Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/tests/series/methods/test_unstack.py

@@ -0,0 +1,169 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_unstack_preserves_object():
+    mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])
+
+    ser = Series(np.arange(4.0), index=mi, dtype=object)
+
+    res1 = ser.unstack()
+    assert (res1.dtypes == object).all()
+
+    res2 = ser.unstack(level=0)
+    assert (res2.dtypes == object).all()
+
+
+def test_unstack():
+    index = MultiIndex(
+        levels=[["bar", "foo"], ["one", "three", "two"]],
+        codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
+    )
+
+    s = Series(np.arange(4.0), index=index)
+    unstacked = s.unstack()
+
+    expected = DataFrame(
+        [[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],
+        index=["bar", "foo"],
+        columns=["one", "three", "two"],
+    )
+
+    tm.assert_frame_equal(unstacked, expected)
+
+    unstacked = s.unstack(level=0)
+    tm.assert_frame_equal(unstacked, expected.T)
+
+    index = MultiIndex(
+        levels=[["bar"], ["one", "two", "three"], [0, 1]],
+        codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    s = Series(np.random.default_rng(2).standard_normal(6), index=index)
+    exp_index = MultiIndex(
+        levels=[["one", "two", "three"], [0, 1]],
+        codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
+    unstacked = s.unstack(0).sort_index()
+    tm.assert_frame_equal(unstacked, expected)
+
+    # GH5873
+    idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
+    ts = Series([1, 2], index=idx)
+    left = ts.unstack()
+    right = DataFrame(
+        [[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5]
+    )
+    tm.assert_frame_equal(left, right)
+
+    idx = MultiIndex.from_arrays(
+        [
+            ["cat", "cat", "cat", "dog", "dog"],
+            ["a", "a", "b", "a", "b"],
+            [1, 2, 1, 1, np.nan],
+        ]
+    )
+    ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
+    right = DataFrame(
+        [[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],
+        columns=["cat", "dog"],
+    )
+    tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]
+    right.index = MultiIndex.from_tuples(tpls)
+    tm.assert_frame_equal(ts.unstack(level=0), right)
+
+
+def test_unstack_tuplename_in_multiindex():
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(("A", "a"))
+
+    expected = DataFrame(
+        [[1, 1, 1], [1, 1, 1], [1, 1, 1]],
+        columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),
+        index=Index([1, 2, 3], name=("B", "b")),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "unstack_idx, expected_values, expected_index, expected_columns",
+    [
+        (
+            ("A", "a"),
+            [[1, 1], [1, 1], [1, 1], [1, 1]],
+            MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]),
+            MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]),
+        ),
+        (
+            (("A", "a"), "B"),
+            [[1, 1, 1, 1], [1, 1, 1, 1]],
+            Index([3, 4], name="C"),
+            MultiIndex.from_tuples(
+                [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]
+            ),
+        ),
+    ],
+)
+def test_unstack_mixed_type_name_in_multiindex(
+    unstack_idx, expected_values, expected_index, expected_columns
+):
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(unstack_idx)
+
+    expected = DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_multi_index_categorical_values():
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        columns=Index(list("ABCD"), dtype=object),
+        index=date_range("2000-01-01", periods=10, freq="B"),
+    )
+    mi = df.stack(future_stack=True).index.rename(["major", "minor"])
+    ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")
+
+    result = ser.unstack()
+
+    dti = ser.index.levels[0]
+    c = pd.Categorical(["foo"] * len(dti))
+    expected = DataFrame(
+        {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
+        columns=Index(list("ABCD"), name="minor"),
+        index=dti.rename("major"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_mixed_level_names():
+    # GH#48763
+    arrays = [["a", "a"], [1, 2], ["red", "blue"]]
+    idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
+    ser = Series([1, 2], index=idx)
+    result = ser.unstack("x")
+    expected = DataFrame(
+        [[1], [2]],
+        columns=Index(["a"], name="x"),
+        index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
+    )
+    tm.assert_frame_equal(result, expected)