Revert extraneous changes

Author: TomAugspurger

pandas/api/extensions/__init__.py

@@ -4,7 +4,7 @@
                                   register_series_accessor)
 from pandas.core.algorithms import take  # noqa
 from pandas.core.arrays import (ExtensionArray,    # noqa
-                                     ExtensionScalarOpsMixin)
+                                ExtensionScalarOpsMixin)
 from pandas.core.dtypes.dtypes import (  # noqa
     ExtensionDtype, register_extension_dtype
 )

pandas/compat/numpy/function.py

@@ -19,8 +19,7 @@
 """
 
 from numpy import ndarray
-from pandas.util._validators import (validate_args,
-                                     validate_kwargs,
+from pandas.util._validators import (validate_args, validate_kwargs,
                                      validate_args_and_kwargs)
 from pandas.errors import UnsupportedFunctionCall
 from pandas.core.dtypes.common import is_integer, is_bool

pandas/compat/pickle_compat.py

@@ -114,7 +114,7 @@ def load_reduce(self):
 
     # 19269, arrays moving
     ('pandas.core.categorical', 'Categorical'):
-        ('pandas.core.arrays.categorical', 'Categorical'),
+        ('pandas.core.arrays', 'Categorical'),
 
     # 19939, add timedeltaindex, float64index compat from 15998 move
     ('pandas.tseries.tdi', 'TimedeltaIndex'):

pandas/core/arrays/datetimes.py

@@ -523,7 +523,7 @@ def _add_delta(self, delta):
         The result's name is set outside of _add_delta by the calling
         method (__add__ or __sub__)
         """
-        from pandas.core.arrays import TimedeltaArrayMixin
+        from pandas.core.arrays.timedeltas import TimedeltaArrayMixin
 
         if isinstance(delta, (Tick, timedelta, np.timedelta64)):
             new_values = self._add_delta_td(delta)
@@ -818,7 +818,7 @@ def to_period(self, freq=None):
         pandas.PeriodIndex: Immutable ndarray holding ordinal values
         pandas.DatetimeIndex.to_pydatetime: Return DatetimeIndex as object
         """
-        from pandas.core.arrays import PeriodArrayMixin
+        from pandas.core.arrays.period import PeriodArrayMixin
 
         if self.tz is not None:
             warnings.warn("Converting to PeriodArray/Index representation "

pandas/core/arrays/integer.py

@@ -18,8 +18,7 @@
     is_integer_dtype,
     is_object_dtype,
     is_list_like)
-from pandas.core.arrays import (ExtensionArray,
-                                ExtensionOpsMixin)
+from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
 from pandas.core.dtypes.base import ExtensionDtype
 from pandas.core.dtypes.dtypes import register_extension_dtype
 from pandas.core.dtypes.missing import isna, notna

pandas/core/arrays/interval.py

@@ -23,7 +23,7 @@
 from pandas.util._decorators import Appender
 from pandas.util._doctools import _WritableDoc
 
-from pandas.core.arrays import ExtensionArray, Categorical
+from . import ExtensionArray, Categorical
 
 _VALID_CLOSED = {'left', 'right', 'both', 'neither'}
 _interval_shared_docs = {}

pandas/core/arrays/period.py

@@ -301,7 +301,7 @@ def to_timestamp(self, freq=None, how='start'):
         -------
         DatetimeArray/Index
         """
-        from pandas.core.arrays import DatetimeArrayMixin
+        from pandas.core.arrays.datetimes import DatetimeArrayMixin
 
         how = libperiod._validate_end_alias(how)
 

pandas/core/groupby/generic.py

@@ -44,7 +44,7 @@
 from pandas.core.dtypes.cast import maybe_downcast_to_dtype
 from pandas.core.base import SpecificationError, DataError
 from pandas.core.index import Index, MultiIndex, CategoricalIndex
-from pandas.core.arrays import Categorical
+from pandas.core.arrays.categorical import Categorical
 from pandas.core.internals import BlockManager, make_block
 from pandas.compat.numpy import _np_version_under1p13
 

pandas/tests/arrays/sparse/test_array.py

@@ -9,9 +9,7 @@
 import numpy as np
 import pandas as pd
 
-from pandas.core.sparse.api import (SparseArray,
-                                    SparseSeries,
-                                    SparseDtype)
+from pandas.core.sparse.api import SparseArray, SparseSeries, SparseDtype
 from pandas._libs.sparse import IntIndex
 from pandas.util.testing import assert_almost_equal
 import pandas.util.testing as tm

pandas/tests/arrays/test_datetimelike.py

@@ -5,9 +5,9 @@
 import pandas as pd
 import pandas.util.testing as tm
 
-from pandas.core.arrays import DatetimeArrayMixin
-from pandas.core.arrays import TimedeltaArrayMixin
-from pandas.core.arrays import PeriodArrayMixin
+from pandas.core.arrays.datetimes import DatetimeArrayMixin
+from pandas.core.arrays.timedeltas import TimedeltaArrayMixin
+from pandas.core.arrays.period import PeriodArrayMixin
 
 
 # TODO: more freq variants

pandas/tests/indexing/test_indexing.py

@@ -18,7 +18,6 @@
 import pandas as pd
 from pandas.core.indexing import (_non_reducing_slice, _maybe_numeric_slice,
                                   validate_indices)
-from pandas.core.arrays import integer_array
 from pandas import NaT, DataFrame, Index, Series, MultiIndex
 import pandas.util.testing as tm
 from pandas.compat import PY2
@@ -1085,10 +1084,10 @@ def test_validate_indices_empty():
 def test_extension_array_cross_section():
     # A cross-section of a homogeneous EA should be an EA
     df = pd.DataFrame({
-        "A": integer_array([1, 2]),
-        "B": integer_array([3, 4])
+        "A": pd.core.arrays.integer_array([1, 2]),
+        "B": pd.core.arrays.integer_array([3, 4])
     }, index=['a', 'b'])
-    expected = pd.Series(integer_array([1, 3]),
+    expected = pd.Series(pd.core.arrays.integer_array([1, 3]),
                          index=['A', 'B'], name='a')
     result = df.loc['a']
     tm.assert_series_equal(result, expected)
@@ -1099,7 +1098,7 @@ def test_extension_array_cross_section():
 
 def test_extension_array_cross_section_converts():
     df = pd.DataFrame({
-        "A": integer_array([1, 2]),
+        "A": pd.core.arrays.integer_array([1, 2]),
         "B": np.array([1, 2]),
     }, index=['a', 'b'])
     result = df.loc['a']

pandas/tests/test_base.py

@@ -1199,8 +1199,8 @@ def test_iter_box(self):
      'datetime64[ns, US/Central]'),
     (pd.TimedeltaIndex([10**10]), np.ndarray, 'm8[ns]'),
     (pd.PeriodIndex([2018, 2019], freq='A'), np.ndarray, 'object'),
-    (pd.IntervalIndex.from_breaks([0, 1, 2]),
-     pd.core.arrays.interval.IntervalArray, 'interval'),
+    (pd.IntervalIndex.from_breaks([0, 1, 2]), pd.core.arrays.IntervalArray,
+     'interval'),
 ])
 def test_values_consistent(array, expected_type, dtype):
     l_values = pd.Series(array)._values