Manage timedelta, datetimetz and date

Author: droussea2001

pandas/_libs/lib.pyx

@@ -96,6 +96,9 @@ from pandas._libs.missing cimport (
     is_null_datetime64,
     is_null_timedelta64,
 )
+from pandas._libs.tslibs.timestamps import Timestamp
+from pandas._libs.tslibs.timedeltas import Timedelta
+
 from pandas._libs.tslibs.conversion cimport convert_to_tsobject
 from pandas._libs.tslibs.nattype cimport (
     NPY_NAT,
@@ -2615,7 +2618,10 @@ def maybe_convert_objects(ndarray[object] objects,
             else:
                 seen.object_ = True
                 break
-        elif PyDate_Check(val):
+        elif (
+            PyDate_Check(val)
+            or (pa is not None and isinstance(val, (pa.Date32Scalar, pa.Date64Scalar)))
+        ):
             if convert_non_numeric:
                 seen.date_ = True
                 break
@@ -2668,12 +2674,16 @@ def maybe_convert_objects(ndarray[object] objects,
         if storage == "pyarrow":
             from pandas.core.dtypes.dtypes import ArrowDtype
 
+            datetime64_array = None
             if isinstance(val, datetime):
                 objects[mask] = None
+                datetime64_array = objects.astype(Timestamp)
             else:
                 objects[mask] = np.datetime64("NaT")
-            datetime64_array = objects.astype(val.dtype)
-            pa_array = pa.array(datetime64_array)
+                datetime64_array = objects.astype(val.dtype)
+            pa_array = pa.array(datetime64_array).cast(
+                pa.timestamp(val.resolution.unit, val.tzinfo)
+            )
             dtype = ArrowDtype(pa_array.type)
             return dtype.construct_array_type()._from_sequence(pa_array, dtype=dtype)
 
@@ -2727,17 +2737,32 @@ def maybe_convert_objects(ndarray[object] objects,
             return dtype.construct_array_type()._from_sequence(pa_array, dtype=dtype)
 
     elif seen.timedelta_:
-        if is_timedelta_or_timedelta64_array(objects):
-            from pandas import TimedeltaIndex
+        if storage == "pyarrow":
+            from pandas.core.dtypes.dtypes import ArrowDtype
 
-            try:
-                tdi = TimedeltaIndex(objects)
-            except OutOfBoundsTimedelta:
-                pass
+            timedelta64_array = None
+            if isinstance(val, timedelta):
+                objects[mask] = None
+                timedelta64_array = objects.astype(Timedelta)
             else:
-                # unbox to ndarray[timedelta64[ns]]
-                return tdi._data._ndarray
-        seen.object_ = True
+                objects[mask] = np.timedelta64("NaT")
+                timedelta64_array = objects.astype(val.dtype)
+            pa_array = pa.array(timedelta64_array)
+
+            dtype = ArrowDtype(pa_array.type)
+            return dtype.construct_array_type()._from_sequence(pa_array, dtype=dtype)
+        else:
+            if is_timedelta_or_timedelta64_array(objects):
+                from pandas import TimedeltaIndex
+
+                try:
+                    tdi = TimedeltaIndex(objects)
+                except OutOfBoundsTimedelta:
+                    pass
+                else:
+                    # unbox to ndarray[timedelta64[ns]]
+                    return tdi._data._ndarray
+            seen.object_ = True
 
     elif seen.period_:
         if is_period_array(objects):

pandas/core/algorithms.py

@@ -1703,13 +1703,11 @@ def map_array(
         arr = cast("ExtensionArray", arr)
         arr_dtype = arr.dtype.__repr__()
         if "pyarrow" in arr_dtype:
-            if any(time_type in arr_dtype for time_type in ["duration"]):
-                values = arr.astype(object, copy=False)
+            storage = "pyarrow"
+            if "date" in arr_dtype:
+                values = np.fromiter(arr._pa_array, dtype='O')
             else:
                 values = np.asarray(arr)
-            storage = "pyarrow"
-        else:
-            values = np.asarray(arr)
         if arr._hasna:
             na_value = arr.dtype.na_value
     else: