Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/tests/groupby/test_apply_mutate.py

@@ -0,0 +1,163 @@
+import numpy as np
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_group_by_copy():
+    # GH#44803
+    df = pd.DataFrame(
+        {
+            "name": ["Alice", "Bob", "Carl"],
+            "age": [20, 21, 20],
+        }
+    ).set_index("name")
+
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        grp_by_same_value = df.groupby(["age"], group_keys=False).apply(
+            lambda group: group
+        )
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        grp_by_copy = df.groupby(["age"], group_keys=False).apply(
+            lambda group: group.copy()
+        )
+    tm.assert_frame_equal(grp_by_same_value, grp_by_copy)
+
+
+def test_mutate_groups():
+    # GH3380
+
+    df = pd.DataFrame(
+        {
+            "cat1": ["a"] * 8 + ["b"] * 6,
+            "cat2": ["c"] * 2
+            + ["d"] * 2
+            + ["e"] * 2
+            + ["f"] * 2
+            + ["c"] * 2
+            + ["d"] * 2
+            + ["e"] * 2,
+            "cat3": [f"g{x}" for x in range(1, 15)],
+            "val": np.random.default_rng(2).integers(100, size=14),
+        }
+    )
+
+    def f_copy(x):
+        x = x.copy()
+        x["rank"] = x.val.rank(method="min")
+        return x.groupby("cat2")["rank"].min()
+
+    def f_no_copy(x):
+        x["rank"] = x.val.rank(method="min")
+        return x.groupby("cat2")["rank"].min()
+
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        grpby_copy = df.groupby("cat1").apply(f_copy)
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        grpby_no_copy = df.groupby("cat1").apply(f_no_copy)
+    tm.assert_series_equal(grpby_copy, grpby_no_copy)
+
+
+def test_no_mutate_but_looks_like():
+    # GH 8467
+    # first show's mutation indicator
+    # second does not, but should yield the same results
+    df = pd.DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
+
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        result1 = df.groupby("key", group_keys=True).apply(lambda x: x[:].key)
+    with tm.assert_produces_warning(DeprecationWarning, match=msg):
+        result2 = df.groupby("key", group_keys=True).apply(lambda x: x.key)
+    tm.assert_series_equal(result1, result2)
+
+
+def test_apply_function_with_indexing(warn_copy_on_write):
+    # GH: 33058
+    df = pd.DataFrame(
+        {"col1": ["A", "A", "A", "B", "B", "B"], "col2": [1, 2, 3, 4, 5, 6]}
+    )
+
+    def fn(x):
+        x.loc[x.index[-1], "col2"] = 0
+        return x.col2
+
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(
+        DeprecationWarning, match=msg, raise_on_extra_warnings=not warn_copy_on_write
+    ):
+        result = df.groupby(["col1"], as_index=False).apply(fn)
+    expected = pd.Series(
+        [1, 2, 0, 4, 5, 0],
+        index=pd.MultiIndex.from_tuples(
+            [(0, 0), (0, 1), (0, 2), (1, 3), (1, 4), (1, 5)]
+        ),
+        name="col2",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_mutate_columns_multiindex():
+    # GH 12652
+    df = pd.DataFrame(
+        {
+            ("C", "julian"): [1, 2, 3],
+            ("B", "geoffrey"): [1, 2, 3],
+            ("A", "julian"): [1, 2, 3],
+            ("B", "julian"): [1, 2, 3],
+            ("A", "geoffrey"): [1, 2, 3],
+            ("C", "geoffrey"): [1, 2, 3],
+        },
+        columns=pd.MultiIndex.from_tuples(
+            [
+                ("A", "julian"),
+                ("A", "geoffrey"),
+                ("B", "julian"),
+                ("B", "geoffrey"),
+                ("C", "julian"),
+                ("C", "geoffrey"),
+            ]
+        ),
+    )
+
+    def add_column(grouped):
+        name = grouped.columns[0][1]
+        grouped["sum", name] = grouped.sum(axis=1)
+        return grouped
+
+    msg = "DataFrame.groupby with axis=1 is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        gb = df.groupby(level=1, axis=1)
+    result = gb.apply(add_column)
+    expected = pd.DataFrame(
+        [
+            [1, 1, 1, 3, 1, 1, 1, 3],
+            [2, 2, 2, 6, 2, 2, 2, 6],
+            [
+                3,
+                3,
+                3,
+                9,
+                3,
+                3,
+                3,
+                9,
+            ],
+        ],
+        columns=pd.MultiIndex.from_tuples(
+            [
+                ("geoffrey", "A", "geoffrey"),
+                ("geoffrey", "B", "geoffrey"),
+                ("geoffrey", "C", "geoffrey"),
+                ("geoffrey", "sum", "geoffrey"),
+                ("julian", "A", "julian"),
+                ("julian", "B", "julian"),
+                ("julian", "C", "julian"),
+                ("julian", "sum", "julian"),
+            ]
+        ),
+    )
+    tm.assert_frame_equal(result, expected)