parametrise over unit for more tests

Author: MarcoGorelli

pandas/tests/resample/test_datetime_index.py

@@ -1204,14 +1204,14 @@ def test_resample_anchored_multiday(label, sec):
     assert result.index[-1] == Timestamp(f"2014-10-15 23:00:{sec}00")
 
 
-def test_corner_cases():
+def test_corner_cases(unit):
     # miscellaneous test coverage
 
-    rng = date_range("1/1/2000", periods=12, freq="t")
+    rng = date_range("1/1/2000", periods=12, freq="t").as_unit(unit)
     ts = Series(np.random.randn(len(rng)), index=rng)
 
     result = ts.resample("5t", closed="right", label="left").mean()
-    ex_index = date_range("1999-12-31 23:55", periods=4, freq="5t")
+    ex_index = date_range("1999-12-31 23:55", periods=4, freq="5t").as_unit(unit)
     tm.assert_index_equal(result.index, ex_index)
 
 
@@ -1223,33 +1223,34 @@ def test_corner_cases_period(simple_period_range_series):
     assert len(result) == 0
 
 
-def test_corner_cases_date(simple_date_range_series):
+def test_corner_cases_date(simple_date_range_series, unit):
     # resample to periods
     ts = simple_date_range_series("2000-04-28", "2000-04-30 11:00", freq="h")
+    ts.index = ts.index.as_unit(unit)
     result = ts.resample("M", kind="period").mean()
     assert len(result) == 1
     assert result.index[0] == Period("2000-04", freq="M")
 
 
-def test_anchored_lowercase_buglet():
-    dates = date_range("4/16/2012 20:00", periods=50000, freq="s")
+def test_anchored_lowercase_buglet(unit):
+    dates = date_range("4/16/2012 20:00", periods=50000, freq="s").as_unit(unit)
     ts = Series(np.random.randn(len(dates)), index=dates)
     # it works!
     ts.resample("d").mean()
 
 
-def test_upsample_apply_functions():
+def test_upsample_apply_functions(unit):
     # #1596
-    rng = date_range("2012-06-12", periods=4, freq="h")
+    rng = date_range("2012-06-12", periods=4, freq="h").as_unit(unit)
 
     ts = Series(np.random.randn(len(rng)), index=rng)
 
     result = ts.resample("20min").aggregate(["mean", "sum"])
     assert isinstance(result, DataFrame)
 
 
-def test_resample_not_monotonic():
-    rng = date_range("2012-06-12", periods=200, freq="h")
+def test_resample_not_monotonic(unit):
+    rng = date_range("2012-06-12", periods=200, freq="h").as_unit(unit)
     ts = Series(np.random.randn(len(rng)), index=rng)
 
     ts = ts.take(np.random.permutation(len(ts)))
@@ -1401,7 +1402,7 @@ def test_resample_timegrouper(dates):
     tm.assert_frame_equal(result, expected)
 
 
-def test_resample_nunique():
+def test_resample_nunique(unit):
 
     # GH 12352
     df = DataFrame(
@@ -1416,6 +1417,7 @@ def test_resample_nunique():
             },
         }
     )
+    df.index = df.index.as_unit(unit)
     r = df.resample("D")
     g = df.groupby(Grouper(freq="D"))
     expected = df.groupby(Grouper(freq="D")).ID.apply(lambda x: x.nunique())
@@ -1432,9 +1434,10 @@ def test_resample_nunique():
     tm.assert_series_equal(result, expected)
 
 
-def test_resample_nunique_preserves_column_level_names():
+def test_resample_nunique_preserves_column_level_names(unit):
     # see gh-23222
     df = tm.makeTimeDataFrame(freq="1D").abs()
+    df.index = df.index.as_unit(unit)
     df.columns = pd.MultiIndex.from_arrays(
         [df.columns.tolist()] * 2, names=["lev0", "lev1"]
     )
@@ -1540,17 +1543,19 @@ def test_resample_across_dst():
     tm.assert_frame_equal(result, expected)
 
 
-def test_groupby_with_dst_time_change():
+def test_groupby_with_dst_time_change(unit):
     # GH 24972
-    index = DatetimeIndex(
-        [1478064900001000000, 1480037118776792000], tz="UTC"
-    ).tz_convert("America/Chicago")
+    index = (
+        DatetimeIndex([1478064900001000000, 1480037118776792000], tz="UTC")
+        .tz_convert("America/Chicago")
+        .as_unit(unit)
+    )
 
     df = DataFrame([1, 2], index=index)
     result = df.groupby(Grouper(freq="1d")).last()
     expected_index_values = date_range(
         "2016-11-02", "2016-11-24", freq="d", tz="America/Chicago"
-    )
+    ).as_unit(unit)
 
     index = DatetimeIndex(expected_index_values)
     expected = DataFrame([1.0] + ([np.nan] * 21) + [2.0], index=index)