update groupby / resample / reshape tests

Author: jorisvandenbossche

pandas/tests/groupby/test_timegrouper.py

@@ -739,8 +739,7 @@ def test_groupby_max_datetime64(self):
         # GH 5869
         # datetimelike dtype conversion from int
         df = DataFrame({"A": Timestamp("20130101"), "B": np.arange(5)})
-        # TODO: can we retain second reso in .apply here?
-        expected = df.groupby("A")["A"].apply(lambda x: x.max()).astype("M8[s]")
+        expected = df.groupby("A")["A"].apply(lambda x: x.max())
         result = df.groupby("A")["A"].max()
         tm.assert_series_equal(result, expected)
 

pandas/tests/resample/test_base.py

@@ -243,7 +243,7 @@ def test_resample_empty_sum_string(string_dtype_no_object, min_count):
     result = rs.sum(min_count=min_count)
 
     value = "" if min_count == 0 else pd.NA
-    index = date_range(start="2000-01-01", freq="20s", periods=2, unit="s")
+    index = date_range(start="2000-01-01", freq="20s", periods=2, unit="us")
     expected = Series(value, index=index, dtype=dtype)
     tm.assert_series_equal(result, expected)
 

pandas/tests/reshape/concat/test_append_common.py

@@ -21,12 +21,12 @@
             "float64": [1.1, np.nan, 3.3],
             "category": Categorical(["X", "Y", "Z"]),
             "object": ["a", "b", "c"],
-            "datetime64[s]": [
+            "datetime64[us]": [
                 pd.Timestamp("2011-01-01"),
                 pd.Timestamp("2011-01-02"),
                 pd.Timestamp("2011-01-03"),
             ],
-            "datetime64[s, US/Eastern]": [
+            "datetime64[us, US/Eastern]": [
                 pd.Timestamp("2011-01-01", tz="US/Eastern"),
                 pd.Timestamp("2011-01-02", tz="US/Eastern"),
                 pd.Timestamp("2011-01-03", tz="US/Eastern"),

pandas/tests/reshape/concat/test_datetimes.py

@@ -358,7 +358,7 @@ def test_concat_tz_series_tzlocal(self):
 
         result = concat([Series(x), Series(y)], ignore_index=True)
         tm.assert_series_equal(result, Series(x + y))
-        assert result.dtype == "datetime64[s, tzlocal()]"
+        assert result.dtype == "datetime64[us, tzlocal()]"
 
     def test_concat_tz_series_with_datetimelike(self):
         # see gh-12620: tz and timedelta

pandas/tests/reshape/test_cut.py

@@ -1,5 +1,3 @@
-from datetime import datetime
-
 import numpy as np
 import pytest
 
@@ -452,9 +450,8 @@ def test_datetime_bin(conv):
     bins = [conv(v) for v in bin_data]
     result = Series(cut(data, bins=bins))
 
-    if type(bins[0]) is datetime:
-        # The bins have microsecond dtype -> so does result
-        expected = expected.astype("interval[datetime64[us]]")
+    if type(bins[0]) is np.datetime64:
+        expected = expected.astype("interval[datetime64[s]]")
 
     expected = expected.astype(CategoricalDtype(ordered=True))
     tm.assert_series_equal(result, expected)