190 lines
7.6 KiB
Python
190 lines
7.6 KiB
Python
from decimal import Decimal
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import numpy as np
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import pytest
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from pandas._libs.missing import is_matching_na
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import pandas as pd
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from pandas import Index
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import pandas._testing as tm
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class TestGetIndexer:
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@pytest.mark.parametrize(
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"method,expected",
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[
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("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
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("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
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],
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)
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def test_get_indexer_strings(self, method, expected):
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index = Index(["b", "c"])
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actual = index.get_indexer(["a", "b", "c", "d"], method=method)
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tm.assert_numpy_array_equal(actual, expected)
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def test_get_indexer_strings_raises(self):
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index = Index(["b", "c"])
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msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
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with pytest.raises(TypeError, match=msg):
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index.get_indexer(["a", "b", "c", "d"], method="nearest")
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with pytest.raises(TypeError, match=msg):
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index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
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with pytest.raises(TypeError, match=msg):
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index.get_indexer(
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["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
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)
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def test_get_indexer_with_NA_values(
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self, unique_nulls_fixture, unique_nulls_fixture2
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):
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# GH#22332
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# check pairwise, that no pair of na values
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# is mangled
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if unique_nulls_fixture is unique_nulls_fixture2:
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return # skip it, values are not unique
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arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
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index = Index(arr, dtype=object)
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result = index.get_indexer(
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[unique_nulls_fixture, unique_nulls_fixture2, "Unknown"]
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)
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expected = np.array([0, 1, -1], dtype=np.intp)
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tm.assert_numpy_array_equal(result, expected)
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class TestGetIndexerNonUnique:
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def test_get_indexer_non_unique_nas(self, nulls_fixture):
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# even though this isn't non-unique, this should still work
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index = Index(["a", "b", nulls_fixture])
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indexer, missing = index.get_indexer_non_unique([nulls_fixture])
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expected_indexer = np.array([2], dtype=np.intp)
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expected_missing = np.array([], dtype=np.intp)
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tm.assert_numpy_array_equal(indexer, expected_indexer)
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tm.assert_numpy_array_equal(missing, expected_missing)
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# actually non-unique
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index = Index(["a", nulls_fixture, "b", nulls_fixture])
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indexer, missing = index.get_indexer_non_unique([nulls_fixture])
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expected_indexer = np.array([1, 3], dtype=np.intp)
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tm.assert_numpy_array_equal(indexer, expected_indexer)
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tm.assert_numpy_array_equal(missing, expected_missing)
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# matching-but-not-identical nans
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if is_matching_na(nulls_fixture, float("NaN")):
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index = Index(["a", float("NaN"), "b", float("NaN")])
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match_but_not_identical = True
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elif is_matching_na(nulls_fixture, Decimal("NaN")):
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index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")])
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match_but_not_identical = True
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else:
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match_but_not_identical = False
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if match_but_not_identical:
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indexer, missing = index.get_indexer_non_unique([nulls_fixture])
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expected_indexer = np.array([1, 3], dtype=np.intp)
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tm.assert_numpy_array_equal(indexer, expected_indexer)
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tm.assert_numpy_array_equal(missing, expected_missing)
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@pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
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def test_get_indexer_non_unique_np_nats(self, np_nat_fixture, np_nat_fixture2):
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expected_missing = np.array([], dtype=np.intp)
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# matching-but-not-identical nats
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if is_matching_na(np_nat_fixture, np_nat_fixture2):
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# ensure nats are different objects
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index = Index(
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np.array(
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["2021-10-02", np_nat_fixture.copy(), np_nat_fixture2.copy()],
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dtype=object,
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),
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dtype=object,
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)
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# pass as index to prevent target from being casted to DatetimeIndex
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indexer, missing = index.get_indexer_non_unique(
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Index([np_nat_fixture], dtype=object)
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)
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expected_indexer = np.array([1, 2], dtype=np.intp)
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tm.assert_numpy_array_equal(indexer, expected_indexer)
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tm.assert_numpy_array_equal(missing, expected_missing)
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# dt64nat vs td64nat
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else:
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try:
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np_nat_fixture == np_nat_fixture2
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except (TypeError, OverflowError):
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# Numpy will raise on uncomparable types, like
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# np.datetime64('NaT', 'Y') and np.datetime64('NaT', 'ps')
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# https://github.com/numpy/numpy/issues/22762
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return
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index = Index(
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np.array(
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[
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"2021-10-02",
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np_nat_fixture,
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np_nat_fixture2,
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np_nat_fixture,
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np_nat_fixture2,
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],
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dtype=object,
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),
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dtype=object,
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)
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# pass as index to prevent target from being casted to DatetimeIndex
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indexer, missing = index.get_indexer_non_unique(
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Index([np_nat_fixture], dtype=object)
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)
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expected_indexer = np.array([1, 3], dtype=np.intp)
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tm.assert_numpy_array_equal(indexer, expected_indexer)
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tm.assert_numpy_array_equal(missing, expected_missing)
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class TestSliceLocs:
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@pytest.mark.parametrize(
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"in_slice,expected",
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[
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# error: Slice index must be an integer or None
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(pd.IndexSlice[::-1], "yxdcb"),
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(pd.IndexSlice["b":"y":-1], ""), # type: ignore[misc]
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(pd.IndexSlice["b"::-1], "b"), # type: ignore[misc]
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(pd.IndexSlice[:"b":-1], "yxdcb"), # type: ignore[misc]
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(pd.IndexSlice[:"y":-1], "y"), # type: ignore[misc]
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(pd.IndexSlice["y"::-1], "yxdcb"), # type: ignore[misc]
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(pd.IndexSlice["y"::-4], "yb"), # type: ignore[misc]
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# absent labels
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(pd.IndexSlice[:"a":-1], "yxdcb"), # type: ignore[misc]
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(pd.IndexSlice[:"a":-2], "ydb"), # type: ignore[misc]
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(pd.IndexSlice["z"::-1], "yxdcb"), # type: ignore[misc]
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(pd.IndexSlice["z"::-3], "yc"), # type: ignore[misc]
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(pd.IndexSlice["m"::-1], "dcb"), # type: ignore[misc]
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(pd.IndexSlice[:"m":-1], "yx"), # type: ignore[misc]
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(pd.IndexSlice["a":"a":-1], ""), # type: ignore[misc]
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(pd.IndexSlice["z":"z":-1], ""), # type: ignore[misc]
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(pd.IndexSlice["m":"m":-1], ""), # type: ignore[misc]
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],
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)
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def test_slice_locs_negative_step(self, in_slice, expected):
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index = Index(list("bcdxy"))
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s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
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result = index[s_start : s_stop : in_slice.step]
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expected = Index(list(expected))
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tm.assert_index_equal(result, expected)
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def test_slice_locs_dup(self):
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index = Index(["a", "a", "b", "c", "d", "d"])
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assert index.slice_locs("a", "d") == (0, 6)
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assert index.slice_locs(end="d") == (0, 6)
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assert index.slice_locs("a", "c") == (0, 4)
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assert index.slice_locs("b", "d") == (2, 6)
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index2 = index[::-1]
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assert index2.slice_locs("d", "a") == (0, 6)
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assert index2.slice_locs(end="a") == (0, 6)
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assert index2.slice_locs("d", "b") == (0, 4)
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assert index2.slice_locs("c", "a") == (2, 6)
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