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| import numpy as np
| import pytest
|
| import pandas.util._test_decorators as td
|
| from pandas import (
| Categorical,
| DataFrame,
| )
|
| # _is_homogeneous_type always returns True for ArrayManager
| pytestmark = td.skip_array_manager_invalid_test
|
|
| @pytest.mark.parametrize(
| "data, expected",
| [
| # empty
| (DataFrame(), True),
| # multi-same
| (DataFrame({"A": [1, 2], "B": [1, 2]}), True),
| # multi-object
| (
| DataFrame(
| {
| "A": np.array([1, 2], dtype=object),
| "B": np.array(["a", "b"], dtype=object),
| }
| ),
| True,
| ),
| # multi-extension
| (
| DataFrame({"A": Categorical(["a", "b"]), "B": Categorical(["a", "b"])}),
| True,
| ),
| # differ types
| (DataFrame({"A": [1, 2], "B": [1.0, 2.0]}), False),
| # differ sizes
| (
| DataFrame(
| {
| "A": np.array([1, 2], dtype=np.int32),
| "B": np.array([1, 2], dtype=np.int64),
| }
| ),
| False,
| ),
| # multi-extension differ
| (
| DataFrame({"A": Categorical(["a", "b"]), "B": Categorical(["b", "c"])}),
| False,
| ),
| ],
| )
| def test_is_homogeneous_type(data, expected):
| assert data._is_homogeneous_type is expected
|
|
