testcase for pandas-dev#33168 addressing review comments

Author: dannyi96

pandas/tests/groupby/test_quantile.py

@@ -350,44 +350,28 @@ def test_timestamp_groupby_quantile():
     df = DataFrame(
         {
             "timestamp": pd.date_range(
-                start="2020-04-19 00:00:00", freq="1T", periods=200, tz="UTC"
+                start="2020-04-19 00:00:00", freq="1T", periods=100, tz="UTC"
             ).floor("1H"),
-            "category": list(range(1, 201)),
-            "value": list(range(201, 401)),
+            "category": list(range(1, 101)),
+            "value": list(range(101, 201)),
         }
     )
 
-    result = df.groupby("timestamp").quantile([0.1, 0.5, 0.9])
+    result = df.groupby("timestamp").quantile([0.2, 0.8])
 
     expected = DataFrame(
         [
-            {"category": 6.9, "value": 206.9},
-            {"category": 30.5, "value": 230.5},
-            {"category": 54.1, "value": 254.1},
-            {"category": 66.9, "value": 266.9},
-            {"category": 90.5, "value": 290.5},
-            {"category": 114.1, "value": 314.1},
-            {"category": 126.9, "value": 326.9},
-            {"category": 150.5, "value": 350.5},
-            {"category": 174.1, "value": 374.1},
-            {"category": 182.9, "value": 382.9},
-            {"category": 190.5, "value": 390.5},
-            {"category": 198.1, "value": 398.1},
+            {"category": 12.8, "value": 112.8},
+            {"category": 48.2, "value": 148.2},
+            {"category": 68.8, "value": 168.8},
+            {"category": 92.2, "value": 192.2},
         ],
         index=pd.MultiIndex.from_tuples(
             [
-                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.1),
-                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.5),
-                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.9),
-                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.1),
-                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.5),
-                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.9),
-                (pd.Timestamp("2020-04-19 02:00:00+00:00"), 0.1),
-                (pd.Timestamp("2020-04-19 02:00:00+00:00"), 0.5),
-                (pd.Timestamp("2020-04-19 02:00:00+00:00"), 0.9),
-                (pd.Timestamp("2020-04-19 03:00:00+00:00"), 0.1),
-                (pd.Timestamp("2020-04-19 03:00:00+00:00"), 0.5),
-                (pd.Timestamp("2020-04-19 03:00:00+00:00"), 0.9),
+                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.2),
+                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.8),
+                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.2),
+                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.8),
             ],
             names=("timestamp", None),
         ),