TST: organize tests in test_timeseries

pandas/tests/frame/test_to_csv.py

@@ -1352,3 +1352,12 @@ def test_gz_lineend(self):
                 result = f.read().decode("utf-8")
 
         assert result == expected
+
+    def test_to_csv_numpy_16_bug(self):
+        frame = DataFrame({"a": date_range("1/1/2000", periods=10)})
+
+        buf = StringIO()
+        frame.to_csv(buf)
+
+        result = buf.getvalue()
+        assert "2000-01-01" in result

pandas/tests/series/methods/test_autocorr.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+
+class TestAutoCorr:
+    def test_autocorr(self, datetime_series):
+        # Just run the function
+        corr1 = datetime_series.autocorr()
+
+        # Now run it with the lag parameter
+        corr2 = datetime_series.autocorr(lag=1)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2
+
+        # Choose a random lag between 1 and length of Series - 2
+        # and compare the result with the Series corr() function
+        n = 1 + np.random.randint(max(1, len(datetime_series) - 2))
+        corr1 = datetime_series.corr(datetime_series.shift(n))
+        corr2 = datetime_series.autocorr(lag=n)
+
+        # corr() with lag needs Series of at least length 2
+        if len(datetime_series) <= 2:
+            assert np.isnan(corr1)
+            assert np.isnan(corr2)
+        else:
+            assert corr1 == corr2

pandas/tests/series/test_constructors.py

@@ -1428,3 +1428,10 @@ def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture):
         result = Series([Timestamp("2019", tz=tz)], dtype="datetime64[ns]")
         expected = Series([Timestamp("2019")])
         tm.assert_series_equal(result, expected)
+
+    def test_constructor_datetime64(self):
+        rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
+        dates = np.asarray(rng)
+
+        series = Series(dates)
+        assert np.issubdtype(series.dtype, np.dtype("M8[ns]"))

pandas/tests/series/test_timeseries.py

@@ -1,6 +1,5 @@
-from io import StringIO
-
 import numpy as np
+import pytest
 
 import pandas as pd
 from pandas import DataFrame, DatetimeIndex, Series, date_range, timedelta_range
@@ -19,33 +18,6 @@ def assert_range_equal(left, right):
 
 
 class TestTimeSeries:
-    def test_autocorr(self, datetime_series):
-        # Just run the function
-        corr1 = datetime_series.autocorr()
-
-        # Now run it with the lag parameter
-        corr2 = datetime_series.autocorr(lag=1)
-
-        # corr() with lag needs Series of at least length 2
-        if len(datetime_series) <= 2:
-            assert np.isnan(corr1)
-            assert np.isnan(corr2)
-        else:
-            assert corr1 == corr2
-
-        # Choose a random lag between 1 and length of Series - 2
-        # and compare the result with the Series corr() function
-        n = 1 + np.random.randint(max(1, len(datetime_series) - 2))
-        corr1 = datetime_series.corr(datetime_series.shift(n))
-        corr2 = datetime_series.autocorr(lag=n)
-
-        # corr() with lag needs Series of at least length 2
-        if len(datetime_series) <= 2:
-            assert np.isnan(corr1)
-            assert np.isnan(corr2)
-        else:
-            assert corr1 == corr2
-
     def test_mpl_compat_hack(self, datetime_series):
 
         # This is currently failing because the test was relying on
@@ -79,13 +51,6 @@ def test_contiguous_boolean_preserve_freq(self):
         masked = rng[mask]
         assert masked.freq is None
 
-    def test_series_ctor_datetime64(self):
-        rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
-        dates = np.asarray(rng)
-
-        series = Series(dates)
-        assert np.issubdtype(series.dtype, np.dtype("M8[ns]"))
-
     def test_promote_datetime_date(self):
         rng = date_range("1/1/2000", periods=20)
         ts = Series(np.random.randn(20), index=rng)
@@ -123,15 +88,6 @@ def test_groupby_count_dateparseerror(self):
 
         tm.assert_series_equal(result, expected)
 
-    def test_to_csv_numpy_16_bug(self):
-        frame = DataFrame({"a": date_range("1/1/2000", periods=10)})
-
-        buf = StringIO()
-        frame.to_csv(buf)
-
-        result = buf.getvalue()
-        assert "2000-01-01" in result
-
     def test_series_map_box_timedelta(self):
         # GH 11349
         s = Series(timedelta_range("1 day 1 s", periods=5, freq="h"))
@@ -175,6 +131,19 @@ def test_view_tz(self):
         )
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_asarray_object_dt64(self, tz):
+        ser = pd.Series(pd.date_range("2000", periods=2, tz=tz))
+
+        with tm.assert_produces_warning(None):
+            # Future behavior (for tzaware case) with no warning
+            result = np.asarray(ser, dtype=object)
+
+        expected = np.array(
+            [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_asarray_tz_naive(self):
         # This shouldn't produce a warning.
         ser = pd.Series(pd.date_range("2000", periods=2))
@@ -183,12 +152,6 @@ def test_asarray_tz_naive(self):
 
         tm.assert_numpy_array_equal(result, expected)
 
-        # optionally, object
-        result = np.asarray(ser, dtype=object)
-
-        expected = np.array([pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-02")])
-        tm.assert_numpy_array_equal(result, expected)
-
     def test_asarray_tz_aware(self):
         tz = "US/Central"
         ser = pd.Series(pd.date_range("2000", periods=2, tz=tz))
@@ -201,11 +164,3 @@ def test_asarray_tz_aware(self):
         result = np.asarray(ser, dtype="M8[ns]")
 
         tm.assert_numpy_array_equal(result, expected)
-
-        # Future behavior with no warning
-        expected = np.array(
-            [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)]
-        )
-        result = np.asarray(ser, dtype=object)
-
-        tm.assert_numpy_array_equal(result, expected)