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https://github.com/PiBrewing/craftbeerpi4.git
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108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
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"""
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The tests in this package are to ensure the proper resultant dtypes of
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set operations.
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"""
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_dtype_equal
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import pandas as pd
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from pandas import Float64Index, Int64Index, RangeIndex, UInt64Index
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import pandas._testing as tm
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from pandas.api.types import pandas_dtype
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COMPATIBLE_INCONSISTENT_PAIRS = {
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(Int64Index, RangeIndex): (tm.makeIntIndex, tm.makeRangeIndex),
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(Float64Index, Int64Index): (tm.makeFloatIndex, tm.makeIntIndex),
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(Float64Index, RangeIndex): (tm.makeFloatIndex, tm.makeIntIndex),
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(Float64Index, UInt64Index): (tm.makeFloatIndex, tm.makeUIntIndex),
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}
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def test_union_same_types(index):
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# Union with a non-unique, non-monotonic index raises error
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# Only needed for bool index factory
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idx1 = index.sort_values()
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idx2 = index.sort_values()
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assert idx1.union(idx2).dtype == idx1.dtype
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def test_union_different_types(index, index_fixture2):
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# This test only considers combinations of indices
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# GH 23525
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idx1, idx2 = index, index_fixture2
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type_pair = tuple(sorted([type(idx1), type(idx2)], key=lambda x: str(x)))
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if type_pair in COMPATIBLE_INCONSISTENT_PAIRS:
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pytest.xfail("This test only considers non compatible indexes.")
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if any(isinstance(idx, pd.MultiIndex) for idx in (idx1, idx2)):
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pytest.xfail("This test doesn't consider multiindixes.")
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if is_dtype_equal(idx1.dtype, idx2.dtype):
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pytest.xfail("This test only considers non matching dtypes.")
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# A union with a CategoricalIndex (even as dtype('O')) and a
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# non-CategoricalIndex can only be made if both indices are monotonic.
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# This is true before this PR as well.
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# Union with a non-unique, non-monotonic index raises error
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# This applies to the boolean index
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idx1 = idx1.sort_values()
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idx2 = idx2.sort_values()
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assert idx1.union(idx2).dtype == np.dtype("O")
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assert idx2.union(idx1).dtype == np.dtype("O")
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@pytest.mark.parametrize("idx_fact1,idx_fact2", COMPATIBLE_INCONSISTENT_PAIRS.values())
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def test_compatible_inconsistent_pairs(idx_fact1, idx_fact2):
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# GH 23525
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idx1 = idx_fact1(10)
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idx2 = idx_fact2(20)
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res1 = idx1.union(idx2)
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res2 = idx2.union(idx1)
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assert res1.dtype in (idx1.dtype, idx2.dtype)
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assert res2.dtype in (idx1.dtype, idx2.dtype)
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@pytest.mark.parametrize(
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"left, right, expected",
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[
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("int64", "int64", "int64"),
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("int64", "uint64", "object"),
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("int64", "float64", "float64"),
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("uint64", "float64", "float64"),
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("uint64", "uint64", "uint64"),
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("float64", "float64", "float64"),
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("datetime64[ns]", "int64", "object"),
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("datetime64[ns]", "uint64", "object"),
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("datetime64[ns]", "float64", "object"),
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("datetime64[ns, CET]", "int64", "object"),
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("datetime64[ns, CET]", "uint64", "object"),
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("datetime64[ns, CET]", "float64", "object"),
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("Period[D]", "int64", "object"),
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("Period[D]", "uint64", "object"),
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("Period[D]", "float64", "object"),
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],
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)
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def test_union_dtypes(left, right, expected):
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left = pandas_dtype(left)
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right = pandas_dtype(right)
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a = pd.Index([], dtype=left)
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b = pd.Index([], dtype=right)
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result = (a | b).dtype
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assert result == expected
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@pytest.mark.parametrize("values", [[1, 2, 2, 3], [3, 3]])
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def test_intersection_duplicates(values):
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# GH#31326
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a = pd.Index(values)
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b = pd.Index([3, 3])
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result = a.intersection(b)
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expected = pd.Index([3])
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tm.assert_index_equal(result, expected)
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