Added fixtures to test_quantile

pandas-dev · blueenvelope31 · Oct 20, 2018 · Oct 20, 2018 · Oct 20, 2018 · Oct 20, 2018
commit 7e2f9d1df5e0c98c053a2e8f95fb32592a4eec49
diff --git a/pandas/tests/series/test_quantile.py b/pandas/tests/series/test_quantile.py
@@ -11,25 +11,23 @@
 from pandas.core.dtypes.common import is_integer
 import pandas.util.testing as tm
 
-from .common import TestData
 
+class TestSeriesQuantile():
 
-class TestSeriesQuantile(TestData):
+    def test_quantile(self, datetime_series):
 
-    def test_quantile(self):
+        q = datetime_series.quantile(0.1)
+        assert q == np.percentile(datetime_series.dropna(), 10)
 
-        q = self.ts.quantile(0.1)
-        assert q == np.percentile(self.ts.dropna(), 10)
-
-        q = self.ts.quantile(0.9)
-        assert q == np.percentile(self.ts.dropna(), 90)
+        q = datetime_series.quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
 
         # object dtype
-        q = Series(self.ts, dtype=object).quantile(0.9)
-        assert q == np.percentile(self.ts.dropna(), 90)
+        q = Series(datetime_series, dtype=object).quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
 
         # datetime64[ns] dtype
-        dts = self.ts.index.to_series()
+        dts = datetime_series.index.to_series()
         q = dts.quantile(.2)
         assert q == Timestamp('2000-01-10 19:12:00')
 
@@ -45,38 +43,38 @@ def test_quantile(self):
         msg = 'percentiles should all be in the interval \\[0, 1\\]'
         for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
             with tm.assert_raises_regex(ValueError, msg):
-                self.ts.quantile(invalid)
+                datetime_series.quantile(invalid)
 
-    def test_quantile_multi(self):
+    def test_quantile_multi(self, datetime_series):
 
         qs = [.1, .9]
-        result = self.ts.quantile(qs)
-        expected = pd.Series([np.percentile(self.ts.dropna(), 10),
-                              np.percentile(self.ts.dropna(), 90)],
-                             index=qs, name=self.ts.name)
+        result = datetime_series.quantile(qs)
+        expected = pd.Series([np.percentile(datetime_series.dropna(), 10),
+                              np.percentile(datetime_series.dropna(), 90)],
+                             index=qs, name=datetime_series.name)
         tm.assert_series_equal(result, expected)
 
-        dts = self.ts.index.to_series()
+        dts = datetime_series.index.to_series()
         dts.name = 'xxx'
         result = dts.quantile((.2, .2))
         expected = Series([Timestamp('2000-01-10 19:12:00'),
                            Timestamp('2000-01-10 19:12:00')],
                           index=[.2, .2], name='xxx')
         tm.assert_series_equal(result, expected)
 
-        result = self.ts.quantile([])
-        expected = pd.Series([], name=self.ts.name, index=Index(
+        result = datetime_series.quantile([])
+        expected = pd.Series([], name=datetime_series.name, index=Index(
             [], dtype=float))
         tm.assert_series_equal(result, expected)
 
-    def test_quantile_interpolation(self):
+    def test_quantile_interpolation(self, datetime_series):
         # see gh-10174
 
         # interpolation = linear (default case)
-        q = self.ts.quantile(0.1, interpolation='linear')
-        assert q == np.percentile(self.ts.dropna(), 10)
-        q1 = self.ts.quantile(0.1)
-        assert q1 == np.percentile(self.ts.dropna(), 10)
+        q = datetime_series.quantile(0.1, interpolation='linear')
+        assert q == np.percentile(datetime_series.dropna(), 10)
+        q1 = datetime_series.quantile(0.1)
+        assert q1 == np.percentile(datetime_series.dropna(), 10)
 
         # test with and without interpolation keyword
         assert q == q1