BUG: during interchanging from non-pandas tz-aware data

doc/source/whatsnew/v2.1.0.rst

@@ -682,6 +682,7 @@ Other
 - Bug in :class:`DataFrame` and :class:`Series` raising for data of complex dtype when ``NaN`` values are present (:issue:`53627`)
 - Bug in :class:`DatetimeIndex` where ``repr`` of index passed with time does not print time is midnight and non-day based freq(:issue:`53470`)
 - Bug in :class:`FloatingArray.__contains__` with ``NaN`` item incorrectly returning ``False`` when ``NaN`` values are present (:issue:`52840`)
+- Bug in :func:`api.interchange.from_dataframe` was raising during interchanging from non-pandas tz-aware data containing ``NaN`` values (:issue:`54287`)
 - Bug in :func:`api.interchange.from_dataframe` when converting an empty DataFrame object (:issue:`53155`)
 - Bug in :func:`assert_almost_equal` now throwing assertion error for two unequal sets (:issue:`51727`)
 - Bug in :func:`assert_frame_equal` checks category dtypes even when asked not to check index type (:issue:`52126`)

pandas/core/interchange/from_dataframe.py

@@ -7,6 +7,7 @@
 import numpy as np
 
 from pandas.compat._optional import import_optional_dependency
+from pandas.errors import SettingWithCopyError
 
 import pandas as pd
 from pandas.core.interchange.dataframe_protocol import (
@@ -513,5 +514,10 @@ def set_nulls(
             # cast the `data` to nullable float dtype.
             data = data.astype(float)
             data[null_pos] = None
+        except SettingWithCopyError:
+            # SettingWithCopyError happens if the `data` appears
+            # to have 'NaT'. If this happens, copy the `data`.
+            data = data.copy()
+            data[null_pos] = None
 
     return data

pandas/tests/interchange/test_impl.py

@@ -295,3 +295,22 @@ def test_datetimetzdtype(tz, unit):
     )
     df = pd.DataFrame({"ts_tz": tz_data})
     tm.assert_frame_equal(df, from_dataframe(df.__dataframe__()))
+
+
+def test_interchange_from_non_pandas_tz_aware():
+    # GH 54239, 54287
+    pa = pytest.importorskip("pyarrow")
+    import pyarrow.compute as pc
+
+    arr = pa.array([datetime(2020, 1, 1), None, datetime(2020, 1, 2)])
+    arr = pc.assume_timezone(arr, "Asia/Kathmandu")
+    table = pa.table({"arr": arr})
+    exchange_df = table.__dataframe__()
+    result = from_dataframe(exchange_df)
+
+    expected = pd.DataFrame(
+        ["2020-01-01 00:00:00+05:45", "NaT", "2020-01-02 00:00:00+05:45"],
+        columns=["arr"],
+        dtype="datetime64[us, Asia/Kathmandu]",
+    )
+    tm.assert_frame_equal(expected, result)