TST: Consolidate groupby any, all tests

pandas/tests/groupby/test_any_all.py

@@ -0,0 +1,70 @@
+import builtins
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    isna,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("agg_func", ["any", "all"])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize(
+    "vals",
+    [
+        ["foo", "bar", "baz"],
+        ["foo", "", ""],
+        ["", "", ""],
+        [1, 2, 3],
+        [1, 0, 0],
+        [0, 0, 0],
+        [1.0, 2.0, 3.0],
+        [1.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [True, True, True],
+        [True, False, False],
+        [False, False, False],
+        [np.nan, np.nan, np.nan],
+    ],
+)
+def test_groupby_bool_aggs(agg_func, skipna, vals):
+    df = DataFrame({"key": ["a"] * 3 + ["b"] * 3, "val": vals * 2})
+
+    # Figure out expectation using Python builtin
+    exp = getattr(builtins, agg_func)(vals)
+
+    # edge case for missing data with skipna and 'any'
+    if skipna and all(isna(vals)) and agg_func == "any":
+        exp = False
+
+    exp_df = DataFrame([exp] * 2, columns=["val"], index=Index(["a", "b"], name="key"))
+    result = getattr(df.groupby("key"), agg_func)(skipna=skipna)
+    tm.assert_frame_equal(result, exp_df)
+
+
+def test_any():
+    df = DataFrame(
+        [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
+        columns=["A", "B", "C"],
+    )
+    expected = DataFrame(
+        [[True, True], [False, True]], columns=["B", "C"], index=[1, 3]
+    )
+    expected.index.name = "A"
+    result = df.groupby("A").any()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+def test_bool_aggs_dup_column_labels(bool_agg_func):
+    # 21668
+    df = DataFrame([[True, True]], columns=["a", "a"])
+    grp_by = df.groupby([0])
+    result = getattr(grp_by, bool_agg_func)()
+
+    expected = df
+    tm.assert_frame_equal(result, expected)

pandas/tests/groupby/test_function.py

@@ -14,7 +14,6 @@
     Series,
     Timestamp,
     date_range,
-    isna,
 )
 import pandas._testing as tm
 import pandas.core.nanops as nanops
@@ -41,41 +40,6 @@ def numpy_dtypes_for_minmax(request):
     return (dtype, min_val, max_val)
 
 
-@pytest.mark.parametrize("agg_func", ["any", "all"])
-@pytest.mark.parametrize("skipna", [True, False])
-@pytest.mark.parametrize(
-    "vals",
-    [
-        ["foo", "bar", "baz"],
-        ["foo", "", ""],
-        ["", "", ""],
-        [1, 2, 3],
-        [1, 0, 0],
-        [0, 0, 0],
-        [1.0, 2.0, 3.0],
-        [1.0, 0.0, 0.0],
-        [0.0, 0.0, 0.0],
-        [True, True, True],
-        [True, False, False],
-        [False, False, False],
-        [np.nan, np.nan, np.nan],
-    ],
-)
-def test_groupby_bool_aggs(agg_func, skipna, vals):
-    df = DataFrame({"key": ["a"] * 3 + ["b"] * 3, "val": vals * 2})
-
-    # Figure out expectation using Python builtin
-    exp = getattr(builtins, agg_func)(vals)
-
-    # edge case for missing data with skipna and 'any'
-    if skipna and all(isna(vals)) and agg_func == "any":
-        exp = False
-
-    exp_df = DataFrame([exp] * 2, columns=["val"], index=Index(["a", "b"], name="key"))
-    result = getattr(df.groupby("key"), agg_func)(skipna=skipna)
-    tm.assert_frame_equal(result, exp_df)
-
-
 def test_max_min_non_numeric():
     # #2700
     aa = DataFrame({"nn": [11, 11, 22, 22], "ii": [1, 2, 3, 4], "ss": 4 * ["mama"]})
@@ -344,14 +308,6 @@ def test_idxmin(self, gb):
         result = gb.idxmin()
         tm.assert_frame_equal(result, expected)
 
-    def test_any(self, gb):
-        expected = DataFrame(
-            [[True, True], [False, True]], columns=["B", "C"], index=[1, 3]
-        )
-        expected.index.name = "A"
-        result = gb.any()
-        tm.assert_frame_equal(result, expected)
-
     def test_mad(self, gb, gni):
         # mad
         expected = DataFrame([[0], [np.nan]], columns=["B"], index=[1, 3])

pandas/tests/groupby/test_groupby.py

@@ -1978,17 +1978,6 @@ def test_groupby_duplicate_index():
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
-def test_bool_aggs_dup_column_labels(bool_agg_func):
-    # 21668
-    df = DataFrame([[True, True]], columns=["a", "a"])
-    grp_by = df.groupby([0])
-    result = getattr(grp_by, bool_agg_func)()
-
-    expected = df
-    tm.assert_frame_equal(result, expected)
-
-
 @pytest.mark.parametrize(
     "idx", [Index(["a", "a"]), MultiIndex.from_tuples((("a", "a"), ("a", "a")))]
 )