import numpy as np
|
import pytest
|
|
import pandas as pd
|
from pandas import (
|
CategoricalIndex,
|
DataFrame,
|
Index,
|
IntervalIndex,
|
MultiIndex,
|
Series,
|
)
|
import pandas._testing as tm
|
from pandas.api.types import (
|
is_float_dtype,
|
is_unsigned_integer_dtype,
|
)
|
|
|
@pytest.mark.parametrize("case", [0.5, "xxx"])
|
@pytest.mark.parametrize(
|
"method", ["intersection", "union", "difference", "symmetric_difference"]
|
)
|
def test_set_ops_error_cases(idx, case, sort, method):
|
# non-iterable input
|
msg = "Input must be Index or array-like"
|
with pytest.raises(TypeError, match=msg):
|
getattr(idx, method)(case, sort=sort)
|
|
|
@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list])
|
def test_intersection_base(idx, sort, klass):
|
first = idx[2::-1] # first 3 elements reversed
|
second = idx[:5]
|
|
if klass is not MultiIndex:
|
second = klass(second.values)
|
|
intersect = first.intersection(second, sort=sort)
|
if sort is None:
|
expected = first.sort_values()
|
else:
|
expected = first
|
tm.assert_index_equal(intersect, expected)
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
first.intersection([1, 2, 3], sort=sort)
|
|
|
@pytest.mark.arm_slow
|
@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list])
|
def test_union_base(idx, sort, klass):
|
first = idx[::-1]
|
second = idx[:5]
|
|
if klass is not MultiIndex:
|
second = klass(second.values)
|
|
union = first.union(second, sort=sort)
|
if sort is None:
|
expected = first.sort_values()
|
else:
|
expected = first
|
tm.assert_index_equal(union, expected)
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
first.union([1, 2, 3], sort=sort)
|
|
|
def test_difference_base(idx, sort):
|
second = idx[4:]
|
answer = idx[:4]
|
result = idx.difference(second, sort=sort)
|
|
if sort is None:
|
answer = answer.sort_values()
|
|
assert result.equals(answer)
|
tm.assert_index_equal(result, answer)
|
|
# GH 10149
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
for case in cases:
|
result = idx.difference(case, sort=sort)
|
tm.assert_index_equal(result, answer)
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
idx.difference([1, 2, 3], sort=sort)
|
|
|
def test_symmetric_difference(idx, sort):
|
first = idx[1:]
|
second = idx[:-1]
|
answer = idx[[-1, 0]]
|
result = first.symmetric_difference(second, sort=sort)
|
|
if sort is None:
|
answer = answer.sort_values()
|
|
tm.assert_index_equal(result, answer)
|
|
# GH 10149
|
cases = [klass(second.values) for klass in [np.array, Series, list]]
|
for case in cases:
|
result = first.symmetric_difference(case, sort=sort)
|
tm.assert_index_equal(result, answer)
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
first.symmetric_difference([1, 2, 3], sort=sort)
|
|
|
def test_multiindex_symmetric_difference():
|
# GH 13490
|
idx = MultiIndex.from_product([["a", "b"], ["A", "B"]], names=["a", "b"])
|
result = idx.symmetric_difference(idx)
|
assert result.names == idx.names
|
|
idx2 = idx.copy().rename(["A", "B"])
|
result = idx.symmetric_difference(idx2)
|
assert result.names == [None, None]
|
|
|
def test_empty(idx):
|
# GH 15270
|
assert not idx.empty
|
assert idx[:0].empty
|
|
|
def test_difference(idx, sort):
|
first = idx
|
result = first.difference(idx[-3:], sort=sort)
|
vals = idx[:-3].values
|
|
if sort is None:
|
vals = sorted(vals)
|
|
expected = MultiIndex.from_tuples(vals, sortorder=0, names=idx.names)
|
|
assert isinstance(result, MultiIndex)
|
assert result.equals(expected)
|
assert result.names == idx.names
|
tm.assert_index_equal(result, expected)
|
|
# empty difference: reflexive
|
result = idx.difference(idx, sort=sort)
|
expected = idx[:0]
|
assert result.equals(expected)
|
assert result.names == idx.names
|
|
# empty difference: superset
|
result = idx[-3:].difference(idx, sort=sort)
|
expected = idx[:0]
|
assert result.equals(expected)
|
assert result.names == idx.names
|
|
# empty difference: degenerate
|
result = idx[:0].difference(idx, sort=sort)
|
expected = idx[:0]
|
assert result.equals(expected)
|
assert result.names == idx.names
|
|
# names not the same
|
chunklet = idx[-3:]
|
chunklet.names = ["foo", "baz"]
|
result = first.difference(chunklet, sort=sort)
|
assert result.names == (None, None)
|
|
# empty, but non-equal
|
result = idx.difference(idx.sortlevel(1)[0], sort=sort)
|
assert len(result) == 0
|
|
# raise Exception called with non-MultiIndex
|
result = first.difference(first.values, sort=sort)
|
assert result.equals(first[:0])
|
|
# name from empty array
|
result = first.difference([], sort=sort)
|
assert first.equals(result)
|
assert first.names == result.names
|
|
# name from non-empty array
|
result = first.difference([("foo", "one")], sort=sort)
|
expected = MultiIndex.from_tuples(
|
[("bar", "one"), ("baz", "two"), ("foo", "two"), ("qux", "one"), ("qux", "two")]
|
)
|
expected.names = first.names
|
assert first.names == result.names
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
first.difference([1, 2, 3, 4, 5], sort=sort)
|
|
|
def test_difference_sort_special():
|
# GH-24959
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
# sort=None, the default
|
result = idx.difference([])
|
tm.assert_index_equal(result, idx)
|
|
|
def test_difference_sort_special_true():
|
# TODO(GH#25151): decide on True behaviour
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
result = idx.difference([], sort=True)
|
expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_difference_sort_incomparable():
|
# GH-24959
|
idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]])
|
|
other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]])
|
# sort=None, the default
|
msg = "sort order is undefined for incomparable objects"
|
with tm.assert_produces_warning(RuntimeWarning, match=msg):
|
result = idx.difference(other)
|
tm.assert_index_equal(result, idx)
|
|
# sort=False
|
result = idx.difference(other, sort=False)
|
tm.assert_index_equal(result, idx)
|
|
|
def test_difference_sort_incomparable_true():
|
idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]])
|
other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]])
|
|
# TODO: this is raising in constructing a Categorical when calling
|
# algos.safe_sort. Should we catch and re-raise with a better message?
|
msg = "'values' is not ordered, please explicitly specify the categories order "
|
with pytest.raises(TypeError, match=msg):
|
idx.difference(other, sort=True)
|
|
|
def test_union(idx, sort):
|
piece1 = idx[:5][::-1]
|
piece2 = idx[3:]
|
|
the_union = piece1.union(piece2, sort=sort)
|
|
if sort is None:
|
tm.assert_index_equal(the_union, idx.sort_values())
|
|
assert tm.equalContents(the_union, idx)
|
|
# corner case, pass self or empty thing:
|
the_union = idx.union(idx, sort=sort)
|
tm.assert_index_equal(the_union, idx)
|
|
the_union = idx.union(idx[:0], sort=sort)
|
tm.assert_index_equal(the_union, idx)
|
|
tuples = idx.values
|
result = idx[:4].union(tuples[4:], sort=sort)
|
if sort is None:
|
tm.equalContents(result, idx)
|
else:
|
assert result.equals(idx)
|
|
|
def test_union_with_regular_index(idx):
|
other = Index(["A", "B", "C"])
|
|
result = other.union(idx)
|
assert ("foo", "one") in result
|
assert "B" in result
|
|
msg = "The values in the array are unorderable"
|
with tm.assert_produces_warning(RuntimeWarning, match=msg):
|
result2 = idx.union(other)
|
# This is more consistent now, if sorting fails then we don't sort at all
|
# in the MultiIndex case.
|
assert not result.equals(result2)
|
|
|
def test_intersection(idx, sort):
|
piece1 = idx[:5][::-1]
|
piece2 = idx[3:]
|
|
the_int = piece1.intersection(piece2, sort=sort)
|
|
if sort is None:
|
tm.assert_index_equal(the_int, idx[3:5])
|
assert tm.equalContents(the_int, idx[3:5])
|
|
# corner case, pass self
|
the_int = idx.intersection(idx, sort=sort)
|
tm.assert_index_equal(the_int, idx)
|
|
# empty intersection: disjoint
|
empty = idx[:2].intersection(idx[2:], sort=sort)
|
expected = idx[:0]
|
assert empty.equals(expected)
|
|
tuples = idx.values
|
result = idx.intersection(tuples)
|
assert result.equals(idx)
|
|
|
@pytest.mark.parametrize(
|
"method", ["intersection", "union", "difference", "symmetric_difference"]
|
)
|
def test_setop_with_categorical(idx, sort, method):
|
other = idx.to_flat_index().astype("category")
|
res_names = [None] * idx.nlevels
|
|
result = getattr(idx, method)(other, sort=sort)
|
expected = getattr(idx, method)(idx, sort=sort).rename(res_names)
|
tm.assert_index_equal(result, expected)
|
|
result = getattr(idx, method)(other[:5], sort=sort)
|
expected = getattr(idx, method)(idx[:5], sort=sort).rename(res_names)
|
tm.assert_index_equal(result, expected)
|
|
|
def test_intersection_non_object(idx, sort):
|
other = Index(range(3), name="foo")
|
|
result = idx.intersection(other, sort=sort)
|
expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=None)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
# if we pass a length-0 ndarray (i.e. no name, we retain our idx.name)
|
result = idx.intersection(np.asarray(other)[:0], sort=sort)
|
expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=idx.names)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
msg = "other must be a MultiIndex or a list of tuples"
|
with pytest.raises(TypeError, match=msg):
|
# With non-zero length non-index, we try and fail to convert to tuples
|
idx.intersection(np.asarray(other), sort=sort)
|
|
|
def test_intersect_equal_sort():
|
# GH-24959
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
tm.assert_index_equal(idx.intersection(idx, sort=False), idx)
|
tm.assert_index_equal(idx.intersection(idx, sort=None), idx)
|
|
|
def test_intersect_equal_sort_true():
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
|
result = idx.intersection(idx, sort=True)
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize("slice_", [slice(None), slice(0)])
|
def test_union_sort_other_empty(slice_):
|
# https://github.com/pandas-dev/pandas/issues/24959
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
|
# default, sort=None
|
other = idx[slice_]
|
tm.assert_index_equal(idx.union(other), idx)
|
tm.assert_index_equal(other.union(idx), idx)
|
|
# sort=False
|
tm.assert_index_equal(idx.union(other, sort=False), idx)
|
|
|
def test_union_sort_other_empty_sort():
|
# TODO(GH#25151): decide on True behaviour
|
# # sort=True
|
idx = MultiIndex.from_product([[1, 0], ["a", "b"]])
|
other = idx[:0]
|
result = idx.union(other, sort=True)
|
expected = MultiIndex.from_product([[0, 1], ["a", "b"]])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_sort_other_incomparable():
|
# https://github.com/pandas-dev/pandas/issues/24959
|
idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]])
|
|
# default, sort=None
|
with tm.assert_produces_warning(RuntimeWarning):
|
result = idx.union(idx[:1])
|
tm.assert_index_equal(result, idx)
|
|
# sort=False
|
result = idx.union(idx[:1], sort=False)
|
tm.assert_index_equal(result, idx)
|
|
|
def test_union_sort_other_incomparable_sort():
|
idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]])
|
msg = "'<' not supported between instances of 'Timestamp' and 'int'"
|
with pytest.raises(TypeError, match=msg):
|
idx.union(idx[:1], sort=True)
|
|
|
def test_union_non_object_dtype_raises():
|
# GH#32646 raise NotImplementedError instead of less-informative error
|
mi = MultiIndex.from_product([["a", "b"], [1, 2]])
|
|
idx = mi.levels[1]
|
|
msg = "Can only union MultiIndex with MultiIndex or Index of tuples"
|
with pytest.raises(NotImplementedError, match=msg):
|
mi.union(idx)
|
|
|
def test_union_empty_self_different_names():
|
# GH#38423
|
mi = MultiIndex.from_arrays([[]])
|
mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
|
result = mi.union(mi2)
|
expected = MultiIndex.from_arrays([[1, 2], [3, 4]])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_multiindex_empty_rangeindex():
|
# GH#41234
|
mi = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
|
ri = pd.RangeIndex(0)
|
|
result_left = mi.union(ri)
|
tm.assert_index_equal(mi, result_left, check_names=False)
|
|
result_right = ri.union(mi)
|
tm.assert_index_equal(mi, result_right, check_names=False)
|
|
|
@pytest.mark.parametrize(
|
"method", ["union", "intersection", "difference", "symmetric_difference"]
|
)
|
def test_setops_sort_validation(method):
|
idx1 = MultiIndex.from_product([["a", "b"], [1, 2]])
|
idx2 = MultiIndex.from_product([["b", "c"], [1, 2]])
|
|
with pytest.raises(ValueError, match="The 'sort' keyword only takes"):
|
getattr(idx1, method)(idx2, sort=2)
|
|
# sort=True is supported as of GH#?
|
getattr(idx1, method)(idx2, sort=True)
|
|
|
@pytest.mark.parametrize("val", [pd.NA, 100])
|
def test_difference_keep_ea_dtypes(any_numeric_ea_dtype, val):
|
# GH#48606
|
midx = MultiIndex.from_arrays(
|
[Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
|
)
|
midx2 = MultiIndex.from_arrays(
|
[Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
|
)
|
result = midx.difference(midx2)
|
expected = MultiIndex.from_arrays([Series([1], dtype=any_numeric_ea_dtype), [2]])
|
tm.assert_index_equal(result, expected)
|
|
result = midx.difference(midx.sort_values(ascending=False))
|
expected = MultiIndex.from_arrays(
|
[Series([], dtype=any_numeric_ea_dtype), Series([], dtype=np.int64)],
|
names=["a", None],
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize("val", [pd.NA, 5])
|
def test_symmetric_difference_keeping_ea_dtype(any_numeric_ea_dtype, val):
|
# GH#48607
|
midx = MultiIndex.from_arrays(
|
[Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
|
)
|
midx2 = MultiIndex.from_arrays(
|
[Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
|
)
|
result = midx.symmetric_difference(midx2)
|
expected = MultiIndex.from_arrays(
|
[Series([1, 1, val], dtype=any_numeric_ea_dtype), [1, 2, 3]]
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
("tuples", "exp_tuples"),
|
[
|
([("val1", "test1")], [("val1", "test1")]),
|
([("val1", "test1"), ("val1", "test1")], [("val1", "test1")]),
|
(
|
[("val2", "test2"), ("val1", "test1")],
|
[("val2", "test2"), ("val1", "test1")],
|
),
|
],
|
)
|
def test_intersect_with_duplicates(tuples, exp_tuples):
|
# GH#36915
|
left = MultiIndex.from_tuples(tuples, names=["first", "second"])
|
right = MultiIndex.from_tuples(
|
[("val1", "test1"), ("val1", "test1"), ("val2", "test2")],
|
names=["first", "second"],
|
)
|
result = left.intersection(right)
|
expected = MultiIndex.from_tuples(exp_tuples, names=["first", "second"])
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"data, names, expected",
|
[
|
((1,), None, [None, None]),
|
((1,), ["a"], [None, None]),
|
((1,), ["b"], [None, None]),
|
((1, 2), ["c", "d"], [None, None]),
|
((1, 2), ["b", "a"], [None, None]),
|
((1, 2, 3), ["a", "b", "c"], [None, None]),
|
((1, 2), ["a", "c"], ["a", None]),
|
((1, 2), ["c", "b"], [None, "b"]),
|
((1, 2), ["a", "b"], ["a", "b"]),
|
((1, 2), [None, "b"], [None, "b"]),
|
],
|
)
|
def test_maybe_match_names(data, names, expected):
|
# GH#38323
|
mi = MultiIndex.from_tuples([], names=["a", "b"])
|
mi2 = MultiIndex.from_tuples([data], names=names)
|
result = mi._maybe_match_names(mi2)
|
assert result == expected
|
|
|
def test_intersection_equal_different_names():
|
# GH#30302
|
mi1 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["c", "b"])
|
mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
|
|
result = mi1.intersection(mi2)
|
expected = MultiIndex.from_arrays([[1, 2], [3, 4]], names=[None, "b"])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_intersection_different_names():
|
# GH#38323
|
mi = MultiIndex.from_arrays([[1], [3]], names=["c", "b"])
|
mi2 = MultiIndex.from_arrays([[1], [3]])
|
result = mi.intersection(mi2)
|
tm.assert_index_equal(result, mi2)
|
|
|
def test_intersection_with_missing_values_on_both_sides(nulls_fixture):
|
# GH#38623
|
mi1 = MultiIndex.from_arrays([[3, nulls_fixture, 4, nulls_fixture], [1, 2, 4, 2]])
|
mi2 = MultiIndex.from_arrays([[3, nulls_fixture, 3], [1, 2, 4]])
|
result = mi1.intersection(mi2)
|
expected = MultiIndex.from_arrays([[3, nulls_fixture], [1, 2]])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_with_missing_values_on_both_sides(nulls_fixture):
|
# GH#38623
|
mi1 = MultiIndex.from_arrays([[1, nulls_fixture]])
|
mi2 = MultiIndex.from_arrays([[1, nulls_fixture, 3]])
|
result = mi1.union(mi2)
|
expected = MultiIndex.from_arrays([[1, 3, nulls_fixture]])
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize("dtype", ["float64", "Float64"])
|
@pytest.mark.parametrize("sort", [None, False])
|
def test_union_nan_got_duplicated(dtype, sort):
|
# GH#38977, GH#49010
|
mi1 = MultiIndex.from_arrays([pd.array([1.0, np.nan], dtype=dtype), [2, 3]])
|
mi2 = MultiIndex.from_arrays([pd.array([1.0, np.nan, 3.0], dtype=dtype), [2, 3, 4]])
|
result = mi1.union(mi2, sort=sort)
|
if sort is None:
|
expected = MultiIndex.from_arrays(
|
[pd.array([1.0, 3.0, np.nan], dtype=dtype), [2, 4, 3]]
|
)
|
else:
|
expected = mi2
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize("val", [4, 1])
|
def test_union_keep_ea_dtype(any_numeric_ea_dtype, val):
|
# GH#48505
|
|
arr1 = Series([val, 2], dtype=any_numeric_ea_dtype)
|
arr2 = Series([2, 1], dtype=any_numeric_ea_dtype)
|
midx = MultiIndex.from_arrays([arr1, [1, 2]], names=["a", None])
|
midx2 = MultiIndex.from_arrays([arr2, [2, 1]])
|
result = midx.union(midx2)
|
if val == 4:
|
expected = MultiIndex.from_arrays(
|
[Series([1, 2, 4], dtype=any_numeric_ea_dtype), [1, 2, 1]]
|
)
|
else:
|
expected = MultiIndex.from_arrays(
|
[Series([1, 2], dtype=any_numeric_ea_dtype), [1, 2]]
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize("dupe_val", [3, pd.NA])
|
def test_union_with_duplicates_keep_ea_dtype(dupe_val, any_numeric_ea_dtype):
|
# GH48900
|
mi1 = MultiIndex.from_arrays(
|
[
|
Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype),
|
Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype),
|
]
|
)
|
mi2 = MultiIndex.from_arrays(
|
[
|
Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
|
Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
|
]
|
)
|
result = mi1.union(mi2)
|
expected = MultiIndex.from_arrays(
|
[
|
Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
|
Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype),
|
]
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_duplicates(index, request):
|
# GH#38977
|
if index.empty or isinstance(index, (IntervalIndex, CategoricalIndex)):
|
# No duplicates in empty indexes
|
return
|
|
values = index.unique().values.tolist()
|
mi1 = MultiIndex.from_arrays([values, [1] * len(values)])
|
mi2 = MultiIndex.from_arrays([[values[0]] + values, [1] * (len(values) + 1)])
|
result = mi2.union(mi1)
|
expected = mi2.sort_values()
|
tm.assert_index_equal(result, expected)
|
|
if (
|
is_unsigned_integer_dtype(mi2.levels[0])
|
and (mi2.get_level_values(0) < 2**63).all()
|
):
|
# GH#47294 - union uses lib.fast_zip, converting data to Python integers
|
# and loses type information. Result is then unsigned only when values are
|
# sufficiently large to require unsigned dtype. This happens only if other
|
# has dups or one of both have missing values
|
expected = expected.set_levels(
|
[expected.levels[0].astype(np.int64), expected.levels[1]]
|
)
|
elif is_float_dtype(mi2.levels[0]):
|
# mi2 has duplicates witch is a different path than above, Fix that path
|
# to use correct float dtype?
|
expected = expected.set_levels(
|
[expected.levels[0].astype(float), expected.levels[1]]
|
)
|
|
result = mi1.union(mi2)
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_keep_dtype_precision(any_real_numeric_dtype):
|
# GH#48498
|
arr1 = Series([4, 1, 1], dtype=any_real_numeric_dtype)
|
arr2 = Series([1, 4], dtype=any_real_numeric_dtype)
|
midx = MultiIndex.from_arrays([arr1, [2, 1, 1]], names=["a", None])
|
midx2 = MultiIndex.from_arrays([arr2, [1, 2]], names=["a", None])
|
|
result = midx.union(midx2)
|
expected = MultiIndex.from_arrays(
|
([Series([1, 1, 4], dtype=any_real_numeric_dtype), [1, 1, 2]]),
|
names=["a", None],
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_keep_ea_dtype_with_na(any_numeric_ea_dtype):
|
# GH#48498
|
arr1 = Series([4, pd.NA], dtype=any_numeric_ea_dtype)
|
arr2 = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
|
midx = MultiIndex.from_arrays([arr1, [2, 1]], names=["a", None])
|
midx2 = MultiIndex.from_arrays([arr2, [1, 2]])
|
result = midx.union(midx2)
|
expected = MultiIndex.from_arrays(
|
[Series([1, 4, pd.NA, pd.NA], dtype=any_numeric_ea_dtype), [1, 2, 1, 2]]
|
)
|
tm.assert_index_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"levels1, levels2, codes1, codes2, names",
|
[
|
(
|
[["a", "b", "c"], [0, ""]],
|
[["c", "d", "b"], [""]],
|
[[0, 1, 2], [1, 1, 1]],
|
[[0, 1, 2], [0, 0, 0]],
|
["name1", "name2"],
|
),
|
],
|
)
|
def test_intersection_lexsort_depth(levels1, levels2, codes1, codes2, names):
|
# GH#25169
|
mi1 = MultiIndex(levels=levels1, codes=codes1, names=names)
|
mi2 = MultiIndex(levels=levels2, codes=codes2, names=names)
|
mi_int = mi1.intersection(mi2)
|
assert mi_int._lexsort_depth == 2
|
|
|
@pytest.mark.parametrize(
|
"a",
|
[pd.Categorical(["a", "b"], categories=["a", "b"]), ["a", "b"]],
|
)
|
@pytest.mark.parametrize(
|
"b",
|
[
|
pd.Categorical(["a", "b"], categories=["b", "a"], ordered=True),
|
pd.Categorical(["a", "b"], categories=["b", "a"]),
|
],
|
)
|
def test_intersection_with_non_lex_sorted_categories(a, b):
|
# GH#49974
|
other = ["1", "2"]
|
|
df1 = DataFrame({"x": a, "y": other})
|
df2 = DataFrame({"x": b, "y": other})
|
|
expected = MultiIndex.from_arrays([a, other], names=["x", "y"])
|
|
res1 = MultiIndex.from_frame(df1).intersection(
|
MultiIndex.from_frame(df2.sort_values(["x", "y"]))
|
)
|
res2 = MultiIndex.from_frame(df1).intersection(MultiIndex.from_frame(df2))
|
res3 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection(
|
MultiIndex.from_frame(df2)
|
)
|
res4 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection(
|
MultiIndex.from_frame(df2.sort_values(["x", "y"]))
|
)
|
|
tm.assert_index_equal(res1, expected)
|
tm.assert_index_equal(res2, expected)
|
tm.assert_index_equal(res3, expected)
|
tm.assert_index_equal(res4, expected)
|
|
|
@pytest.mark.parametrize("val", [pd.NA, 100])
|
def test_intersection_keep_ea_dtypes(val, any_numeric_ea_dtype):
|
# GH#48604
|
midx = MultiIndex.from_arrays(
|
[Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None]
|
)
|
midx2 = MultiIndex.from_arrays(
|
[Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]]
|
)
|
result = midx.intersection(midx2)
|
expected = MultiIndex.from_arrays([Series([2], dtype=any_numeric_ea_dtype), [1]])
|
tm.assert_index_equal(result, expected)
|
|
|
def test_union_with_na_when_constructing_dataframe():
|
# GH43222
|
series1 = Series((1,), index=MultiIndex.from_tuples(((None, None),)))
|
series2 = Series((10, 20), index=MultiIndex.from_tuples(((None, None), ("a", "b"))))
|
result = DataFrame([series1, series2])
|
expected = DataFrame({(np.nan, np.nan): [1.0, 10.0], ("a", "b"): [np.nan, 20.0]})
|
tm.assert_frame_equal(result, expected)
|
