googleapis · tswast · Mar 24, 2022 · Jan 27, 2022 · Jan 27, 2022 · Feb 1, 2022
@@ -152,29 +152,35 @@ def min(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
         result = pandas_backports.nanmin(
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
-        return self._box_func(result)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
 
     def max(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
         pandas_backports.numpy_validate_max((), kwargs)
         result = pandas_backports.nanmax(
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
-        return self._box_func(result)
-
-    if pandas_release >= (1, 2):
-
-        def median(
-            self,
-            *,
-            axis: Optional[int] = None,
-            out=None,
-            overwrite_input: bool = False,
-            keepdims: bool = False,
-            skipna: bool = True,
-        ):
-            pandas_backports.numpy_validate_median(
-                (),
-                {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims},
-            )
-            result = pandas_backports.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        if axis is None or self.ndim == 1:
+            return self._box_func(result)
+        return self._from_backing_data(result)
+
+    def median(
+        self,
+        *,
+        axis: Optional[int] = None,
+        out=None,
+        overwrite_input: bool = False,
+        keepdims: bool = False,
+        skipna: bool = True,
+    ):
+        if not hasattr(pandas_backports, "numpy_validate_median"):
+            raise NotImplementedError("Need pandas 1.3 or later to calculate median.")
+
+        pandas_backports.numpy_validate_median(
+            (), {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims},
+        )
+        result = pandas_backports.nanmedian(self._ndarray, axis=axis, skipna=skipna)
+        if axis is None or self.ndim == 1:
             return self._box_func(result)
+        return self._from_backing_data(result)
@@ -106,12 +106,8 @@ def __ge__(self, other):
 # See: https://github.com/pandas-dev/pandas/pull/45544
 @import_default("pandas.core.arrays._mixins", pandas_release < (1, 3))
 class NDArrayBackedExtensionArray(pandas.core.arrays.base.ExtensionArray):
-
-    ndim = 1
-
     def __init__(self, values, dtype):
         assert isinstance(values, numpy.ndarray)
-        assert values.ndim == 1
         self._ndarray = values
         self._dtype = dtype
 

@@ -0,0 +1,35 @@
+# Copyright 2022 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Tests for extension interface compliance, inherited from pandas.
+
+See:
+https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/decimal/test_decimal.py
+and
+https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/test_period.py
+"""
+
+from pandas.tests.extension import base
+import pytest
+
+# NDArrayBacked2DTests suite added in https://github.com/pandas-dev/pandas/pull/44974
+pytest.importorskip("pandas", minversion="1.5.0dev")
+
+
+class Test2DCompat(base.NDArrayBacked2DTests):
+    pass
+
+
+class TestIndex(base.BaseIndexTests):
+    pass
@@ -16,6 +16,7 @@
 import operator
 
 import numpy
+import numpy.testing
 import pandas
 import pandas.testing
 import pytest
@@ -154,6 +155,100 @@ def test_date_parsing_errors(value, error):
         pandas.Series([value], dtype="dbdate")
 
 
+def test_date_max_2d():
+    input_array = db_dtypes.DateArray(
+        numpy.array(
+            [
+                [
+                    numpy.datetime64("1970-01-01"),
+                    numpy.datetime64("1980-02-02"),
+                    numpy.datetime64("1990-03-03"),
+                ],
+                [
+                    numpy.datetime64("1971-02-02"),
+                    numpy.datetime64("1981-03-03"),
+                    numpy.datetime64("1991-04-04"),
+                ],
+                [
+                    numpy.datetime64("1972-03-03"),
+                    numpy.datetime64("1982-04-04"),
+                    numpy.datetime64("1992-05-05"),
+                ],
+            ],
+            dtype="datetime64[ns]",
+        )
+    )
+    numpy.testing.assert_array_equal(
+        input_array.max(axis=0)._ndarray,
+        numpy.array(
+            [
+                numpy.datetime64("1972-03-03"),
+                numpy.datetime64("1982-04-04"),
+                numpy.datetime64("1992-05-05"),
+            ],
+            dtype="datetime64[ns]",
+        ),
+    )
+    numpy.testing.assert_array_equal(
+        input_array.max(axis=1)._ndarray,
+        numpy.array(
+            [
+                numpy.datetime64("1990-03-03"),
+                numpy.datetime64("1991-04-04"),
+                numpy.datetime64("1992-05-05"),
+            ],
+            dtype="datetime64[ns]",
+        ),
+    )
+
+
+def test_date_min_2d():
+    input_array = db_dtypes.DateArray(
+        numpy.array(
+            [
+                [
+                    numpy.datetime64("1970-01-01"),
+                    numpy.datetime64("1980-02-02"),
+                    numpy.datetime64("1990-03-03"),
+                ],
+                [
+                    numpy.datetime64("1971-02-02"),
+                    numpy.datetime64("1981-03-03"),
+                    numpy.datetime64("1991-04-04"),
+                ],
+                [
+                    numpy.datetime64("1972-03-03"),
+                    numpy.datetime64("1982-04-04"),
+                    numpy.datetime64("1992-05-05"),
+                ],
+            ],
+            dtype="datetime64[ns]",
+        )
+    )
+    numpy.testing.assert_array_equal(
+        input_array.min(axis=0)._ndarray,
+        numpy.array(
+            [
+                numpy.datetime64("1970-01-01"),
+                numpy.datetime64("1980-02-02"),
+                numpy.datetime64("1990-03-03"),
+            ],
+            dtype="datetime64[ns]",
+        ),
+    )
+    numpy.testing.assert_array_equal(
+        input_array.min(axis=1)._ndarray,
+        numpy.array(
+            [
+                numpy.datetime64("1970-01-01"),
+                numpy.datetime64("1971-02-02"),
+                numpy.datetime64("1972-03-03"),
+            ],
+            dtype="datetime64[ns]",
+        ),
+    )
+
+
 @pytest.mark.skipif(
     not hasattr(pandas_backports, "numpy_validate_median"),
     reason="median not available with this version of pandas",
@@ -178,3 +273,58 @@ def test_date_parsing_errors(value, error):
 def test_date_median(values, expected):
     series = pandas.Series(values, dtype="dbdate")
     assert series.median() == expected
+
+
+@pytest.mark.skipif(
+    not hasattr(pandas_backports, "numpy_validate_median"),
+    reason="median not available with this version of pandas",
+)
+def test_date_median_2d():
+    input_array = db_dtypes.DateArray(
+        numpy.array(
+            [
+                [
+                    numpy.datetime64("1970-01-01"),
+                    numpy.datetime64("1980-02-02"),
+                    numpy.datetime64("1990-03-03"),
+                ],
+                [
+                    numpy.datetime64("1971-02-02"),
+                    numpy.datetime64("1981-03-03"),
+                    numpy.datetime64("1991-04-04"),
+                ],
+                [
+                    numpy.datetime64("1972-03-03"),
+                    numpy.datetime64("1982-04-04"),
+                    numpy.datetime64("1992-05-05"),
+                ],
+            ],
+            dtype="datetime64[ns]",
+        )
+    )
+    pandas.testing.assert_extension_array_equal(
+        input_array.median(axis=0),
+        db_dtypes.DateArray(
+            numpy.array(
+                [
+                    numpy.datetime64("1971-02-02"),
+                    numpy.datetime64("1981-03-03"),
+                    numpy.datetime64("1991-04-04"),
+                ],
+                dtype="datetime64[ns]",
+            )
+        ),
+    )
+    pandas.testing.assert_extension_array_equal(
+        input_array.median(axis=1),
+        db_dtypes.DateArray(
+            numpy.array(
+                [
+                    numpy.datetime64("1980-02-02"),
+                    numpy.datetime64("1981-03-03"),
+                    numpy.datetime64("1982-04-04"),
+                ],
+                dtype="datetime64[ns]",
+            )
+        ),
+    )