Fix tests broken by II.astype('category') changes

Author: jschendel

pandas/tests/reshape/test_tile.py

@@ -4,9 +4,8 @@
 import numpy as np
 from pandas.compat import zip
 
-from pandas import (Series, Index, isna,
-                    to_datetime, DatetimeIndex, Timestamp,
-                    Interval, IntervalIndex, Categorical,
+from pandas import (Series, isna, to_datetime, DatetimeIndex,
+                    Timestamp, Interval, IntervalIndex, Categorical,
                     cut, qcut, date_range)
 import pandas.util.testing as tm
 from pandas.api.types import CategoricalDtype as CDT
@@ -29,7 +28,8 @@ def test_bins(self):
         result, bins = cut(data, 3, retbins=True)
 
         intervals = IntervalIndex.from_breaks(bins.round(3))
-        expected = intervals.take([0, 0, 0, 1, 2, 0]).astype('category')
+        intervals = intervals.take([0, 0, 0, 1, 2, 0])
+        expected = Categorical(intervals, ordered=True)
         tm.assert_categorical_equal(result, expected)
         tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667,
                                                6.53333333, 9.7]))
@@ -38,7 +38,8 @@ def test_right(self):
         data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
         result, bins = cut(data, 4, right=True, retbins=True)
         intervals = IntervalIndex.from_breaks(bins.round(3))
-        expected = intervals.astype('category').take([0, 0, 0, 2, 3, 0, 0])
+        expected = Categorical(intervals, ordered=True)
+        expected = expected.take([0, 0, 0, 2, 3, 0, 0])
         tm.assert_categorical_equal(result, expected)
         tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95,
                                                7.325, 9.7]))
@@ -47,7 +48,8 @@ def test_noright(self):
         data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
         result, bins = cut(data, 4, right=False, retbins=True)
         intervals = IntervalIndex.from_breaks(bins.round(3), closed='left')
-        expected = intervals.take([0, 0, 0, 2, 3, 0, 1]).astype('category')
+        intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
+        expected = Categorical(intervals, ordered=True)
         tm.assert_categorical_equal(result, expected)
         tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95,
                                                7.325, 9.7095]))
@@ -56,7 +58,8 @@ def test_arraylike(self):
         data = [.2, 1.4, 2.5, 6.2, 9.7, 2.1]
         result, bins = cut(data, 3, retbins=True)
         intervals = IntervalIndex.from_breaks(bins.round(3))
-        expected = intervals.take([0, 0, 0, 1, 2, 0]).astype('category')
+        intervals = intervals.take([0, 0, 0, 1, 2, 0])
+        expected = Categorical(intervals, ordered=True)
         tm.assert_categorical_equal(result, expected)
         tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667,
                                                6.53333333, 9.7]))
@@ -249,8 +252,8 @@ def test_qcut_nas(self):
 
     def test_qcut_index(self):
         result = qcut([0, 2], 2)
-        expected = Index([Interval(-0.001, 1), Interval(1, 2)]).astype(
-            'category')
+        intervals = [Interval(-0.001, 1), Interval(1, 2)]
+        expected = Categorical(intervals, ordered=True)
         tm.assert_categorical_equal(result, expected)
 
     def test_round_frac(self):

pandas/tests/test_algos.py

@@ -18,6 +18,7 @@
 from pandas.compat import lrange, range
 import pandas.core.algorithms as algos
 import pandas.util.testing as tm
+from pandas.core.dtypes.dtypes import CategoricalDtype as CDT
 from pandas.compat.numpy import np_array_datetime64_compat
 from pandas.util.testing import assert_almost_equal
 
@@ -537,8 +538,8 @@ def test_value_counts(self):
         # assert isinstance(factor, n)
         result = algos.value_counts(factor)
         breaks = [-1.194, -0.535, 0.121, 0.777, 1.433]
-        expected_index = IntervalIndex.from_breaks(breaks).astype('category')
-        expected = Series([1, 1, 1, 1], index=expected_index)
+        index = IntervalIndex.from_breaks(breaks).astype(CDT(ordered=True))
+        expected = Series([1, 1, 1, 1], index=index)
         tm.assert_series_equal(result.sort_index(), expected.sort_index())
 
     def test_value_counts_bins(self):