import numpy as np
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import pytest
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from pandas._libs import index as libindex
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import pandas as pd
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from pandas import (
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Index,
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NaT,
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)
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import pandas._testing as tm
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class TestGetSliceBounds:
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@pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
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def test_get_slice_bounds_within(self, side, expected):
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index = Index(list("abcdef"))
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result = index.get_slice_bound("e", side=side)
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assert result == expected
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@pytest.mark.parametrize("side", ["left", "right"])
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@pytest.mark.parametrize(
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"data, bound, expected", [(list("abcdef"), "x", 6), (list("bcdefg"), "a", 0)]
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)
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def test_get_slice_bounds_outside(self, side, expected, data, bound):
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index = Index(data)
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result = index.get_slice_bound(bound, side=side)
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assert result == expected
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def test_get_slice_bounds_invalid_side(self):
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with pytest.raises(ValueError, match="Invalid value for side kwarg"):
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Index([]).get_slice_bound("a", side="middle")
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class TestGetIndexerNonUnique:
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def test_get_indexer_non_unique_dtype_mismatch(self):
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# GH#25459
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indexes, missing = Index(["A", "B"]).get_indexer_non_unique(Index([0]))
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tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes)
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tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing)
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@pytest.mark.parametrize(
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"idx_values,idx_non_unique",
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[
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([np.nan, 100, 200, 100], [np.nan, 100]),
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([np.nan, 100.0, 200.0, 100.0], [np.nan, 100.0]),
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],
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)
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def test_get_indexer_non_unique_int_index(self, idx_values, idx_non_unique):
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indexes, missing = Index(idx_values).get_indexer_non_unique(Index([np.nan]))
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tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes)
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tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
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indexes, missing = Index(idx_values).get_indexer_non_unique(
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Index(idx_non_unique)
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)
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tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes)
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tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing)
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class TestGetLoc:
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@pytest.mark.slow # to_flat_index takes a while
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def test_get_loc_tuple_monotonic_above_size_cutoff(self, monkeypatch):
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# Go through the libindex path for which using
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# _bin_search vs ndarray.searchsorted makes a difference
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with monkeypatch.context():
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monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 100)
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lev = list("ABCD")
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dti = pd.date_range("2016-01-01", periods=10)
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mi = pd.MultiIndex.from_product([lev, range(5), dti])
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oidx = mi.to_flat_index()
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loc = len(oidx) // 2
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tup = oidx[loc]
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res = oidx.get_loc(tup)
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assert res == loc
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def test_get_loc_nan_object_dtype_nonmonotonic_nonunique(self):
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# case that goes through _maybe_get_bool_indexer
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idx = Index(["foo", np.nan, None, "foo", 1.0, None], dtype=object)
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# we dont raise KeyError on nan
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res = idx.get_loc(np.nan)
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assert res == 1
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# we only match on None, not on np.nan
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res = idx.get_loc(None)
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expected = np.array([False, False, True, False, False, True])
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tm.assert_numpy_array_equal(res, expected)
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# we don't match at all on mismatched NA
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with pytest.raises(KeyError, match="NaT"):
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idx.get_loc(NaT)
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def test_getitem_boolean_ea_indexer():
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# GH#45806
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ser = pd.Series([True, False, pd.NA], dtype="boolean")
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result = ser.index[ser]
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expected = Index([0])
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tm.assert_index_equal(result, expected)
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