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/tools/datetimes.py", line 1068, in to_datetime
    values = convert_listlike(arg._values, format)
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/tools/datetimes.py", line 393, in _convert_listlike_datetimes
    return _to_datetime_with_unit(arg, unit, name, tz, errors)
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/tools/datetimes.py", line 557, in _to_datetime_with_unit
    arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
  File "pandas/_libs/tslib.pyx", line 312, in pandas._libs.tslib.array_with_unit_to_datetime
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: cannot convert input with unit 's'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/util/_decorators.py", line 331, in wrapper
    return func(*args, **kwargs)
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sasreader.py", line 175, in read_sas
    return reader.read()
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py", line 742, in read
    rslt = self._chunk_to_dataframe()
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py", line 792, in _chunk_to_dataframe
    rslt[name] = _convert_datetimes(rslt[name], "s")
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py", line 85, in _convert_datetimes
    s_series = sas_datetimes.apply(_parse_datetime, unit=unit)
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/series.py", line 4771, in apply
    return SeriesApply(self, func, convert_dtype, args, kwargs).apply()
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py", line 1123, in apply
    return self.apply_standard()
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py", line 1174, in apply_standard
    mapped = lib.map_infer(
  File "pandas/_libs/lib.pyx", line 2924, in pandas._libs.lib.map_infer
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/core/apply.py", line 142, in f
    return func(x, *args, **kwargs)
  File "/opt/anaconda3/lib/python3.10/site-packages/pandas/io/sas/sas7bdat.py", line 55, in _parse_datetime
    return datetime(1960, 1, 1) + timedelta(seconds=sas_datetime)
OverflowError: days=-1176508800; must have magnitude <= 999999999

Reading the same file in R using the haven package works just fine and actually shows the bad timestamps clearly.

> library(haven)
> df <- read_sas('my_sas_file.sas7bdat')
> summary(df$DtObgnOrig)
                          Min.                        1st Qu. 
"-3219212-04-24 00:00:00.0000"     "2004-01-27 00:00:00.0000" 
                        Median                           Mean 
    "2008-08-11 00:00:00.0000"     "2009-03-08 10:15:16.7828" 
                       3rd Qu.                           Max. 
    "2014-12-09 00:00:00.0000"     "2027-06-12 00:00:00.0000" 
                          NA's 
                    "93215826"