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339 lines
11 KiB
339 lines
11 KiB
from itertools import product
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import numpy as np
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import pytest
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from pandas._libs import hashtable
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from pandas import (
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DatetimeIndex,
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MultiIndex,
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Series,
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)
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import pandas._testing as tm
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@pytest.mark.parametrize("names", [None, ["first", "second"]])
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def test_unique(names):
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mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]], names=names)
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res = mi.unique()
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exp = MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]], names=mi.names)
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tm.assert_index_equal(res, exp)
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mi = MultiIndex.from_arrays([list("aaaa"), list("abab")], names=names)
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res = mi.unique()
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exp = MultiIndex.from_arrays([list("aa"), list("ab")], names=mi.names)
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tm.assert_index_equal(res, exp)
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mi = MultiIndex.from_arrays([list("aaaa"), list("aaaa")], names=names)
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res = mi.unique()
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exp = MultiIndex.from_arrays([["a"], ["a"]], names=mi.names)
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tm.assert_index_equal(res, exp)
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# GH #20568 - empty MI
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mi = MultiIndex.from_arrays([[], []], names=names)
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res = mi.unique()
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tm.assert_index_equal(mi, res)
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def test_unique_datetimelike():
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idx1 = DatetimeIndex(
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["2015-01-01", "2015-01-01", "2015-01-01", "2015-01-01", "NaT", "NaT"]
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)
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idx2 = DatetimeIndex(
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["2015-01-01", "2015-01-01", "2015-01-02", "2015-01-02", "NaT", "2015-01-01"],
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tz="Asia/Tokyo",
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)
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result = MultiIndex.from_arrays([idx1, idx2]).unique()
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eidx1 = DatetimeIndex(["2015-01-01", "2015-01-01", "NaT", "NaT"])
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eidx2 = DatetimeIndex(
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["2015-01-01", "2015-01-02", "NaT", "2015-01-01"], tz="Asia/Tokyo"
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)
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exp = MultiIndex.from_arrays([eidx1, eidx2])
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tm.assert_index_equal(result, exp)
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@pytest.mark.parametrize("level", [0, "first", 1, "second"])
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def test_unique_level(idx, level):
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# GH #17896 - with level= argument
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result = idx.unique(level=level)
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expected = idx.get_level_values(level).unique()
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tm.assert_index_equal(result, expected)
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# With already unique level
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mi = MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]], names=["first", "second"])
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result = mi.unique(level=level)
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expected = mi.get_level_values(level)
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tm.assert_index_equal(result, expected)
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# With empty MI
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mi = MultiIndex.from_arrays([[], []], names=["first", "second"])
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result = mi.unique(level=level)
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expected = mi.get_level_values(level)
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tm.assert_index_equal(result, expected)
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def test_duplicate_multiindex_codes():
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# GH 17464
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# Make sure that a MultiIndex with duplicate levels throws a ValueError
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msg = r"Level values must be unique: \[[A', ]+\] on level 0"
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with pytest.raises(ValueError, match=msg):
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mi = MultiIndex([["A"] * 10, range(10)], [[0] * 10, range(10)])
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# And that using set_levels with duplicate levels fails
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mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
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msg = r"Level values must be unique: \[[AB', ]+\] on level 0"
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with pytest.raises(ValueError, match=msg):
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with tm.assert_produces_warning(FutureWarning):
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mi.set_levels([["A", "B", "A", "A", "B"], [2, 1, 3, -2, 5]], inplace=True)
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@pytest.mark.parametrize("names", [["a", "b", "a"], [1, 1, 2], [1, "a", 1]])
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def test_duplicate_level_names(names):
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# GH18872, GH19029
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mi = MultiIndex.from_product([[0, 1]] * 3, names=names)
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assert mi.names == names
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# With .rename()
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mi = MultiIndex.from_product([[0, 1]] * 3)
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mi = mi.rename(names)
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assert mi.names == names
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# With .rename(., level=)
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mi.rename(names[1], level=1, inplace=True)
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mi = mi.rename([names[0], names[2]], level=[0, 2])
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assert mi.names == names
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def test_duplicate_meta_data():
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# GH 10115
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mi = MultiIndex(
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levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
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)
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for idx in [
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mi,
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mi.set_names([None, None]),
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mi.set_names([None, "Num"]),
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mi.set_names(["Upper", "Num"]),
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]:
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assert idx.has_duplicates
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assert idx.drop_duplicates().names == idx.names
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def test_has_duplicates(idx, idx_dup):
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# see fixtures
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assert idx.is_unique is True
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assert idx.has_duplicates is False
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assert idx_dup.is_unique is False
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assert idx_dup.has_duplicates is True
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mi = MultiIndex(
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levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
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)
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assert mi.is_unique is False
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assert mi.has_duplicates is True
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# single instance of NaN
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mi_nan = MultiIndex(
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levels=[["a", "b"], [0, 1]], codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]]
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)
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assert mi_nan.is_unique is True
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assert mi_nan.has_duplicates is False
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# multiple instances of NaN
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mi_nan_dup = MultiIndex(
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levels=[["a", "b"], [0, 1]], codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]]
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)
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assert mi_nan_dup.is_unique is False
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assert mi_nan_dup.has_duplicates is True
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def test_has_duplicates_from_tuples():
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# GH 9075
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t = [
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("x", "out", "z", 5, "y", "in", "z", 169),
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("x", "out", "z", 7, "y", "in", "z", 119),
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("x", "out", "z", 9, "y", "in", "z", 135),
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("x", "out", "z", 13, "y", "in", "z", 145),
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("x", "out", "z", 14, "y", "in", "z", 158),
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("x", "out", "z", 16, "y", "in", "z", 122),
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("x", "out", "z", 17, "y", "in", "z", 160),
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("x", "out", "z", 18, "y", "in", "z", 180),
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("x", "out", "z", 20, "y", "in", "z", 143),
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("x", "out", "z", 21, "y", "in", "z", 128),
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("x", "out", "z", 22, "y", "in", "z", 129),
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("x", "out", "z", 25, "y", "in", "z", 111),
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("x", "out", "z", 28, "y", "in", "z", 114),
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("x", "out", "z", 29, "y", "in", "z", 121),
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("x", "out", "z", 31, "y", "in", "z", 126),
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("x", "out", "z", 32, "y", "in", "z", 155),
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("x", "out", "z", 33, "y", "in", "z", 123),
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("x", "out", "z", 12, "y", "in", "z", 144),
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]
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mi = MultiIndex.from_tuples(t)
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assert not mi.has_duplicates
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@pytest.mark.parametrize("nlevels", [4, 8])
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@pytest.mark.parametrize("with_nulls", [True, False])
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def test_has_duplicates_overflow(nlevels, with_nulls):
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# handle int64 overflow if possible
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# no overflow with 4
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# overflow possible with 8
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codes = np.tile(np.arange(500), 2)
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level = np.arange(500)
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if with_nulls: # inject some null values
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codes[500] = -1 # common nan value
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codes = [codes.copy() for i in range(nlevels)]
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for i in range(nlevels):
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codes[i][500 + i - nlevels // 2] = -1
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codes += [np.array([-1, 1]).repeat(500)]
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else:
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codes = [codes] * nlevels + [np.arange(2).repeat(500)]
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levels = [level] * nlevels + [[0, 1]]
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# no dups
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mi = MultiIndex(levels=levels, codes=codes)
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assert not mi.has_duplicates
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# with a dup
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if with_nulls:
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def f(a):
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return np.insert(a, 1000, a[0])
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codes = list(map(f, codes))
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mi = MultiIndex(levels=levels, codes=codes)
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else:
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values = mi.values.tolist()
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mi = MultiIndex.from_tuples(values + [values[0]])
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assert mi.has_duplicates
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@pytest.mark.parametrize(
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"keep, expected",
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[
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("first", np.array([False, False, False, True, True, False])),
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("last", np.array([False, True, True, False, False, False])),
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(False, np.array([False, True, True, True, True, False])),
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],
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)
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def test_duplicated(idx_dup, keep, expected):
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result = idx_dup.duplicated(keep=keep)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.arm_slow
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def test_duplicated_large(keep):
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# GH 9125
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n, k = 200, 5000
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levels = [np.arange(n), tm.makeStringIndex(n), 1000 + np.arange(n)]
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codes = [np.random.choice(n, k * n) for lev in levels]
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mi = MultiIndex(levels=levels, codes=codes)
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result = mi.duplicated(keep=keep)
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expected = hashtable.duplicated(mi.values, keep=keep)
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tm.assert_numpy_array_equal(result, expected)
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def test_duplicated2():
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# TODO: more informative test name
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# GH5873
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for a in [101, 102]:
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mi = MultiIndex.from_arrays([[101, a], [3.5, np.nan]])
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assert not mi.has_duplicates
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tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(2, dtype="bool"))
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for n in range(1, 6): # 1st level shape
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for m in range(1, 5): # 2nd level shape
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# all possible unique combinations, including nan
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codes = product(range(-1, n), range(-1, m))
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mi = MultiIndex(
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levels=[list("abcde")[:n], list("WXYZ")[:m]],
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codes=np.random.permutation(list(codes)).T,
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)
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assert len(mi) == (n + 1) * (m + 1)
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assert not mi.has_duplicates
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tm.assert_numpy_array_equal(
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mi.duplicated(), np.zeros(len(mi), dtype="bool")
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)
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def test_duplicated_drop_duplicates():
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# GH#4060
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idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
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expected = np.array([False, False, False, True, False, False], dtype=bool)
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duplicated = idx.duplicated()
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tm.assert_numpy_array_equal(duplicated, expected)
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assert duplicated.dtype == bool
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expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
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tm.assert_index_equal(idx.drop_duplicates(), expected)
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expected = np.array([True, False, False, False, False, False])
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duplicated = idx.duplicated(keep="last")
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tm.assert_numpy_array_equal(duplicated, expected)
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assert duplicated.dtype == bool
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expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
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tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected)
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expected = np.array([True, False, False, True, False, False])
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duplicated = idx.duplicated(keep=False)
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tm.assert_numpy_array_equal(duplicated, expected)
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assert duplicated.dtype == bool
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expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
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tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
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@pytest.mark.parametrize(
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"dtype",
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[
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np.complex64,
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np.complex128,
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],
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)
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def test_duplicated_series_complex_numbers(dtype):
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# GH 17927
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expected = Series(
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[False, False, False, True, False, False, False, True, False, True],
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dtype=bool,
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)
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result = Series(
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[
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np.nan + np.nan * 1j,
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0,
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1j,
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1j,
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1,
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1 + 1j,
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1 + 2j,
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1 + 1j,
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np.nan,
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np.nan + np.nan * 1j,
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],
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dtype=dtype,
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).duplicated()
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tm.assert_series_equal(result, expected)
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def test_multi_drop_duplicates_pos_args_deprecation():
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# GH#41485
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idx = MultiIndex.from_arrays([[1, 2, 3, 1], [1, 2, 3, 1]])
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msg = (
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"In a future version of pandas all arguments of "
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"MultiIndex.drop_duplicates will be keyword-only"
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)
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = idx.drop_duplicates("last")
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expected = MultiIndex.from_arrays([[2, 3, 1], [2, 3, 1]])
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tm.assert_index_equal(expected, result)
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