Issue 20927 fix resolves read_sas error for dates/datetimes greater than 2262-04-11

doc/source/whatsnew/v1.1.0.rst

@@ -781,6 +781,7 @@ I/O
   timestamps with ``version="2.0"`` (:issue:`31652`).
 - Bug in :meth:`read_csv` was raising `TypeError` when `sep=None` was used in combination with `comment` keyword (:issue:`31396`)
 - Bug in :class:`HDFStore` that caused it to set to ``int64`` the dtype of a ``datetime64`` column when reading a DataFrame in Python 3 from fixed format written in Python 2 (:issue:`31750`)
+- :func:`read_sas()` now handles dates and datetimes larger than :attr:`Timestamp.max` returning them as :class:`datetime.datetime` objects (:issue:`20927`)
 - Bug in :meth:`DataFrame.to_json` where ``Timedelta`` objects would not be serialized correctly with ``date_format="iso"`` (:issue:`28256`)
 - :func:`read_csv` will raise a ``ValueError`` when the column names passed in `parse_dates` are missing in the Dataframe (:issue:`31251`)
 - Bug in :meth:`read_excel` where a UTF-8 string with a high surrogate would cause a segmentation violation (:issue:`23809`)

pandas/io/sas/sas7bdat.py

@@ -14,12 +14,12 @@
   http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/CUJ/1992/9210/ross/ross.htm
 """
 from collections import abc
-from datetime import datetime
+from datetime import datetime, timedelta
 import struct
 
 import numpy as np
 
-from pandas.errors import EmptyDataError
+from pandas.errors import EmptyDataError, OutOfBoundsDatetime
 
 import pandas as pd
 
@@ -29,6 +29,39 @@
 from pandas.io.sas.sasreader import ReaderBase
 
 
+def _convert_datetimes(sas_datetimes: pd.Series, unit: str) -> pd.Series:
+    """
+    Convert to Timestamp if possible, otherwise to datetime.datetime.
+    SAS float64 lacks precision for more than ms resolution so the fit
+    to datetime.datetime is ok.
+
+    Parameters
+    ----------
+    sas_datetimes : {Series, Sequence[float]}
+       Dates or datetimes in SAS
+    unit : {str}
+       "d" if the floats represent dates, "s" for datetimes
+
+    Returns
+    -------
+    Series
+       Series of datetime64 dtype or datetime.datetime.
+    """
+    try:
+        return pd.to_datetime(sas_datetimes, unit=unit, origin="1960-01-01")
+    except OutOfBoundsDatetime:
+        if unit == "s":
+            return sas_datetimes.apply(
+                lambda sas_float: datetime(1960, 1, 1) + timedelta(seconds=sas_float)
+            )
+        elif unit == "d":
+            return sas_datetimes.apply(
+                lambda sas_float: datetime(1960, 1, 1) + timedelta(days=sas_float)
+            )
+        else:
+            raise ValueError("unit must be 'd' or 's'")
+
+
 class _subheader_pointer:
     pass
 
@@ -706,15 +739,10 @@ def _chunk_to_dataframe(self):
                 rslt[name] = self._byte_chunk[jb, :].view(dtype=self.byte_order + "d")
                 rslt[name] = np.asarray(rslt[name], dtype=np.float64)
                 if self.convert_dates:
-                    unit = None
                     if self.column_formats[j] in const.sas_date_formats:
-                        unit = "d"
+                        rslt[name] = _convert_datetimes(rslt[name], "d")
                     elif self.column_formats[j] in const.sas_datetime_formats:
-                        unit = "s"
-                    if unit:
-                        rslt[name] = pd.to_datetime(
-                            rslt[name], unit=unit, origin="1960-01-01"
-                        )
+                        rslt[name] = _convert_datetimes(rslt[name], "s")
                 jb += 1
             elif self._column_types[j] == b"s":
                 rslt[name] = self._string_chunk[js, :]

pandas/tests/io/sas/test_sas7bdat.py

@@ -3,6 +3,7 @@
 import os
 from pathlib import Path
 
+import dateutil.parser
 import numpy as np
 import pytest
 
@@ -214,3 +215,94 @@ def test_zero_variables(datapath):
     fname = datapath("io", "sas", "data", "zero_variables.sas7bdat")
     with pytest.raises(EmptyDataError):
         pd.read_sas(fname)
+
+
+def round_datetime_to_ms(ts):
+    if isinstance(ts, datetime):
+        return ts.replace(microsecond=int(round(ts.microsecond, -3) / 1000) * 1000)
+    elif isinstance(ts, str):
+        _ts = dateutil.parser.parse(timestr=ts)
+        return _ts.replace(microsecond=int(round(_ts.microsecond, -3) / 1000) * 1000)
+    else:
+        return ts
+
+
+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
+    fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")
+    df = pd.read_sas(fname, encoding="iso-8859-1")
+
+    # SAS likes to left pad strings with spaces - lstrip before comparing
+    df = df.applymap(lambda x: x.lstrip() if isinstance(x, str) else x)
+    # GH 19732: Timestamps imported from sas will incur floating point errors
+    try:
+        df["dt_as_dt"] = df["dt_as_dt"].dt.round("us")
+    except pd._libs.tslibs.np_datetime.OutOfBoundsDatetime:
+        df = df.applymap(round_datetime_to_ms)
+    except AttributeError:
+        df["dt_as_dt"] = df["dt_as_dt"].apply(round_datetime_to_ms)
+    # if there are any date/times > pandas.Timestamp.max then ALL in that chunk
+    # are returned as datetime.datetime
+    expected = pd.DataFrame(
+        {
+            "text": ["max", "normal"],
+            "dt_as_float": [253717747199.999, 1880323199.999],
+            "dt_as_dt": [
+                datetime(9999, 12, 29, 23, 59, 59, 999000),
+                datetime(2019, 8, 1, 23, 59, 59, 999000),
+            ],
+            "date_as_float": [2936547.0, 21762.0],
+            "date_as_date": [datetime(9999, 12, 29), datetime(2019, 8, 1)],
+        },
+        columns=["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"],
+    )
+    tm.assert_frame_equal(df, expected)
+
+
+def test_max_sas_date_iterator(datapath):
+    # GH 20927
+    # when called as an iterator, only those chunks with a date > pd.Timestamp.max
+    # are returned as datetime.datetime, if this happens that whole chunk is returned
+    # as datetime.datetime
+    col_order = ["text", "dt_as_float", "dt_as_dt", "date_as_float", "date_as_date"]
+    fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")
+    results = []
+    for df in pd.read_sas(fname, encoding="iso-8859-1", chunksize=1):
+        # SAS likes to left pad strings with spaces - lstrip before comparing
+        df = df.applymap(lambda x: x.lstrip() if isinstance(x, str) else x)
+        # GH 19732: Timestamps imported from sas will incur floating point errors
+        try:
+            df["dt_as_dt"] = df["dt_as_dt"].dt.round("us")
+        except pd._libs.tslibs.np_datetime.OutOfBoundsDatetime:
+            df = df.applymap(round_datetime_to_ms)
+        except AttributeError:
+            df["dt_as_dt"] = df["dt_as_dt"].apply(round_datetime_to_ms)
+        df.reset_index(inplace=True, drop=True)
+        results.append(df)
+    expected = [
+        pd.DataFrame(
+            {
+                "text": ["max"],
+                "dt_as_float": [253717747199.999],
+                "dt_as_dt": [datetime(9999, 12, 29, 23, 59, 59, 999000)],
+                "date_as_float": [2936547.0],
+                "date_as_date": [datetime(9999, 12, 29)],
+            },
+            columns=col_order,
+        ),
+        pd.DataFrame(
+            {
+                "text": ["normal"],
+                "dt_as_float": [1880323199.999],
+                "dt_as_dt": [np.datetime64("2019-08-01 23:59:59.999")],
+                "date_as_float": [21762.0],
+                "date_as_date": [np.datetime64("2019-08-01")],
+            },
+            columns=col_order,
+        ),
+    ]
+    for result, expected in zip(results, expected):
+        tm.assert_frame_equal(result, expected)