Fixturize tests/frame/test_quantile.py

Author: makbigc

pandas/tests/frame/test_quantile.py

@@ -3,24 +3,23 @@
 
 import pandas as pd
 from pandas import DataFrame, Series, Timestamp
-from pandas.tests.frame.common import TestData
 import pandas.util.testing as tm
 from pandas.util.testing import assert_frame_equal, assert_series_equal
 
 
-class TestDataFrameQuantile(TestData):
+class TestDataFrameQuantile():
 
-    def test_quantile(self):
+    def test_quantile(self, datetime_frame):
         from numpy import percentile
 
-        q = self.tsframe.quantile(0.1, axis=0)
-        assert q['A'] == percentile(self.tsframe['A'], 10)
-        tm.assert_index_equal(q.index, self.tsframe.columns)
+        q = datetime_frame.quantile(0.1, axis=0)
+        assert q['A'] == percentile(datetime_frame['A'], 10)
+        tm.assert_index_equal(q.index, datetime_frame.columns)
 
-        q = self.tsframe.quantile(0.9, axis=1)
+        q = datetime_frame.quantile(0.9, axis=1)
         assert (q['2000-01-17'] ==
-                percentile(self.tsframe.loc['2000-01-17'], 90))
-        tm.assert_index_equal(q.index, self.tsframe.index)
+                percentile(datetime_frame.loc['2000-01-17'], 90))
+        tm.assert_index_equal(q.index, datetime_frame.index)
 
         # test degenerate case
         q = DataFrame({'x': [], 'y': []}).quantile(0.1, axis=0)
@@ -97,19 +96,19 @@ def test_quantile_axis_parameter(self):
         with pytest.raises(ValueError, match=msg):
             df.quantile(0.1, axis="column")
 
-    def test_quantile_interpolation(self):
+    def test_quantile_interpolation(self, datetime_frame, int_frame):
         # see gh-10174
         from numpy import percentile
 
         # interpolation = linear (default case)
-        q = self.tsframe.quantile(0.1, axis=0, interpolation='linear')
-        assert q['A'] == percentile(self.tsframe['A'], 10)
-        q = self.intframe.quantile(0.1)
-        assert q['A'] == percentile(self.intframe['A'], 10)
+        q = datetime_frame.quantile(0.1, axis=0, interpolation='linear')
+        assert q['A'] == percentile(datetime_frame['A'], 10)
+        q = int_frame.quantile(0.1)
+        assert q['A'] == percentile(int_frame['A'], 10)
 
         # test with and without interpolation keyword
-        q1 = self.intframe.quantile(0.1)
-        assert q1['A'] == np.percentile(self.intframe['A'], 10)
+        q1 = int_frame.quantile(0.1)
+        assert q1['A'] == np.percentile(int_frame['A'], 10)
         tm.assert_series_equal(q, q1)
 
         # interpolation method other than default linear
@@ -214,11 +213,11 @@ def test_quantile_datetime(self):
         # result = df[['a', 'c']].quantile(.5)
         # result = df[['a', 'c']].quantile([.5])
 
-    def test_quantile_invalid(self):
+    def test_quantile_invalid(self, datetime_frame):
         msg = 'percentiles should all be in the interval \\[0, 1\\]'
         for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
             with pytest.raises(ValueError, match=msg):
-                self.tsframe.quantile(invalid)
+                datetime_frame.quantile(invalid)
 
     def test_quantile_box(self):
         df = DataFrame({'A': [pd.Timestamp('2011-01-01'),