Fix styling (#40221)

Author: thomasyu888

pandas/tests/arrays/categorical/test_missing.py

@@ -154,14 +154,14 @@ def test_use_inf_as_na_outside_context(self, values, expected):
         cat = Categorical(values)
 
         with pd.option_context("mode.use_inf_as_na", True):
-            result = pd.isna(cat)
+            result = isna(cat)
             tm.assert_numpy_array_equal(result, expected)
 
-            result = pd.isna(Series(cat))
+            result = isna(Series(cat))
             expected = Series(expected)
             tm.assert_series_equal(result, expected)
 
-            result = pd.isna(DataFrame(cat))
+            result = isna(DataFrame(cat))
             expected = DataFrame(expected)
             tm.assert_frame_equal(result, expected)
 

pandas/tests/arrays/datetimes/test_reductions.py

@@ -40,33 +40,33 @@ def test_min_max(self, arr1d):
         assert result == expected
 
         result = arr.min(skipna=False)
-        assert result is pd.NaT
+        assert result is NaT
 
         result = arr.max(skipna=False)
-        assert result is pd.NaT
+        assert result is NaT
 
     @pytest.mark.parametrize("tz", [None, "US/Central"])
     @pytest.mark.parametrize("skipna", [True, False])
     def test_min_max_empty(self, skipna, tz):
         dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence([], dtype=dtype)
         result = arr.min(skipna=skipna)
-        assert result is pd.NaT
+        assert result is NaT
 
         result = arr.max(skipna=skipna)
-        assert result is pd.NaT
+        assert result is NaT
 
     @pytest.mark.parametrize("tz", [None, "US/Central"])
     @pytest.mark.parametrize("skipna", [True, False])
     def test_median_empty(self, skipna, tz):
         dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence([], dtype=dtype)
         result = arr.median(skipna=skipna)
-        assert result is pd.NaT
+        assert result is NaT
 
         arr = arr.reshape(0, 3)
         result = arr.median(axis=0, skipna=skipna)
-        expected = type(arr)._from_sequence([pd.NaT, pd.NaT, pd.NaT], dtype=arr.dtype)
+        expected = type(arr)._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
         tm.assert_equal(result, expected)
 
         result = arr.median(axis=1, skipna=skipna)
@@ -79,7 +79,7 @@ def test_median(self, arr1d):
         result = arr.median()
         assert result == arr[0]
         result = arr.median(skipna=False)
-        assert result is pd.NaT
+        assert result is NaT
 
         result = arr.dropna().median(skipna=False)
         assert result == arr[0]
@@ -90,7 +90,7 @@ def test_median(self, arr1d):
     def test_median_axis(self, arr1d):
         arr = arr1d
         assert arr.median(axis=0) == arr.median()
-        assert arr.median(axis=0, skipna=False) is pd.NaT
+        assert arr.median(axis=0, skipna=False) is NaT
 
         msg = r"abs\(axis\) must be less than ndim"
         with pytest.raises(ValueError, match=msg):
@@ -102,7 +102,7 @@ def test_median_2d(self, arr1d):
 
         # axis = None
         assert arr.median() == arr1d.median()
-        assert arr.median(skipna=False) is pd.NaT
+        assert arr.median(skipna=False) is NaT
 
         # axis = 0
         result = arr.median(axis=0)
@@ -120,7 +120,7 @@ def test_median_2d(self, arr1d):
         tm.assert_equal(result, expected)
 
         result = arr.median(axis=1, skipna=False)
-        expected = type(arr)._from_sequence([pd.NaT], dtype=arr.dtype)
+        expected = type(arr)._from_sequence([NaT], dtype=arr.dtype)
         tm.assert_equal(result, expected)
 
     def test_mean(self, arr1d):
@@ -132,7 +132,7 @@ def test_mean(self, arr1d):
         result = arr.mean()
         assert result == expected
         result = arr.mean(skipna=False)
-        assert result is pd.NaT
+        assert result is NaT
 
         result = arr.dropna().mean(skipna=False)
         assert result == expected

pandas/tests/arrays/interval/test_interval.py

@@ -90,7 +90,7 @@ def test_shift(self):
         tm.assert_interval_array_equal(result, expected)
 
     def test_shift_datetime(self):
-        a = IntervalArray.from_breaks(pd.date_range("2000", periods=4))
+        a = IntervalArray.from_breaks(date_range("2000", periods=4))
         result = a.shift(2)
         expected = a.take([-1, -1, 0], allow_fill=True)
         tm.assert_interval_array_equal(result, expected)
@@ -248,7 +248,7 @@ def test_arrow_array_missing():
 @pyarrow_skip
 @pytest.mark.parametrize(
     "breaks",
-    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    [[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
     ids=["float", "datetime64[ns]"],
 )
 def test_arrow_table_roundtrip(breaks):
@@ -275,7 +275,7 @@ def test_arrow_table_roundtrip(breaks):
 @pyarrow_skip
 @pytest.mark.parametrize(
     "breaks",
-    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    [[0.0, 1.0, 2.0, 3.0], date_range("2017", periods=4, freq="D")],
     ids=["float", "datetime64[ns]"],
 )
 def test_arrow_table_roundtrip_without_metadata(breaks):

pandas/tests/arrays/sparse/test_array.py

@@ -185,8 +185,8 @@ def test_constructor_spindex_dtype_scalar_broadcasts(self):
     def test_constructor_inferred_fill_value(self, data, fill_value):
         result = SparseArray(data).fill_value
 
-        if pd.isna(fill_value):
-            assert pd.isna(result)
+        if isna(fill_value):
+            assert isna(result)
         else:
             assert result == fill_value
 

pandas/tests/arrays/test_datetimelike.py

@@ -184,13 +184,13 @@ def test_take_fill(self):
         arr = self.array_cls(data, freq="D")
 
         result = arr.take([-1, 1], allow_fill=True, fill_value=None)
-        assert result[0] is pd.NaT
+        assert result[0] is NaT
 
         result = arr.take([-1, 1], allow_fill=True, fill_value=np.nan)
-        assert result[0] is pd.NaT
+        assert result[0] is NaT
 
-        result = arr.take([-1, 1], allow_fill=True, fill_value=pd.NaT)
-        assert result[0] is pd.NaT
+        result = arr.take([-1, 1], allow_fill=True, fill_value=NaT)
+        assert result[0] is NaT
 
     def test_take_fill_str(self, arr1d):
         # Cast str fill_value matching other fill_value-taking methods
@@ -205,7 +205,7 @@ def test_take_fill_str(self, arr1d):
     def test_concat_same_type(self, arr1d):
         arr = arr1d
         idx = self.index_cls(arr)
-        idx = idx.insert(0, pd.NaT)
+        idx = idx.insert(0, NaT)
         arr = self.array_cls(idx)
 
         result = arr._concat_same_type([arr[:-1], arr[1:], arr])
@@ -221,7 +221,7 @@ def test_unbox_scalar(self):
         expected = arr._data.dtype.type
         assert isinstance(result, expected)
 
-        result = arr._unbox_scalar(pd.NaT)
+        result = arr._unbox_scalar(NaT)
         assert isinstance(result, expected)
 
         msg = f"'value' should be a {self.dtype.__name__}."
@@ -234,7 +234,7 @@ def test_check_compatible_with(self):
 
         arr._check_compatible_with(arr[0])
         arr._check_compatible_with(arr[:1])
-        arr._check_compatible_with(pd.NaT)
+        arr._check_compatible_with(NaT)
 
     def test_scalar_from_string(self):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
@@ -254,15 +254,15 @@ def test_reduce_invalid(self):
     def test_fillna_method_doesnt_change_orig(self, method):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
         arr = self.array_cls(data, freq="D")
-        arr[4] = pd.NaT
+        arr[4] = NaT
 
         fill_value = arr[3] if method == "pad" else arr[5]
 
         result = arr.fillna(method=method)
         assert result[4] == fill_value
 
         # check that the original was not changed
-        assert arr[4] is pd.NaT
+        assert arr[4] is NaT
 
     def test_searchsorted(self):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
@@ -286,7 +286,7 @@ def test_searchsorted(self):
 
         # GH#29884 match numpy convention on whether NaT goes
         #  at the end or the beginning
-        result = arr.searchsorted(pd.NaT)
+        result = arr.searchsorted(NaT)
         if np_version_under1p18:
             # Following numpy convention, NaT goes at the beginning
             #  (unlike NaN which goes at the end)
@@ -616,7 +616,7 @@ def test_median(self, arr1d):
 
 
 class TestDatetimeArray(SharedTests):
-    index_cls = pd.DatetimeIndex
+    index_cls = DatetimeIndex
     array_cls = DatetimeArray
     dtype = Timestamp
 
@@ -749,7 +749,7 @@ def test_from_dti(self, arr1d):
 
         # Check that Index.__new__ knows what to do with DatetimeArray
         dti2 = pd.Index(arr)
-        assert isinstance(dti2, pd.DatetimeIndex)
+        assert isinstance(dti2, DatetimeIndex)
         assert list(dti2) == list(arr)
 
     def test_astype_object(self, arr1d):
@@ -800,7 +800,7 @@ def test_to_period_2d(self, arr1d):
             expected = arr1d.to_period("D").reshape(1, -1)
         tm.assert_period_array_equal(result, expected)
 
-    @pytest.mark.parametrize("propname", pd.DatetimeIndex._bool_ops)
+    @pytest.mark.parametrize("propname", DatetimeIndex._bool_ops)
     def test_bool_properties(self, arr1d, propname):
         # in this case _bool_ops is just `is_leap_year`
         dti = self.index_cls(arr1d)
@@ -812,7 +812,7 @@ def test_bool_properties(self, arr1d, propname):
 
         tm.assert_numpy_array_equal(result, expected)
 
-    @pytest.mark.parametrize("propname", pd.DatetimeIndex._field_ops)
+    @pytest.mark.parametrize("propname", DatetimeIndex._field_ops)
     def test_int_properties(self, arr1d, propname):
         if propname in ["week", "weekofyear"]:
             # GH#33595 Deprecate week and weekofyear
@@ -849,7 +849,7 @@ def test_take_fill_valid(self, arr1d):
             # Timestamp with mismatched tz-awareness
             arr.take([-1, 1], allow_fill=True, fill_value=now)
 
-        value = pd.NaT.value
+        value = NaT.value
         msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
         with pytest.raises(TypeError, match=msg):
             # require NaT, not iNaT, as it could be confused with an integer
@@ -908,7 +908,7 @@ def test_strftime(self, arr1d):
 
     def test_strftime_nat(self):
         # GH 29578
-        arr = DatetimeArray(DatetimeIndex(["2019-01-01", pd.NaT]))
+        arr = DatetimeArray(DatetimeIndex(["2019-01-01", NaT]))
 
         result = arr.strftime("%Y-%m-%d")
         expected = np.array(["2019-01-01", np.nan], dtype=object)
@@ -1064,7 +1064,7 @@ def test_astype_object(self, arr1d):
     def test_take_fill_valid(self, arr1d):
         arr = arr1d
 
-        value = pd.NaT.value
+        value = NaT.value
         msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got"
         with pytest.raises(TypeError, match=msg):
             # require NaT, not iNaT, as it could be confused with an integer
@@ -1152,7 +1152,7 @@ def test_strftime(self, arr1d):
 
     def test_strftime_nat(self):
         # GH 29578
-        arr = PeriodArray(PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]"))
+        arr = PeriodArray(PeriodIndex(["2019-01-01", NaT], dtype="period[D]"))
 
         result = arr.strftime("%Y-%m-%d")
         expected = np.array(["2019-01-01", np.nan], dtype=object)
@@ -1164,18 +1164,18 @@ def test_strftime_nat(self):
     [
         (
             TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
-            (pd.NaT, np.timedelta64("NaT", "ns")),
+            (NaT, np.timedelta64("NaT", "ns")),
         ),
         (
             pd.date_range("2000-01-01", periods=3, freq="D")._data,
-            (pd.NaT, np.datetime64("NaT", "ns")),
+            (NaT, np.datetime64("NaT", "ns")),
         ),
-        (pd.period_range("2000-01-01", periods=3, freq="D")._data, (pd.NaT,)),
+        (pd.period_range("2000-01-01", periods=3, freq="D")._data, (NaT,)),
     ],
     ids=lambda x: type(x).__name__,
 )
 def test_casting_nat_setitem_array(array, casting_nats):
-    expected = type(array)._from_sequence([pd.NaT, array[1], array[2]])
+    expected = type(array)._from_sequence([NaT, array[1], array[2]])
 
     for nat in casting_nats:
         arr = array.copy()
@@ -1188,15 +1188,15 @@ def test_casting_nat_setitem_array(array, casting_nats):
     [
         (
             TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
-            (np.datetime64("NaT", "ns"), pd.NaT.value),
+            (np.datetime64("NaT", "ns"), NaT.value),
         ),
         (
             pd.date_range("2000-01-01", periods=3, freq="D")._data,
-            (np.timedelta64("NaT", "ns"), pd.NaT.value),
+            (np.timedelta64("NaT", "ns"), NaT.value),
         ),
         (
             pd.period_range("2000-01-01", periods=3, freq="D")._data,
-            (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns"), pd.NaT.value),
+            (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns"), NaT.value),
         ),
     ],
     ids=lambda x: type(x).__name__,
@@ -1226,7 +1226,7 @@ def test_to_numpy_extra(array):
     else:
         isnan = np.isnan
 
-    array[0] = pd.NaT
+    array[0] = NaT
     original = array.copy()
 
     result = array.to_numpy()