Updated script that can be controled by Nodejs web app

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

@@ -0,0 +1,141 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
+def test_compare_axis(align_axis):
+    # GH#30429
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", "z"])
+
+    result = s1.compare(s2, align_axis=align_axis)
+
+    if align_axis in (1, "columns"):
+        indices = pd.Index([0, 2])
+        columns = pd.Index(["self", "other"])
+        expected = pd.DataFrame(
+            [["a", "x"], ["c", "z"]], index=indices, columns=columns
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]])
+        expected = pd.Series(["a", "x", "c", "z"], index=indices)
+        tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keep_shape, keep_equal",
+    [
+        (True, False),
+        (False, True),
+        (True, True),
+        # False, False case is already covered in test_compare_axis
+    ],
+)
+def test_compare_various_formats(keep_shape, keep_equal):
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", "z"])
+
+    result = s1.compare(s2, keep_shape=keep_shape, keep_equal=keep_equal)
+
+    if keep_shape:
+        indices = pd.Index([0, 1, 2])
+        columns = pd.Index(["self", "other"])
+        if keep_equal:
+            expected = pd.DataFrame(
+                [["a", "x"], ["b", "b"], ["c", "z"]], index=indices, columns=columns
+            )
+        else:
+            expected = pd.DataFrame(
+                [["a", "x"], [np.nan, np.nan], ["c", "z"]],
+                index=indices,
+                columns=columns,
+            )
+    else:
+        indices = pd.Index([0, 2])
+        columns = pd.Index(["self", "other"])
+        expected = pd.DataFrame(
+            [["a", "x"], ["c", "z"]], index=indices, columns=columns
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_compare_with_equal_nulls():
+    # We want to make sure two NaNs are considered the same
+    # and dropped where applicable
+    s1 = pd.Series(["a", "b", np.nan])
+    s2 = pd.Series(["x", "b", np.nan])
+
+    result = s1.compare(s2)
+    expected = pd.DataFrame([["a", "x"]], columns=["self", "other"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_compare_with_non_equal_nulls():
+    # We want to make sure the relevant NaNs do not get dropped
+    s1 = pd.Series(["a", "b", "c"])
+    s2 = pd.Series(["x", "b", np.nan])
+
+    result = s1.compare(s2, align_axis=0)
+
+    indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]])
+    expected = pd.Series(["a", "x", "c", np.nan], index=indices)
+    tm.assert_series_equal(result, expected)
+
+
+def test_compare_multi_index():
+    index = pd.MultiIndex.from_arrays([[0, 0, 1], [0, 1, 2]])
+    s1 = pd.Series(["a", "b", "c"], index=index)
+    s2 = pd.Series(["x", "b", "z"], index=index)
+
+    result = s1.compare(s2, align_axis=0)
+
+    indices = pd.MultiIndex.from_arrays(
+        [[0, 0, 1, 1], [0, 0, 2, 2], ["self", "other", "self", "other"]]
+    )
+    expected = pd.Series(["a", "x", "c", "z"], index=indices)
+    tm.assert_series_equal(result, expected)
+
+
+def test_compare_unaligned_objects():
+    # test Series with different indices
+    msg = "Can only compare identically-labeled Series objects"
+    with pytest.raises(ValueError, match=msg):
+        ser1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
+        ser2 = pd.Series([1, 2, 3], index=["a", "b", "d"])
+        ser1.compare(ser2)
+
+    # test Series with different lengths
+    msg = "Can only compare identically-labeled Series objects"
+    with pytest.raises(ValueError, match=msg):
+        ser1 = pd.Series([1, 2, 3])
+        ser2 = pd.Series([1, 2, 3, 4])
+        ser1.compare(ser2)
+
+
+def test_compare_datetime64_and_string():
+    # Issue https://github.com/pandas-dev/pandas/issues/45506
+    # Catch OverflowError when comparing datetime64 and string
+    data = [
+        {"a": "2015-07-01", "b": "08335394550"},
+        {"a": "2015-07-02", "b": "+49 (0) 0345 300033"},
+        {"a": "2015-07-03", "b": "+49(0)2598 04457"},
+        {"a": "2015-07-04", "b": "0741470003"},
+        {"a": "2015-07-05", "b": "04181 83668"},
+    ]
+    dtypes = {"a": "datetime64[ns]", "b": "string"}
+    df = pd.DataFrame(data=data).astype(dtypes)
+
+    result_eq1 = df["a"].eq(df["b"])
+    result_eq2 = df["a"] == df["b"]
+    result_neq = df["a"] != df["b"]
+
+    expected_eq = pd.Series([False] * 5)  # For .eq and ==
+    expected_neq = pd.Series([True] * 5)  # For !=
+
+    tm.assert_series_equal(result_eq1, expected_eq)
+    tm.assert_series_equal(result_eq2, expected_eq)
+    tm.assert_series_equal(result_neq, expected_neq)