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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)
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