fix inconsistent namespace usage (#37842)

Author: jan-mue

pandas/tests/arithmetic/test_period.py

@@ -8,7 +8,7 @@
 
 
 def test_uses_pandas_na():
-    a = pd.array([1, None], dtype=pd.Float64Dtype())
+    a = pd.array([1, None], dtype=Float64Dtype())
     assert a[1] is pd.NA
 
 

pandas/tests/arrays/floating/test_construction.py

@@ -200,10 +200,10 @@ def test_update_dtype_raises(original, dtype, expected_error_msg):
 
 def test_repr():
     # GH-34352
-    result = str(pd.SparseDtype("int64", fill_value=0))
+    result = str(SparseDtype("int64", fill_value=0))
     expected = "Sparse[int64, 0]"
     assert result == expected
 
-    result = str(pd.SparseDtype(object, fill_value="0"))
+    result = str(SparseDtype(object, fill_value="0"))
     expected = "Sparse[object, '0']"
     assert result == expected

pandas/tests/arrays/sparse/test_dtype.py

@@ -31,7 +31,7 @@ def period_index(freqstr):
     the PeriodIndex behavior.
     """
     # TODO: non-monotone indexes; NaTs, different start dates
-    pi = pd.period_range(start=pd.Timestamp("2000-01-01"), periods=100, freq=freqstr)
+    pi = pd.period_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
     return pi
 
 
@@ -45,7 +45,7 @@ def datetime_index(freqstr):
     the DatetimeIndex behavior.
     """
     # TODO: non-monotone indexes; NaTs, different start dates, timezones
-    dti = pd.date_range(start=pd.Timestamp("2000-01-01"), periods=100, freq=freqstr)
+    dti = pd.date_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr)
     return dti
 
 
@@ -58,7 +58,7 @@ def timedelta_index():
     the TimedeltaIndex behavior.
     """
     # TODO: flesh this out
-    return pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])
+    return TimedeltaIndex(["1 Day", "3 Hours", "NaT"])
 
 
 class SharedTests:
@@ -139,7 +139,7 @@ def test_take(self):
 
         tm.assert_index_equal(self.index_cls(result), expected)
 
-    @pytest.mark.parametrize("fill_value", [2, 2.0, pd.Timestamp.now().time])
+    @pytest.mark.parametrize("fill_value", [2, 2.0, Timestamp.now().time])
     def test_take_fill_raises(self, fill_value):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
 
@@ -539,7 +539,7 @@ def test_median(self, arr1d):
 class TestDatetimeArray(SharedTests):
     index_cls = pd.DatetimeIndex
     array_cls = DatetimeArray
-    dtype = pd.Timestamp
+    dtype = Timestamp
 
     @pytest.fixture
     def arr1d(self, tz_naive_fixture, freqstr):
@@ -741,7 +741,7 @@ def test_take_fill_valid(self, arr1d):
         arr = arr1d
         dti = self.index_cls(arr1d)
 
-        now = pd.Timestamp.now().tz_localize(dti.tz)
+        now = Timestamp.now().tz_localize(dti.tz)
         result = arr.take([-1, 1], allow_fill=True, fill_value=now)
         assert result[0] == now
 
@@ -752,10 +752,10 @@ def test_take_fill_valid(self, arr1d):
 
         with pytest.raises(TypeError, match=msg):
             # fill_value Period invalid
-            arr.take([-1, 1], allow_fill=True, fill_value=pd.Period("2014Q1"))
+            arr.take([-1, 1], allow_fill=True, fill_value=Period("2014Q1"))
 
         tz = None if dti.tz is not None else "US/Eastern"
-        now = pd.Timestamp.now().tz_localize(tz)
+        now = Timestamp.now().tz_localize(tz)
         msg = "Cannot compare tz-naive and tz-aware datetime-like objects"
         with pytest.raises(TypeError, match=msg):
             # Timestamp with mismatched tz-awareness
@@ -828,22 +828,22 @@ def test_strftime_nat(self):
 
 
 class TestTimedeltaArray(SharedTests):
-    index_cls = pd.TimedeltaIndex
+    index_cls = TimedeltaIndex
     array_cls = TimedeltaArray
     dtype = pd.Timedelta
 
     def test_from_tdi(self):
-        tdi = pd.TimedeltaIndex(["1 Day", "3 Hours"])
+        tdi = TimedeltaIndex(["1 Day", "3 Hours"])
         arr = TimedeltaArray(tdi)
         assert list(arr) == list(tdi)
 
         # Check that Index.__new__ knows what to do with TimedeltaArray
         tdi2 = pd.Index(arr)
-        assert isinstance(tdi2, pd.TimedeltaIndex)
+        assert isinstance(tdi2, TimedeltaIndex)
         assert list(tdi2) == list(arr)
 
     def test_astype_object(self):
-        tdi = pd.TimedeltaIndex(["1 Day", "3 Hours"])
+        tdi = TimedeltaIndex(["1 Day", "3 Hours"])
         arr = TimedeltaArray(tdi)
         asobj = arr.astype("O")
         assert isinstance(asobj, np.ndarray)
@@ -868,7 +868,7 @@ def test_total_seconds(self, timedelta_index):
 
         tm.assert_numpy_array_equal(result, expected.values)
 
-    @pytest.mark.parametrize("propname", pd.TimedeltaIndex._field_ops)
+    @pytest.mark.parametrize("propname", TimedeltaIndex._field_ops)
     def test_int_properties(self, timedelta_index, propname):
         tdi = timedelta_index
         arr = TimedeltaArray(tdi)
@@ -928,7 +928,7 @@ def test_take_fill_valid(self, timedelta_index):
         result = arr.take([-1, 1], allow_fill=True, fill_value=td1)
         assert result[0] == td1
 
-        now = pd.Timestamp.now()
+        now = Timestamp.now()
         value = now
         msg = f"value should be a '{arr._scalar_type.__name__}' or 'NaT'. Got"
         with pytest.raises(TypeError, match=msg):
@@ -947,9 +947,9 @@ def test_take_fill_valid(self, timedelta_index):
 
 
 class TestPeriodArray(SharedTests):
-    index_cls = pd.PeriodIndex
+    index_cls = PeriodIndex
     array_cls = PeriodArray
-    dtype = pd.Period
+    dtype = Period
 
     @pytest.fixture
     def arr1d(self, period_index):
@@ -962,7 +962,7 @@ def test_from_pi(self, arr1d):
 
         # Check that Index.__new__ knows what to do with PeriodArray
         pi2 = pd.Index(arr)
-        assert isinstance(pi2, pd.PeriodIndex)
+        assert isinstance(pi2, PeriodIndex)
         assert list(pi2) == list(arr)
 
     def test_astype_object(self, arr1d):
@@ -1075,7 +1075,7 @@ def test_strftime_nat(self):
     "array,casting_nats",
     [
         (
-            pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
             (pd.NaT, np.timedelta64("NaT", "ns")),
         ),
         (
@@ -1099,7 +1099,7 @@ def test_casting_nat_setitem_array(array, casting_nats):
     "array,non_casting_nats",
     [
         (
-            pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
             (np.datetime64("NaT", "ns"), pd.NaT.value),
         ),
         (
@@ -1164,8 +1164,8 @@ def test_to_numpy_extra(array):
     "values",
     [
         pd.to_datetime(["2020-01-01", "2020-02-01"]),
-        pd.TimedeltaIndex([1, 2], unit="D"),
-        pd.PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
+        TimedeltaIndex([1, 2], unit="D"),
+        PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
     ],
 )
 @pytest.mark.parametrize(
@@ -1195,12 +1195,12 @@ def test_searchsorted_datetimelike_with_listlike(values, klass, as_index):
     "values",
     [
         pd.to_datetime(["2020-01-01", "2020-02-01"]),
-        pd.TimedeltaIndex([1, 2], unit="D"),
-        pd.PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
+        TimedeltaIndex([1, 2], unit="D"),
+        PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"),
     ],
 )
 @pytest.mark.parametrize(
-    "arg", [[1, 2], ["a", "b"], [pd.Timestamp("2020-01-01", tz="Europe/London")] * 2]
+    "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2]
 )
 def test_searchsorted_datetimelike_with_listlike_invalid_dtype(values, arg):
     # https://github.com/pandas-dev/pandas/issues/32762

pandas/tests/arrays/test_datetimelike.py

@@ -3,18 +3,18 @@
 """
 import pandas._config.config as cf
 
-import pandas as pd
+from pandas import CategoricalIndex
 
 
 class TestCategoricalIndexRepr:
     def test_string_categorical_index_repr(self):
         # short
-        idx = pd.CategoricalIndex(["a", "bb", "ccc"])
+        idx = CategoricalIndex(["a", "bb", "ccc"])
         expected = """CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa
         assert repr(idx) == expected
 
         # multiple lines
-        idx = pd.CategoricalIndex(["a", "bb", "ccc"] * 10)
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 10)
         expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
                   'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb',
                   'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
@@ -23,7 +23,7 @@ def test_string_categorical_index_repr(self):
         assert repr(idx) == expected
 
         # truncated
-        idx = pd.CategoricalIndex(["a", "bb", "ccc"] * 100)
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 100)
         expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
                   ...
                   'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
@@ -32,20 +32,20 @@ def test_string_categorical_index_repr(self):
         assert repr(idx) == expected
 
         # larger categories
-        idx = pd.CategoricalIndex(list("abcdefghijklmmo"))
+        idx = CategoricalIndex(list("abcdefghijklmmo"))
         expected = """CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
                   'm', 'm', 'o'],
                  categories=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', ...], ordered=False, dtype='category')"""  # noqa
 
         assert repr(idx) == expected
 
         # short
-        idx = pd.CategoricalIndex(["あ", "いい", "ううう"])
+        idx = CategoricalIndex(["あ", "いい", "ううう"])
         expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa
         assert repr(idx) == expected
 
         # multiple lines
-        idx = pd.CategoricalIndex(["あ", "いい", "ううう"] * 10)
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
         expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
                   'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                   'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
@@ -54,7 +54,7 @@ def test_string_categorical_index_repr(self):
         assert repr(idx) == expected
 
         # truncated
-        idx = pd.CategoricalIndex(["あ", "いい", "ううう"] * 100)
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
         expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
                   ...
                   'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
@@ -63,7 +63,7 @@ def test_string_categorical_index_repr(self):
         assert repr(idx) == expected
 
         # larger categories
-        idx = pd.CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+        idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
         expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し',
                   'す', 'せ', 'そ'],
                  categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')"""  # noqa
@@ -74,12 +74,12 @@ def test_string_categorical_index_repr(self):
         with cf.option_context("display.unicode.east_asian_width", True):
 
             # short
-            idx = pd.CategoricalIndex(["あ", "いい", "ううう"])
+            idx = CategoricalIndex(["あ", "いい", "ううう"])
             expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa
             assert repr(idx) == expected
 
             # multiple lines
-            idx = pd.CategoricalIndex(["あ", "いい", "ううう"] * 10)
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
             expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                   'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
                   'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
@@ -89,7 +89,7 @@ def test_string_categorical_index_repr(self):
             assert repr(idx) == expected
 
             # truncated
-            idx = pd.CategoricalIndex(["あ", "いい", "ううう"] * 100)
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
             expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
                   'ううう', 'あ',
                   ...
@@ -100,7 +100,7 @@ def test_string_categorical_index_repr(self):
             assert repr(idx) == expected
 
             # larger categories
-            idx = pd.CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+            idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
             expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ',
                   'さ', 'し', 'す', 'せ', 'そ'],
                  categories=['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', ...], ordered=False, dtype='category')"""  # noqa