CLN: incorporate final comments

Author: AlexKirko

pandas/_libs/tslib.pyx

@@ -296,8 +296,8 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None,
     return result
 
 
-def array_with_unit_to_datetime(ndarray values, object unit,
-                                str errors='coerce', ndarray mask=None):
+def array_with_unit_to_datetime(ndarray values, ndarray mask, object unit,
+                                str errors='coerce'):
     """
     Convert the ndarray to datetime according to the time unit.
 
@@ -316,10 +316,10 @@ def array_with_unit_to_datetime(ndarray values, object unit,
     ----------
     values : ndarray of object
          Date-like objects to convert
-    unit : object
-         Time unit to use during conversion
     mask : ndarray of bool, default None
          Not-a-time mask for non-nullable integer types conversion
+    unit : object
+         Time unit to use during conversion
     errors : str, default 'raise'
          Error behavior when parsing
 

pandas/core/tools/datetimes.py

@@ -318,19 +318,20 @@ def _convert_listlike_datetimes(
         if format is not None:
             raise ValueError("cannot specify both format and unit")
         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_na = arg.isna()
+            mask = arg.isna()
             arg = getattr(arg, "_ndarray_values", arg)
-            result, tz_parsed = tslib.array_with_unit_to_datetime(
-                arg, unit, mask=mask_na, errors=errors
-            )
         else:
-            result, tz_parsed = tslib.array_with_unit_to_datetime(
-                arg, unit, errors=errors
-            )
+            mask = None
+
+        result, tz_parsed = tslib.array_with_unit_to_datetime(
+            arg, mask, unit, errors=errors
+        )
+
         if errors == "ignore":
             from pandas import Index