BUG: to_datetime with unit with Int64

doc/source/whatsnew/v1.0.0.rst

@@ -796,6 +796,7 @@ Datetimelike
 - Bug in :meth:`DataFrame.append` would remove the timezone-awareness of new data (:issue:`30238`)
 - Bug in :meth:`Series.cummin` and :meth:`Series.cummax` with timezone-aware dtype incorrectly dropping its timezone (:issue:`15553`)
 - Bug in :class:`DatetimeArray`, :class:`TimedeltaArray`, and :class:`PeriodArray` where inplace addition and subtraction did not actually operate inplace (:issue:`24115`)
+- Bug in :func:`pandas.to_datetime` when called with ``Series`` storing ``IntegerArray`` raising ``TypeError`` instead of returning ``Series`` (:issue:`30050`)
 
 Timedelta
 ^^^^^^^^^

pandas/_libs/tslib.pyx

@@ -296,17 +296,34 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None,
     return result
 
 
-def array_with_unit_to_datetime(ndarray values, object unit,
+def array_with_unit_to_datetime(ndarray values, ndarray mask, object unit,
                                 str errors='coerce'):
     """
-    convert the ndarray according to the unit
+    Convert the ndarray to datetime according to the time unit.
+
+    This function converts an array of objects into a numpy array of
+    datetime64[ns]. It returns the converted array
+    and also returns the timezone offset
+
     if errors:
       - raise: return converted values or raise OutOfBoundsDatetime
           if out of range on the conversion or
           ValueError for other conversions (e.g. a string)
       - ignore: return non-convertible values as the same unit
       - coerce: NaT for non-convertibles
 
+    Parameters
+    ----------
+    values : ndarray of object
+         Date-like objects to convert
+    mask : ndarray of bool
+         Not-a-time mask for non-nullable integer types conversion,
+         can be None
+    unit : object
+         Time unit to use during conversion
+    errors : str, default 'raise'
+         Error behavior when parsing
+
     Returns
     -------
     result : ndarray of m8 values
@@ -316,7 +333,6 @@ def array_with_unit_to_datetime(ndarray values, object unit,
         Py_ssize_t i, j, n=len(values)
         int64_t m
         ndarray[float64_t] fvalues
-        ndarray mask
         bint is_ignore = errors=='ignore'
         bint is_coerce = errors=='coerce'
         bint is_raise = errors=='raise'
@@ -329,9 +345,13 @@ def array_with_unit_to_datetime(ndarray values, object unit,
 
     if unit == 'ns':
         if issubclass(values.dtype.type, np.integer):
-            return values.astype('M8[ns]'), tz
-        # This will return a tz
-        return array_to_datetime(values.astype(object), errors=errors)
+            result = values.astype('M8[ns]')
+        else:
+            result, tz = array_to_datetime(values.astype(object), errors=errors)
+        if mask is not None:
+            iresult = result.view('i8')
+            iresult[mask] = NPY_NAT
+        return result, tz
 
     m = cast_from_unit(None, unit)
 
@@ -343,7 +363,9 @@ def array_with_unit_to_datetime(ndarray values, object unit,
         if values.dtype.kind == "i":
             # Note: this condition makes the casting="same_kind" redundant
             iresult = values.astype('i8', casting='same_kind', copy=False)
-            mask = iresult == NPY_NAT
+            # If no mask, fill mask by comparing to NPY_NAT constant
+            if mask is None:
+                mask = iresult == NPY_NAT
             iresult[mask] = 0
             fvalues = iresult.astype('f8') * m
             need_to_iterate = False

pandas/core/tools/datetimes.py

@@ -38,6 +38,7 @@
 )
 from pandas.core.dtypes.missing import notna
 
+from pandas.arrays import IntegerArray
 from pandas.core import algorithms
 from pandas.core.algorithms import unique
 
@@ -316,8 +317,21 @@ def _convert_listlike_datetimes(
     elif unit is not None:
         if format is not None:
             raise ValueError("cannot specify both format and unit")
-        arg = getattr(arg, "values", arg)
-        result, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
+        arg = getattr(arg, "_values", arg)
+
+        # GH 30050 pass an ndarray to tslib.array_with_unit_to_datetime
+        # because it expects an ndarray argument
+        if isinstance(arg, IntegerArray):
+            # Explicitly pass NaT mask to array_with_unit_to_datetime
+            mask = arg.isna()
+            arg = arg._ndarray_values
+        else:
+            mask = None
+
+        result, tz_parsed = tslib.array_with_unit_to_datetime(
+            arg, mask, unit, errors=errors
+        )
+
         if errors == "ignore":
             from pandas import Index
 

pandas/tests/indexes/datetimes/test_tools.py

@@ -2291,3 +2291,25 @@ def test_should_cache_errors(unique_share, check_count, err_message):
 
     with pytest.raises(AssertionError, match=err_message):
         tools.should_cache(arg, unique_share, check_count)
+
+
+def test_nullable_integer_to_datetime():
+    # Test for #30050
+    ser = pd.Series([1, 2, None, 2 ** 61, None])
+    ser = ser.astype("Int64")
+    ser_copy = ser.copy()
+
+    res = pd.to_datetime(ser, unit="ns")
+
+    expected = pd.Series(
+        [
+            np.datetime64("1970-01-01 00:00:00.000000001"),
+            np.datetime64("1970-01-01 00:00:00.000000002"),
+            np.datetime64("NaT"),
+            np.datetime64("2043-01-25 23:56:49.213693952"),
+            np.datetime64("NaT"),
+        ]
+    )
+    tm.assert_series_equal(res, expected)
+    # Check that ser isn't mutated
+    tm.assert_series_equal(ser, ser_copy)