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| from itertools import product
|
| import numpy as np
| import pytest
|
| from pandas._libs import hashtable
|
| from pandas import (
| NA,
| DatetimeIndex,
| MultiIndex,
| Series,
| )
| import pandas._testing as tm
|
|
| @pytest.mark.parametrize("names", [None, ["first", "second"]])
| def test_unique(names):
| mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]], names=names)
|
| res = mi.unique()
| exp = MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]], names=mi.names)
| tm.assert_index_equal(res, exp)
|
| mi = MultiIndex.from_arrays([list("aaaa"), list("abab")], names=names)
| res = mi.unique()
| exp = MultiIndex.from_arrays([list("aa"), list("ab")], names=mi.names)
| tm.assert_index_equal(res, exp)
|
| mi = MultiIndex.from_arrays([list("aaaa"), list("aaaa")], names=names)
| res = mi.unique()
| exp = MultiIndex.from_arrays([["a"], ["a"]], names=mi.names)
| tm.assert_index_equal(res, exp)
|
| # GH #20568 - empty MI
| mi = MultiIndex.from_arrays([[], []], names=names)
| res = mi.unique()
| tm.assert_index_equal(mi, res)
|
|
| def test_unique_datetimelike():
| idx1 = DatetimeIndex(
| ["2015-01-01", "2015-01-01", "2015-01-01", "2015-01-01", "NaT", "NaT"]
| )
| idx2 = DatetimeIndex(
| ["2015-01-01", "2015-01-01", "2015-01-02", "2015-01-02", "NaT", "2015-01-01"],
| tz="Asia/Tokyo",
| )
| result = MultiIndex.from_arrays([idx1, idx2]).unique()
|
| eidx1 = DatetimeIndex(["2015-01-01", "2015-01-01", "NaT", "NaT"])
| eidx2 = DatetimeIndex(
| ["2015-01-01", "2015-01-02", "NaT", "2015-01-01"], tz="Asia/Tokyo"
| )
| exp = MultiIndex.from_arrays([eidx1, eidx2])
| tm.assert_index_equal(result, exp)
|
|
| @pytest.mark.parametrize("level", [0, "first", 1, "second"])
| def test_unique_level(idx, level):
| # GH #17896 - with level= argument
| result = idx.unique(level=level)
| expected = idx.get_level_values(level).unique()
| tm.assert_index_equal(result, expected)
|
| # With already unique level
| mi = MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]], names=["first", "second"])
| result = mi.unique(level=level)
| expected = mi.get_level_values(level)
| tm.assert_index_equal(result, expected)
|
| # With empty MI
| mi = MultiIndex.from_arrays([[], []], names=["first", "second"])
| result = mi.unique(level=level)
| expected = mi.get_level_values(level)
| tm.assert_index_equal(result, expected)
|
|
| def test_duplicate_multiindex_codes():
| # GH 17464
| # Make sure that a MultiIndex with duplicate levels throws a ValueError
| msg = r"Level values must be unique: \[[A', ]+\] on level 0"
| with pytest.raises(ValueError, match=msg):
| mi = MultiIndex([["A"] * 10, range(10)], [[0] * 10, range(10)])
|
| # And that using set_levels with duplicate levels fails
| mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
| msg = r"Level values must be unique: \[[AB', ]+\] on level 0"
| with pytest.raises(ValueError, match=msg):
| mi.set_levels([["A", "B", "A", "A", "B"], [2, 1, 3, -2, 5]])
|
|
| @pytest.mark.parametrize("names", [["a", "b", "a"], [1, 1, 2], [1, "a", 1]])
| def test_duplicate_level_names(names):
| # GH18872, GH19029
| mi = MultiIndex.from_product([[0, 1]] * 3, names=names)
| assert mi.names == names
|
| # With .rename()
| mi = MultiIndex.from_product([[0, 1]] * 3)
| mi = mi.rename(names)
| assert mi.names == names
|
| # With .rename(., level=)
| mi.rename(names[1], level=1, inplace=True)
| mi = mi.rename([names[0], names[2]], level=[0, 2])
| assert mi.names == names
|
|
| def test_duplicate_meta_data():
| # GH 10115
| mi = MultiIndex(
| levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
| )
|
| for idx in [
| mi,
| mi.set_names([None, None]),
| mi.set_names([None, "Num"]),
| mi.set_names(["Upper", "Num"]),
| ]:
| assert idx.has_duplicates
| assert idx.drop_duplicates().names == idx.names
|
|
| def test_has_duplicates(idx, idx_dup):
| # see fixtures
| assert idx.is_unique is True
| assert idx.has_duplicates is False
| assert idx_dup.is_unique is False
| assert idx_dup.has_duplicates is True
|
| mi = MultiIndex(
| levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
| )
| assert mi.is_unique is False
| assert mi.has_duplicates is True
|
| # single instance of NaN
| mi_nan = MultiIndex(
| levels=[["a", "b"], [0, 1]], codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]]
| )
| assert mi_nan.is_unique is True
| assert mi_nan.has_duplicates is False
|
| # multiple instances of NaN
| mi_nan_dup = MultiIndex(
| levels=[["a", "b"], [0, 1]], codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]]
| )
| assert mi_nan_dup.is_unique is False
| assert mi_nan_dup.has_duplicates is True
|
|
| def test_has_duplicates_from_tuples():
| # GH 9075
| t = [
| ("x", "out", "z", 5, "y", "in", "z", 169),
| ("x", "out", "z", 7, "y", "in", "z", 119),
| ("x", "out", "z", 9, "y", "in", "z", 135),
| ("x", "out", "z", 13, "y", "in", "z", 145),
| ("x", "out", "z", 14, "y", "in", "z", 158),
| ("x", "out", "z", 16, "y", "in", "z", 122),
| ("x", "out", "z", 17, "y", "in", "z", 160),
| ("x", "out", "z", 18, "y", "in", "z", 180),
| ("x", "out", "z", 20, "y", "in", "z", 143),
| ("x", "out", "z", 21, "y", "in", "z", 128),
| ("x", "out", "z", 22, "y", "in", "z", 129),
| ("x", "out", "z", 25, "y", "in", "z", 111),
| ("x", "out", "z", 28, "y", "in", "z", 114),
| ("x", "out", "z", 29, "y", "in", "z", 121),
| ("x", "out", "z", 31, "y", "in", "z", 126),
| ("x", "out", "z", 32, "y", "in", "z", 155),
| ("x", "out", "z", 33, "y", "in", "z", 123),
| ("x", "out", "z", 12, "y", "in", "z", 144),
| ]
|
| mi = MultiIndex.from_tuples(t)
| assert not mi.has_duplicates
|
|
| @pytest.mark.parametrize("nlevels", [4, 8])
| @pytest.mark.parametrize("with_nulls", [True, False])
| def test_has_duplicates_overflow(nlevels, with_nulls):
| # handle int64 overflow if possible
| # no overflow with 4
| # overflow possible with 8
| codes = np.tile(np.arange(500), 2)
| level = np.arange(500)
|
| if with_nulls: # inject some null values
| codes[500] = -1 # common nan value
| codes = [codes.copy() for i in range(nlevels)]
| for i in range(nlevels):
| codes[i][500 + i - nlevels // 2] = -1
|
| codes += [np.array([-1, 1]).repeat(500)]
| else:
| codes = [codes] * nlevels + [np.arange(2).repeat(500)]
|
| levels = [level] * nlevels + [[0, 1]]
|
| # no dups
| mi = MultiIndex(levels=levels, codes=codes)
| assert not mi.has_duplicates
|
| # with a dup
| if with_nulls:
|
| def f(a):
| return np.insert(a, 1000, a[0])
|
| codes = list(map(f, codes))
| mi = MultiIndex(levels=levels, codes=codes)
| else:
| values = mi.values.tolist()
| mi = MultiIndex.from_tuples(values + [values[0]])
|
| assert mi.has_duplicates
|
|
| @pytest.mark.parametrize(
| "keep, expected",
| [
| ("first", np.array([False, False, False, True, True, False])),
| ("last", np.array([False, True, True, False, False, False])),
| (False, np.array([False, True, True, True, True, False])),
| ],
| )
| def test_duplicated(idx_dup, keep, expected):
| result = idx_dup.duplicated(keep=keep)
| tm.assert_numpy_array_equal(result, expected)
|
|
| @pytest.mark.arm_slow
| def test_duplicated_large(keep):
| # GH 9125
| n, k = 200, 5000
| levels = [np.arange(n), tm.makeStringIndex(n), 1000 + np.arange(n)]
| codes = [np.random.choice(n, k * n) for lev in levels]
| mi = MultiIndex(levels=levels, codes=codes)
|
| result = mi.duplicated(keep=keep)
| expected = hashtable.duplicated(mi.values, keep=keep)
| tm.assert_numpy_array_equal(result, expected)
|
|
| def test_duplicated2():
| # TODO: more informative test name
| # GH5873
| for a in [101, 102]:
| mi = MultiIndex.from_arrays([[101, a], [3.5, np.nan]])
| assert not mi.has_duplicates
|
| tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(2, dtype="bool"))
|
| for n in range(1, 6): # 1st level shape
| for m in range(1, 5): # 2nd level shape
| # all possible unique combinations, including nan
| codes = product(range(-1, n), range(-1, m))
| mi = MultiIndex(
| levels=[list("abcde")[:n], list("WXYZ")[:m]],
| codes=np.random.permutation(list(codes)).T,
| )
| assert len(mi) == (n + 1) * (m + 1)
| assert not mi.has_duplicates
|
| tm.assert_numpy_array_equal(
| mi.duplicated(), np.zeros(len(mi), dtype="bool")
| )
|
|
| def test_duplicated_drop_duplicates():
| # GH#4060
| idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
|
| expected = np.array([False, False, False, True, False, False], dtype=bool)
| duplicated = idx.duplicated()
| tm.assert_numpy_array_equal(duplicated, expected)
| assert duplicated.dtype == bool
| expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
| tm.assert_index_equal(idx.drop_duplicates(), expected)
|
| expected = np.array([True, False, False, False, False, False])
| duplicated = idx.duplicated(keep="last")
| tm.assert_numpy_array_equal(duplicated, expected)
| assert duplicated.dtype == bool
| expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
| tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected)
|
| expected = np.array([True, False, False, True, False, False])
| duplicated = idx.duplicated(keep=False)
| tm.assert_numpy_array_equal(duplicated, expected)
| assert duplicated.dtype == bool
| expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
| tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
|
|
| @pytest.mark.parametrize(
| "dtype",
| [
| np.complex64,
| np.complex128,
| ],
| )
| def test_duplicated_series_complex_numbers(dtype):
| # GH 17927
| expected = Series(
| [False, False, False, True, False, False, False, True, False, True],
| dtype=bool,
| )
| result = Series(
| [
| np.nan + np.nan * 1j,
| 0,
| 1j,
| 1j,
| 1,
| 1 + 1j,
| 1 + 2j,
| 1 + 1j,
| np.nan,
| np.nan + np.nan * 1j,
| ],
| dtype=dtype,
| ).duplicated()
| tm.assert_series_equal(result, expected)
|
|
| def test_midx_unique_ea_dtype():
| # GH#48335
| vals_a = Series([1, 2, NA, NA], dtype="Int64")
| vals_b = np.array([1, 2, 3, 3])
| midx = MultiIndex.from_arrays([vals_a, vals_b], names=["a", "b"])
| result = midx.unique()
|
| exp_vals_a = Series([1, 2, NA], dtype="Int64")
| exp_vals_b = np.array([1, 2, 3])
| expected = MultiIndex.from_arrays([exp_vals_a, exp_vals_b], names=["a", "b"])
| tm.assert_index_equal(result, expected)
|
|
