Inzynierka/Lib/site-packages/pandas/tests/indexes/multi/test_join.py
2023-06-02 12:51:02 +02:00

272 lines
8.3 KiB
Python

import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
Interval,
MultiIndex,
Series,
StringDtype,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"other", [Index(["three", "one", "two"]), Index(["one"]), Index(["one", "three"])]
)
def test_join_level(idx, other, join_type):
join_index, lidx, ridx = other.join(
idx, how=join_type, level="second", return_indexers=True
)
exp_level = other.join(idx.levels[1], how=join_type)
assert join_index.levels[0].equals(idx.levels[0])
assert join_index.levels[1].equals(exp_level)
# pare down levels
mask = np.array([x[1] in exp_level for x in idx], dtype=bool)
exp_values = idx.values[mask]
tm.assert_numpy_array_equal(join_index.values, exp_values)
if join_type in ("outer", "inner"):
join_index2, ridx2, lidx2 = idx.join(
other, how=join_type, level="second", return_indexers=True
)
assert join_index.equals(join_index2)
tm.assert_numpy_array_equal(lidx, lidx2)
tm.assert_numpy_array_equal(ridx, ridx2)
tm.assert_numpy_array_equal(join_index2.values, exp_values)
def test_join_level_corner_case(idx):
# some corner cases
index = Index(["three", "one", "two"])
result = index.join(idx, level="second")
assert isinstance(result, MultiIndex)
with pytest.raises(TypeError, match="Join.*MultiIndex.*ambiguous"):
idx.join(idx, level=1)
def test_join_self(idx, join_type):
joined = idx.join(idx, how=join_type)
tm.assert_index_equal(joined, idx)
def test_join_multi():
# GH 10665
midx = MultiIndex.from_product([np.arange(4), np.arange(4)], names=["a", "b"])
idx = Index([1, 2, 5], name="b")
# inner
jidx, lidx, ridx = midx.join(idx, how="inner", return_indexers=True)
exp_idx = MultiIndex.from_product([np.arange(4), [1, 2]], names=["a", "b"])
exp_lidx = np.array([1, 2, 5, 6, 9, 10, 13, 14], dtype=np.intp)
exp_ridx = np.array([0, 1, 0, 1, 0, 1, 0, 1], dtype=np.intp)
tm.assert_index_equal(jidx, exp_idx)
tm.assert_numpy_array_equal(lidx, exp_lidx)
tm.assert_numpy_array_equal(ridx, exp_ridx)
# flip
jidx, ridx, lidx = idx.join(midx, how="inner", return_indexers=True)
tm.assert_index_equal(jidx, exp_idx)
tm.assert_numpy_array_equal(lidx, exp_lidx)
tm.assert_numpy_array_equal(ridx, exp_ridx)
# keep MultiIndex
jidx, lidx, ridx = midx.join(idx, how="left", return_indexers=True)
exp_ridx = np.array(
[-1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1], dtype=np.intp
)
tm.assert_index_equal(jidx, midx)
assert lidx is None
tm.assert_numpy_array_equal(ridx, exp_ridx)
# flip
jidx, ridx, lidx = idx.join(midx, how="right", return_indexers=True)
tm.assert_index_equal(jidx, midx)
assert lidx is None
tm.assert_numpy_array_equal(ridx, exp_ridx)
def test_join_self_unique(idx, join_type):
if idx.is_unique:
joined = idx.join(idx, how=join_type)
assert (idx == joined).all()
def test_join_multi_wrong_order():
# GH 25760
# GH 28956
midx1 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["b", "a"])
join_idx, lidx, ridx = midx1.join(midx2, return_indexers=True)
exp_ridx = np.array([-1, -1, -1, -1], dtype=np.intp)
tm.assert_index_equal(midx1, join_idx)
assert lidx is None
tm.assert_numpy_array_equal(ridx, exp_ridx)
def test_join_multi_return_indexers():
# GH 34074
midx1 = MultiIndex.from_product([[1, 2], [3, 4], [5, 6]], names=["a", "b", "c"])
midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
result = midx1.join(midx2, return_indexers=False)
tm.assert_index_equal(result, midx1)
def test_join_overlapping_interval_level():
# GH 44096
idx_1 = MultiIndex.from_tuples(
[
(1, Interval(0.0, 1.0)),
(1, Interval(1.0, 2.0)),
(1, Interval(2.0, 5.0)),
(2, Interval(0.0, 1.0)),
(2, Interval(1.0, 3.0)), # interval limit is here at 3.0, not at 2.0
(2, Interval(3.0, 5.0)),
],
names=["num", "interval"],
)
idx_2 = MultiIndex.from_tuples(
[
(1, Interval(2.0, 5.0)),
(1, Interval(0.0, 1.0)),
(1, Interval(1.0, 2.0)),
(2, Interval(3.0, 5.0)),
(2, Interval(0.0, 1.0)),
(2, Interval(1.0, 3.0)),
],
names=["num", "interval"],
)
expected = MultiIndex.from_tuples(
[
(1, Interval(0.0, 1.0)),
(1, Interval(1.0, 2.0)),
(1, Interval(2.0, 5.0)),
(2, Interval(0.0, 1.0)),
(2, Interval(1.0, 3.0)),
(2, Interval(3.0, 5.0)),
],
names=["num", "interval"],
)
result = idx_1.join(idx_2, how="outer")
tm.assert_index_equal(result, expected)
def test_join_midx_ea():
# GH#49277
midx = MultiIndex.from_arrays(
[Series([1, 1, 3], dtype="Int64"), Series([1, 2, 3], dtype="Int64")],
names=["a", "b"],
)
midx2 = MultiIndex.from_arrays(
[Series([1], dtype="Int64"), Series([3], dtype="Int64")], names=["a", "c"]
)
result = midx.join(midx2, how="inner")
expected = MultiIndex.from_arrays(
[
Series([1, 1], dtype="Int64"),
Series([1, 2], dtype="Int64"),
Series([3, 3], dtype="Int64"),
],
names=["a", "b", "c"],
)
tm.assert_index_equal(result, expected)
def test_join_midx_string():
# GH#49277
midx = MultiIndex.from_arrays(
[
Series(["a", "a", "c"], dtype=StringDtype()),
Series(["a", "b", "c"], dtype=StringDtype()),
],
names=["a", "b"],
)
midx2 = MultiIndex.from_arrays(
[Series(["a"], dtype=StringDtype()), Series(["c"], dtype=StringDtype())],
names=["a", "c"],
)
result = midx.join(midx2, how="inner")
expected = MultiIndex.from_arrays(
[
Series(["a", "a"], dtype=StringDtype()),
Series(["a", "b"], dtype=StringDtype()),
Series(["c", "c"], dtype=StringDtype()),
],
names=["a", "b", "c"],
)
tm.assert_index_equal(result, expected)
def test_join_multi_with_nan():
# GH29252
df1 = DataFrame(
data={"col1": [1.1, 1.2]},
index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]),
)
df2 = DataFrame(
data={"col2": [2.1, 2.2]},
index=MultiIndex.from_product([["A"], [np.NaN, 2.0]], names=["id1", "id2"]),
)
result = df1.join(df2)
expected = DataFrame(
data={"col1": [1.1, 1.2], "col2": [np.nan, 2.2]},
index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("val", [0, 5])
def test_join_dtypes(any_numeric_ea_dtype, val):
# GH#49830
midx = MultiIndex.from_arrays([Series([1, 2], dtype=any_numeric_ea_dtype), [3, 4]])
midx2 = MultiIndex.from_arrays(
[Series([1, val, val], dtype=any_numeric_ea_dtype), [3, 4, 4]]
)
result = midx.join(midx2, how="outer")
expected = MultiIndex.from_arrays(
[Series([val, val, 1, 2], dtype=any_numeric_ea_dtype), [4, 4, 3, 4]]
).sort_values()
tm.assert_index_equal(result, expected)
def test_join_dtypes_all_nan(any_numeric_ea_dtype):
# GH#49830
midx = MultiIndex.from_arrays(
[Series([1, 2], dtype=any_numeric_ea_dtype), [np.nan, np.nan]]
)
midx2 = MultiIndex.from_arrays(
[Series([1, 0, 0], dtype=any_numeric_ea_dtype), [np.nan, np.nan, np.nan]]
)
result = midx.join(midx2, how="outer")
expected = MultiIndex.from_arrays(
[
Series([0, 0, 1, 2], dtype=any_numeric_ea_dtype),
[np.nan, np.nan, np.nan, np.nan],
]
)
tm.assert_index_equal(result, expected)
def test_join_index_levels():
# GH#53093
midx = midx = MultiIndex.from_tuples([("a", "2019-02-01"), ("a", "2019-02-01")])
midx2 = MultiIndex.from_tuples([("a", "2019-01-31")])
result = midx.join(midx2, how="outer")
expected = MultiIndex.from_tuples(
[("a", "2019-01-31"), ("a", "2019-02-01"), ("a", "2019-02-01")]
)
tm.assert_index_equal(result.levels[1], expected.levels[1])
tm.assert_index_equal(result, expected)