针对pulse-transit的工具

dist/client/pandas/tests/arrays/sparse/test_combine_concat.py

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+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays.sparse import SparseArray
+
+
+class TestSparseArrayConcat:
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_basic(self, kind):
+        a = SparseArray([1, 0, 0, 2], kind=kind)
+        b = SparseArray([1, 0, 2, 2], kind=kind)
+
+        result = SparseArray._concat_same_type([a, b])
+        # Can't make any assertions about the sparse index itself
+        # since we aren't don't merge sparse blocs across arrays
+        # in to_concat
+        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
+        tm.assert_numpy_array_equal(result.sp_values, expected)
+        assert result.kind == kind
+
+    @pytest.mark.parametrize("kind", ["integer", "block"])
+    def test_uses_first_kind(self, kind):
+        other = "integer" if kind == "block" else "block"
+        a = SparseArray([1, 0, 0, 2], kind=kind)
+        b = SparseArray([1, 0, 2, 2], kind=other)
+
+        result = SparseArray._concat_same_type([a, b])
+        expected = np.array([1, 2, 1, 2, 2], dtype="int64")
+        tm.assert_numpy_array_equal(result.sp_values, expected)
+        assert result.kind == kind
+
+
+@pytest.mark.parametrize(
+    "other, expected_dtype",
+    [
+        # compatible dtype -> preserve sparse
+        (pd.Series([3, 4, 5], dtype="int64"), pd.SparseDtype("int64", 0)),
+        # (pd.Series([3, 4, 5], dtype="Int64"), pd.SparseDtype("int64", 0)),
+        # incompatible dtype -> Sparse[common dtype]
+        (pd.Series([1.5, 2.5, 3.5], dtype="float64"), pd.SparseDtype("float64", 0)),
+        # incompatible dtype -> Sparse[object] dtype
+        (pd.Series(["a", "b", "c"], dtype=object), pd.SparseDtype(object, 0)),
+        # categorical with compatible categories -> dtype of the categories
+        (pd.Series([3, 4, 5], dtype="category"), np.dtype("int64")),
+        (pd.Series([1.5, 2.5, 3.5], dtype="category"), np.dtype("float64")),
+        # categorical with incompatible categories -> object dtype
+        (pd.Series(["a", "b", "c"], dtype="category"), np.dtype(object)),
+    ],
+)
+def test_concat_with_non_sparse(other, expected_dtype):
+    # https://github.com/pandas-dev/pandas/issues/34336
+    s_sparse = pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0))
+
+    result = pd.concat([s_sparse, other], ignore_index=True)
+    expected = pd.Series(list(s_sparse) + list(other)).astype(expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = pd.concat([other, s_sparse], ignore_index=True)
+    expected = pd.Series(list(other) + list(s_sparse)).astype(expected_dtype)
+    tm.assert_series_equal(result, expected)