SpringFocus/venv/lib/python3.8/site-packages/pandas/tests/indexing/multiindex/test_xs.py

import numpy as np
import pytest

from pandas import DataFrame, Index, MultiIndex, Series, concat, date_range
import pandas._testing as tm
import pandas.core.common as com


@pytest.fixture
def four_level_index_dataframe():
    arr = np.array(
        [
            [-0.5109, -2.3358, -0.4645, 0.05076, 0.364],
            [0.4473, 1.4152, 0.2834, 1.00661, 0.1744],
            [-0.6662, -0.5243, -0.358, 0.89145, 2.5838],
        ]
    )
    index = MultiIndex(
        levels=[["a", "x"], ["b", "q"], [10.0032, 20.0, 30.0], [3, 4, 5]],
        codes=[[0, 0, 1], [0, 1, 1], [0, 1, 2], [2, 1, 0]],
        names=["one", "two", "three", "four"],
    )
    return DataFrame(arr, index=index, columns=list("ABCDE"))


@pytest.mark.parametrize(
    "key, level, exp_arr, exp_index",
    [
        ("a", "lvl0", lambda x: x[:, 0:2], Index(["bar", "foo"], name="lvl1")),
        ("foo", "lvl1", lambda x: x[:, 1:2], Index(["a"], name="lvl0")),
    ],
)
def test_xs_named_levels_axis_eq_1(key, level, exp_arr, exp_index):
    # see gh-2903
    arr = np.random.randn(4, 4)
    index = MultiIndex(
        levels=[["a", "b"], ["bar", "foo", "hello", "world"]],
        codes=[[0, 0, 1, 1], [0, 1, 2, 3]],
        names=["lvl0", "lvl1"],
    )
    df = DataFrame(arr, columns=index)
    result = df.xs(key, level=level, axis=1)
    expected = DataFrame(exp_arr(arr), columns=exp_index)
    tm.assert_frame_equal(result, expected)


def test_xs_values(multiindex_dataframe_random_data):
    df = multiindex_dataframe_random_data
    result = df.xs(("bar", "two")).values
    expected = df.values[4]
    tm.assert_almost_equal(result, expected)


def test_xs_loc_equality(multiindex_dataframe_random_data):
    df = multiindex_dataframe_random_data
    result = df.xs(("bar", "two"))
    expected = df.loc[("bar", "two")]
    tm.assert_series_equal(result, expected)


def test_xs_missing_values_in_index():
    # see gh-6574
    # missing values in returned index should be preserved
    acc = [
        ("a", "abcde", 1),
        ("b", "bbcde", 2),
        ("y", "yzcde", 25),
        ("z", "xbcde", 24),
        ("z", None, 26),
        ("z", "zbcde", 25),
        ("z", "ybcde", 26),
    ]
    df = DataFrame(acc, columns=["a1", "a2", "cnt"]).set_index(["a1", "a2"])
    expected = DataFrame(
        {"cnt": [24, 26, 25, 26]},
        index=Index(["xbcde", np.nan, "zbcde", "ybcde"], name="a2"),
    )

    result = df.xs("z", level="a1")
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("key, level", [("one", "second"), (["one"], ["second"])])
def test_xs_with_duplicates(key, level, multiindex_dataframe_random_data):
    # see gh-13719
    frame = multiindex_dataframe_random_data
    df = concat([frame] * 2)
    assert df.index.is_unique is False
    expected = concat([frame.xs("one", level="second")] * 2)

    result = df.xs(key, level=level)
    tm.assert_frame_equal(result, expected)


def test_xs_level(multiindex_dataframe_random_data):
    df = multiindex_dataframe_random_data
    result = df.xs("two", level="second")
    expected = df[df.index.get_level_values(1) == "two"]
    expected.index = Index(["foo", "bar", "baz", "qux"], name="first")
    tm.assert_frame_equal(result, expected)


def test_xs_level_eq_2():
    arr = np.random.randn(3, 5)
    index = MultiIndex(
        levels=[["a", "p", "x"], ["b", "q", "y"], ["c", "r", "z"]],
        codes=[[2, 0, 1], [2, 0, 1], [2, 0, 1]],
    )
    df = DataFrame(arr, index=index)
    expected = DataFrame(arr[1:2], index=[["a"], ["b"]])
    result = df.xs("c", level=2)
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "indexer",
    [
        lambda df: df.xs(("a", 4), level=["one", "four"]),
        lambda df: df.xs("a").xs(4, level="four"),
    ],
)
def test_xs_level_multiple(indexer, four_level_index_dataframe):
    df = four_level_index_dataframe
    expected_values = [[0.4473, 1.4152, 0.2834, 1.00661, 0.1744]]
    expected_index = MultiIndex(
        levels=[["q"], [20.0]], codes=[[0], [0]], names=["two", "three"]
    )
    expected = DataFrame(expected_values, index=expected_index, columns=list("ABCDE"))
    result = indexer(df)
    tm.assert_frame_equal(result, expected)


def test_xs_setting_with_copy_error(multiindex_dataframe_random_data):
    # this is a copy in 0.14
    df = multiindex_dataframe_random_data
    result = df.xs("two", level="second")

    # setting this will give a SettingWithCopyError
    # as we are trying to write a view
    msg = "A value is trying to be set on a copy of a slice from a DataFrame"
    with pytest.raises(com.SettingWithCopyError, match=msg):
        result[:] = 10


def test_xs_setting_with_copy_error_multiple(four_level_index_dataframe):
    # this is a copy in 0.14
    df = four_level_index_dataframe
    result = df.xs(("a", 4), level=["one", "four"])

    # setting this will give a SettingWithCopyError
    # as we are trying to write a view
    msg = "A value is trying to be set on a copy of a slice from a DataFrame"
    with pytest.raises(com.SettingWithCopyError, match=msg):
        result[:] = 10


def test_xs_integer_key():
    # see gh-2107
    dates = range(20111201, 20111205)
    ids = list("abcde")
    index = MultiIndex.from_product([dates, ids], names=["date", "secid"])
    df = DataFrame(np.random.randn(len(index), 3), index, ["X", "Y", "Z"])

    result = df.xs(20111201, level="date")
    expected = df.loc[20111201, :]
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "indexer", [lambda df: df.xs("a", level=0), lambda df: df.xs("a")]
)
def test_xs_level0(indexer, four_level_index_dataframe):
    df = four_level_index_dataframe
    expected_values = [
        [-0.5109, -2.3358, -0.4645, 0.05076, 0.364],
        [0.4473, 1.4152, 0.2834, 1.00661, 0.1744],
    ]
    expected_index = MultiIndex(
        levels=[["b", "q"], [10.0032, 20.0], [4, 5]],
        codes=[[0, 1], [0, 1], [1, 0]],
        names=["two", "three", "four"],
    )
    expected = DataFrame(expected_values, index=expected_index, columns=list("ABCDE"))

    result = indexer(df)
    tm.assert_frame_equal(result, expected)


def test_xs_level_series(multiindex_dataframe_random_data):
    # this test is not explicitly testing .xs functionality
    # TODO: move to another module or refactor
    df = multiindex_dataframe_random_data
    s = df["A"]
    result = s[:, "two"]
    expected = df.xs("two", level=1)["A"]
    tm.assert_series_equal(result, expected)


def test_xs_level_series_ymd(multiindex_year_month_day_dataframe_random_data):
    # this test is not explicitly testing .xs functionality
    # TODO: move to another module or refactor
    df = multiindex_year_month_day_dataframe_random_data
    s = df["A"]
    result = s[2000, 5]
    expected = df.loc[2000, 5]["A"]
    tm.assert_series_equal(result, expected)


def test_xs_level_series_slice_not_implemented(
    multiindex_year_month_day_dataframe_random_data,
):
    # this test is not explicitly testing .xs functionality
    # TODO: move to another module or refactor
    # not implementing this for now
    df = multiindex_year_month_day_dataframe_random_data
    s = df["A"]

    msg = r"\(2000, slice\(3, 4, None\)\)"
    with pytest.raises(TypeError, match=msg):
        s[2000, 3:4]


def test_series_getitem_multiindex_xs():
    # GH6258
    dt = list(date_range("20130903", periods=3))
    idx = MultiIndex.from_product([list("AB"), dt])
    s = Series([1, 3, 4, 1, 3, 4], index=idx)
    expected = Series([1, 1], index=list("AB"))

    result = s.xs("20130903", level=1)
    tm.assert_series_equal(result, expected)


def test_series_getitem_multiindex_xs_by_label():
    # GH5684
    idx = MultiIndex.from_tuples(
        [("a", "one"), ("a", "two"), ("b", "one"), ("b", "two")]
    )
    s = Series([1, 2, 3, 4], index=idx)
    s.index.set_names(["L1", "L2"], inplace=True)
    expected = Series([1, 3], index=["a", "b"])
    expected.index.set_names(["L1"], inplace=True)

    result = s.xs("one", level="L2")
    tm.assert_series_equal(result, expected)