import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_any_real_numeric_dtype
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import pandas as pd
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from pandas import (
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Index,
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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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def test_equals(idx):
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assert idx.equals(idx)
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assert idx.equals(idx.copy())
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assert idx.equals(idx.astype(object))
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assert idx.equals(idx.to_flat_index())
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assert idx.equals(idx.to_flat_index().astype("category"))
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assert not idx.equals(list(idx))
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assert not idx.equals(np.array(idx))
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same_values = Index(idx, dtype=object)
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assert idx.equals(same_values)
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assert same_values.equals(idx)
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if idx.nlevels == 1:
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# do not test MultiIndex
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assert not idx.equals(Series(idx))
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def test_equals_op(idx):
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# GH9947, GH10637
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index_a = idx
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n = len(index_a)
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index_b = index_a[0:-1]
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index_c = index_a[0:-1].append(index_a[-2:-1])
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index_d = index_a[0:1]
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == index_b
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expected1 = np.array([True] * n)
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expected2 = np.array([True] * (n - 1) + [False])
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tm.assert_numpy_array_equal(index_a == index_a, expected1)
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tm.assert_numpy_array_equal(index_a == index_c, expected2)
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# test comparisons with numpy arrays
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array_a = np.array(index_a)
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array_b = np.array(index_a[0:-1])
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array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
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array_d = np.array(index_a[0:1])
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == array_b
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tm.assert_numpy_array_equal(index_a == array_a, expected1)
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tm.assert_numpy_array_equal(index_a == array_c, expected2)
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# test comparisons with Series
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series_a = Series(array_a)
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series_b = Series(array_b)
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series_c = Series(array_c)
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series_d = Series(array_d)
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == series_b
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tm.assert_numpy_array_equal(index_a == series_a, expected1)
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tm.assert_numpy_array_equal(index_a == series_c, expected2)
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# cases where length is 1 for one of them
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == index_d
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == series_d
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with pytest.raises(ValueError, match="Lengths must match"):
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index_a == array_d
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msg = "Can only compare identically-labeled Series objects"
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with pytest.raises(ValueError, match=msg):
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series_a == series_d
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with pytest.raises(ValueError, match="Lengths must match"):
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series_a == array_d
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# comparing with a scalar should broadcast; note that we are excluding
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# MultiIndex because in this case each item in the index is a tuple of
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# length 2, and therefore is considered an array of length 2 in the
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# comparison instead of a scalar
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if not isinstance(index_a, MultiIndex):
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expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
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# assuming the 2nd to last item is unique in the data
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item = index_a[-2]
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tm.assert_numpy_array_equal(index_a == item, expected3)
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tm.assert_series_equal(series_a == item, Series(expected3))
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def test_compare_tuple():
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# GH#21517
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mi = MultiIndex.from_product([[1, 2]] * 2)
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all_false = np.array([False, False, False, False])
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result = mi == mi[0]
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expected = np.array([True, False, False, False])
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tm.assert_numpy_array_equal(result, expected)
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result = mi != mi[0]
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tm.assert_numpy_array_equal(result, ~expected)
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result = mi < mi[0]
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tm.assert_numpy_array_equal(result, all_false)
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result = mi <= mi[0]
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tm.assert_numpy_array_equal(result, expected)
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result = mi > mi[0]
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tm.assert_numpy_array_equal(result, ~expected)
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result = mi >= mi[0]
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tm.assert_numpy_array_equal(result, ~all_false)
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def test_compare_tuple_strs():
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# GH#34180
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mi = MultiIndex.from_tuples([("a", "b"), ("b", "c"), ("c", "a")])
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result = mi == ("c", "a")
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expected = np.array([False, False, True])
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tm.assert_numpy_array_equal(result, expected)
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result = mi == ("c",)
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expected = np.array([False, False, False])
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tm.assert_numpy_array_equal(result, expected)
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def test_equals_multi(idx):
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assert idx.equals(idx)
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assert not idx.equals(idx.values)
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assert idx.equals(Index(idx.values))
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assert idx.equal_levels(idx)
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assert not idx.equals(idx[:-1])
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assert not idx.equals(idx[-1])
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# different number of levels
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index = MultiIndex(
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levels=[Index(list(range(4))), Index(list(range(4))), Index(list(range(4)))],
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codes=[
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np.array([0, 0, 1, 2, 2, 2, 3, 3]),
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np.array([0, 1, 0, 0, 0, 1, 0, 1]),
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np.array([1, 0, 1, 1, 0, 0, 1, 0]),
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],
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)
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index2 = MultiIndex(levels=index.levels[:-1], codes=index.codes[:-1])
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assert not index.equals(index2)
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assert not index.equal_levels(index2)
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# levels are different
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major_axis = Index(list(range(4)))
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minor_axis = Index(list(range(2)))
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major_codes = np.array([0, 0, 1, 2, 2, 3])
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minor_codes = np.array([0, 1, 0, 0, 1, 0])
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index = MultiIndex(
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levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
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)
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assert not idx.equals(index)
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assert not idx.equal_levels(index)
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# some of the labels are different
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major_axis = Index(["foo", "bar", "baz", "qux"])
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minor_axis = Index(["one", "two"])
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major_codes = np.array([0, 0, 2, 2, 3, 3])
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minor_codes = np.array([0, 1, 0, 1, 0, 1])
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index = MultiIndex(
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levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
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)
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assert not idx.equals(index)
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def test_identical(idx):
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mi = idx.copy()
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mi2 = idx.copy()
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assert mi.identical(mi2)
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mi = mi.set_names(["new1", "new2"])
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assert mi.equals(mi2)
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assert not mi.identical(mi2)
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mi2 = mi2.set_names(["new1", "new2"])
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assert mi.identical(mi2)
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mi4 = Index(mi.tolist(), tupleize_cols=False)
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assert not mi.identical(mi4)
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assert mi.equals(mi4)
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def test_equals_operator(idx):
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# GH9785
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assert (idx == idx).all()
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def test_equals_missing_values():
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# make sure take is not using -1
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i = MultiIndex.from_tuples([(0, pd.NaT), (0, pd.Timestamp("20130101"))])
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result = i[0:1].equals(i[0])
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assert not result
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result = i[1:2].equals(i[1])
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assert not result
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def test_equals_missing_values_differently_sorted():
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# GH#38439
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mi1 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
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mi2 = MultiIndex.from_tuples([(np.nan, np.nan), (81.0, np.nan)])
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assert not mi1.equals(mi2)
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mi2 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)])
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assert mi1.equals(mi2)
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def test_is_():
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mi = MultiIndex.from_tuples(zip(range(10), range(10)))
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assert mi.is_(mi)
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assert mi.is_(mi.view())
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assert mi.is_(mi.view().view().view().view())
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mi2 = mi.view()
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# names are metadata, they don't change id
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mi2.names = ["A", "B"]
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assert mi2.is_(mi)
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assert mi.is_(mi2)
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assert not mi.is_(mi.set_names(["C", "D"]))
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# levels are inherent properties, they change identity
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mi3 = mi2.set_levels([list(range(10)), list(range(10))])
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assert not mi3.is_(mi2)
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# shouldn't change
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assert mi2.is_(mi)
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mi4 = mi3.view()
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# GH 17464 - Remove duplicate MultiIndex levels
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mi4 = mi4.set_levels([list(range(10)), list(range(10))])
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assert not mi4.is_(mi3)
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mi5 = mi.view()
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mi5 = mi5.set_levels(mi5.levels)
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assert not mi5.is_(mi)
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def test_is_all_dates(idx):
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assert not idx._is_all_dates
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def test_is_numeric(idx):
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# MultiIndex is never numeric
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assert not is_any_real_numeric_dtype(idx)
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def test_multiindex_compare():
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# GH 21149
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# Ensure comparison operations for MultiIndex with nlevels == 1
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# behave consistently with those for MultiIndex with nlevels > 1
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midx = MultiIndex.from_product([[0, 1]])
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# Equality self-test: MultiIndex object vs self
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expected = Series([True, True])
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result = Series(midx == midx)
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tm.assert_series_equal(result, expected)
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# Greater than comparison: MultiIndex object vs self
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expected = Series([False, False])
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result = Series(midx > midx)
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tm.assert_series_equal(result, expected)
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def test_equals_ea_int_regular_int():
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# GH#46026
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mi1 = MultiIndex.from_arrays([Index([1, 2], dtype="Int64"), [3, 4]])
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mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]])
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assert not mi1.equals(mi2)
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assert not mi2.equals(mi1)
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