REF: collect reduction tests

pandas/tests/series/test_analytics.py

@@ -9,12 +9,6 @@
 
 
 class TestSeriesAnalytics:
-    def test_prod_numpy16_bug(self):
-        s = Series([1.0, 1.0, 1.0], index=range(3))
-        result = s.prod()
-
-        assert not isinstance(result, Series)
-
     def test_matmul(self):
         # matmul test is for GH #10259
         a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
@@ -125,74 +119,3 @@ def test_is_monotonic(self):
         s = Series(list(reversed(s.tolist())))
         assert s.is_monotonic is False
         assert s.is_monotonic_decreasing is True
-
-    @pytest.mark.parametrize("func", [np.any, np.all])
-    @pytest.mark.parametrize("kwargs", [dict(keepdims=True), dict(out=object())])
-    def test_validate_any_all_out_keepdims_raises(self, kwargs, func):
-        s = pd.Series([1, 2])
-        param = list(kwargs)[0]
-        name = func.__name__
-
-        msg = (
-            f"the '{param}' parameter is not "
-            "supported in the pandas "
-            fr"implementation of {name}\(\)"
-        )
-        with pytest.raises(ValueError, match=msg):
-            func(s, **kwargs)
-
-    def test_validate_sum_initial(self):
-        s = pd.Series([1, 2])
-        msg = (
-            r"the 'initial' parameter is not "
-            r"supported in the pandas "
-            r"implementation of sum\(\)"
-        )
-        with pytest.raises(ValueError, match=msg):
-            np.sum(s, initial=10)
-
-    def test_validate_median_initial(self):
-        s = pd.Series([1, 2])
-        msg = (
-            r"the 'overwrite_input' parameter is not "
-            r"supported in the pandas "
-            r"implementation of median\(\)"
-        )
-        with pytest.raises(ValueError, match=msg):
-            # It seems like np.median doesn't dispatch, so we use the
-            # method instead of the ufunc.
-            s.median(overwrite_input=True)
-
-    def test_validate_stat_keepdims(self):
-        s = pd.Series([1, 2])
-        msg = (
-            r"the 'keepdims' parameter is not "
-            r"supported in the pandas "
-            r"implementation of sum\(\)"
-        )
-        with pytest.raises(ValueError, match=msg):
-            np.sum(s, keepdims=True)
-
-    def test_td64_summation_overflow(self):
-        # GH 9442
-        s = pd.Series(pd.date_range("20130101", periods=100000, freq="H"))
-        s[0] += pd.Timedelta("1s 1ms")
-
-        # mean
-        result = (s - s.min()).mean()
-        expected = pd.Timedelta((pd.TimedeltaIndex(s - s.min()).asi8 / len(s)).sum())
-
-        # the computation is converted to float so
-        # might be some loss of precision
-        assert np.allclose(result.value / 1000, expected.value / 1000)
-
-        # sum
-        msg = "overflow in timedelta operation"
-        with pytest.raises(ValueError, match=msg):
-            (s - s.min()).sum()
-
-        s1 = s[0:10000]
-        with pytest.raises(ValueError, match=msg):
-            (s1 - s1.min()).sum()
-        s2 = s[0:1000]
-        (s2 - s2.min()).sum()

pandas/tests/series/test_reductions.py

@@ -1,3 +1,6 @@
+import numpy as np
+import pytest
+
 import pandas as pd
 from pandas import Series
 
@@ -9,3 +12,86 @@ def test_reductions_td64_with_nat():
     assert ser.median() == exp
     assert ser.min() == exp
     assert ser.max() == exp
+
+
+def test_td64_summation_overflow():
+    # GH#9442
+    ser = Series(pd.date_range("20130101", periods=100000, freq="H"))
+    ser[0] += pd.Timedelta("1s 1ms")
+
+    # mean
+    result = (ser - ser.min()).mean()
+    expected = pd.Timedelta((pd.TimedeltaIndex(ser - ser.min()).asi8 / len(ser)).sum())
+
+    # the computation is converted to float so
+    # might be some loss of precision
+    assert np.allclose(result.value / 1000, expected.value / 1000)
+
+    # sum
+    msg = "overflow in timedelta operation"
+    with pytest.raises(ValueError, match=msg):
+        (ser - ser.min()).sum()
+
+    s1 = ser[0:10000]
+    with pytest.raises(ValueError, match=msg):
+        (s1 - s1.min()).sum()
+    s2 = ser[0:1000]
+    (s2 - s2.min()).sum()
+
+
+def test_prod_numpy16_bug():
+    ser = Series([1.0, 1.0, 1.0], index=range(3))
+    result = ser.prod()
+
+    assert not isinstance(result, Series)
+
+
+@pytest.mark.parametrize("func", [np.any, np.all])
+@pytest.mark.parametrize("kwargs", [dict(keepdims=True), dict(out=object())])
+def test_validate_any_all_out_keepdims_raises(kwargs, func):
+    ser = Series([1, 2])
+    param = list(kwargs)[0]
+    name = func.__name__
+
+    msg = (
+        f"the '{param}' parameter is not "
+        "supported in the pandas "
+        fr"implementation of {name}\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        func(ser, **kwargs)
+
+
+def test_validate_sum_initial():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'initial' parameter is not "
+        r"supported in the pandas "
+        r"implementation of sum\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.sum(ser, initial=10)
+
+
+def test_validate_median_initial():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'overwrite_input' parameter is not "
+        r"supported in the pandas "
+        r"implementation of median\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        # It seems like np.median doesn't dispatch, so we use the
+        # method instead of the ufunc.
+        ser.median(overwrite_input=True)
+
+
+def test_validate_stat_keepdims():
+    ser = Series([1, 2])
+    msg = (
+        r"the 'keepdims' parameter is not "
+        r"supported in the pandas "
+        r"implementation of sum\(\)"
+    )
+    with pytest.raises(ValueError, match=msg):
+        np.sum(ser, keepdims=True)

pandas/tests/test_lib.py

@@ -1,9 +1,8 @@
 import numpy as np
 import pytest
 
-from pandas._libs import lib, writers as libwriters
+from pandas._libs import Timestamp, lib, writers as libwriters
 
-import pandas as pd
 from pandas import Index
 import pandas._testing as tm
 
@@ -41,7 +40,7 @@ def test_fast_unique_multiple_list_gen_sort(self):
         tm.assert_numpy_array_equal(np.array(out), expected)
 
     def test_fast_unique_multiple_unsortable_runtimewarning(self):
-        arr = [np.array(["foo", pd.Timestamp("2000")])]
+        arr = [np.array(["foo", Timestamp("2000")])]
         with tm.assert_produces_warning(RuntimeWarning):
             lib.fast_unique_multiple(arr, sort=None)
 

pandas/tests/test_sorting.py

@@ -298,7 +298,7 @@ def verify_order(df):
             "outer": np.ones(len(out), dtype="bool"),
         }
 
-        for how in "left", "right", "outer", "inner":
+        for how in ["left", "right", "outer", "inner"]:
             mask = jmask[how]
             frame = align(out[mask].copy())
             assert mask.all() ^ mask.any() or how == "outer"