test_sorting.py

import numpy as np
import pytest

from pandas.errors import PerformanceWarning, UnsortedIndexError

import pandas as pd
from pandas import CategoricalIndex, DataFrame, Index, MultiIndex, RangeIndex
import pandas._testing as tm


def test_sortlevel(idx):
    import random

    tuples = list(idx)
    random.shuffle(tuples)

    index = MultiIndex.from_tuples(tuples)

    sorted_idx, _ = index.sortlevel(0)
    expected = MultiIndex.from_tuples(sorted(tuples))
    assert sorted_idx.equals(expected)

    sorted_idx, _ = index.sortlevel(0, ascending=False)
    assert sorted_idx.equals(expected[::-1])

    sorted_idx, _ = index.sortlevel(1)
    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
    expected = MultiIndex.from_tuples(by1)
    assert sorted_idx.equals(expected)

    sorted_idx, _ = index.sortlevel(1, ascending=False)
    assert sorted_idx.equals(expected[::-1])


def test_sortlevel_not_sort_remaining():
    mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
    sorted_idx, _ = mi.sortlevel("A", sort_remaining=False)
    assert sorted_idx.equals(mi)


def test_sortlevel_deterministic():
    tuples = [
        ("bar", "one"),
        ("foo", "two"),
        ("qux", "two"),
        ("foo", "one"),
        ("baz", "two"),
        ("qux", "one"),
    ]

    index = MultiIndex.from_tuples(tuples)

    sorted_idx, _ = index.sortlevel(0)
    expected = MultiIndex.from_tuples(sorted(tuples))
    assert sorted_idx.equals(expected)

    sorted_idx, _ = index.sortlevel(0, ascending=False)
    assert sorted_idx.equals(expected[::-1])

    sorted_idx, _ = index.sortlevel(1)
    by1 = sorted(tuples, key=lambda x: (x[1], x[0]))
    expected = MultiIndex.from_tuples(by1)
    assert sorted_idx.equals(expected)

    sorted_idx, _ = index.sortlevel(1, ascending=False)
    assert sorted_idx.equals(expected[::-1])


def test_sort(indices):
    with pytest.raises(TypeError):
        indices.sort()


def test_numpy_argsort(idx):
    result = np.argsort(idx)
    expected = idx.argsort()
    tm.assert_numpy_array_equal(result, expected)

    # these are the only two types that perform
    # pandas compatibility input validation - the
    # rest already perform separate (or no) such
    # validation via their 'values' attribute as
    # defined in pandas.core.indexes/base.py - they
    # cannot be changed at the moment due to
    # backwards compatibility concerns
    if isinstance(type(idx), (CategoricalIndex, RangeIndex)):
        msg = "the 'axis' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.argsort(idx, axis=1)

        msg = "the 'kind' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.argsort(idx, kind="mergesort")

        msg = "the 'order' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.argsort(idx, order=("a", "b"))


def test_unsortedindex():
    # GH 11897
    mi = pd.MultiIndex.from_tuples(
        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
        names=["one", "two"],
    )
    df = pd.DataFrame([[i, 10 * i] for i in range(6)], index=mi, columns=["one", "two"])

    # GH 16734: not sorted, but no real slicing
    result = df.loc(axis=0)["z", "a"]
    expected = df.iloc[0]
    tm.assert_series_equal(result, expected)

    with pytest.raises(UnsortedIndexError):
        df.loc(axis=0)["z", slice("a")]
    df.sort_index(inplace=True)
    assert len(df.loc(axis=0)["z", :]) == 2

    with pytest.raises(KeyError, match="'q'"):
        df.loc(axis=0)["q", :]


def test_unsortedindex_doc_examples():
    # https://pandas.pydata.org/pandas-docs/stable/advanced.html#sorting-a-multiindex  # noqa
    dfm = DataFrame(
        {"jim": [0, 0, 1, 1], "joe": ["x", "x", "z", "y"], "jolie": np.random.rand(4)}
    )

    dfm = dfm.set_index(["jim", "joe"])
    with tm.assert_produces_warning(PerformanceWarning):
        dfm.loc[(1, "z")]

    with pytest.raises(UnsortedIndexError):
        dfm.loc[(0, "y"):(1, "z")]

    assert not dfm.index.is_lexsorted()
    assert dfm.index.lexsort_depth == 1

    # sort it
    dfm = dfm.sort_index()
    dfm.loc[(1, "z")]
    dfm.loc[(0, "y"):(1, "z")]

    assert dfm.index.is_lexsorted()
    assert dfm.index.lexsort_depth == 2


def test_reconstruct_sort():

    # starts off lexsorted & monotonic
    mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
    assert mi.is_lexsorted()
    assert mi.is_monotonic

    recons = mi._sort_levels_monotonic()
    assert recons.is_lexsorted()
    assert recons.is_monotonic
    assert mi is recons

    assert mi.equals(recons)
    assert Index(mi.values).equals(Index(recons.values))

    # cannot convert to lexsorted
    mi = pd.MultiIndex.from_tuples(
        [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")],
        names=["one", "two"],
    )
    assert not mi.is_lexsorted()
    assert not mi.is_monotonic

    recons = mi._sort_levels_monotonic()
    assert not recons.is_lexsorted()
    assert not recons.is_monotonic

    assert mi.equals(recons)
    assert Index(mi.values).equals(Index(recons.values))

    # cannot convert to lexsorted
    mi = MultiIndex(
        levels=[["b", "d", "a"], [1, 2, 3]],
        codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
        names=["col1", "col2"],
    )
    assert not mi.is_lexsorted()
    assert not mi.is_monotonic

    recons = mi._sort_levels_monotonic()
    assert not recons.is_lexsorted()
    assert not recons.is_monotonic

    assert mi.equals(recons)
    assert Index(mi.values).equals(Index(recons.values))


def test_reconstruct_remove_unused():
    # xref to GH 2770
    df = DataFrame(
        [["deleteMe", 1, 9], ["keepMe", 2, 9], ["keepMeToo", 3, 9]],
        columns=["first", "second", "third"],
    )
    df2 = df.set_index(["first", "second"], drop=False)
    df2 = df2[df2["first"] != "deleteMe"]

    # removed levels are there
    expected = MultiIndex(
        levels=[["deleteMe", "keepMe", "keepMeToo"], [1, 2, 3]],
        codes=[[1, 2], [1, 2]],
        names=["first", "second"],
    )
    result = df2.index
    tm.assert_index_equal(result, expected)

    expected = MultiIndex(
        levels=[["keepMe", "keepMeToo"], [2, 3]],
        codes=[[0, 1], [0, 1]],
        names=["first", "second"],
    )
    result = df2.index.remove_unused_levels()
    tm.assert_index_equal(result, expected)

    # idempotent
    result2 = result.remove_unused_levels()
    tm.assert_index_equal(result2, expected)
    assert result2.is_(result)


@pytest.mark.parametrize(
    "first_type,second_type", [("int64", "int64"), ("datetime64[D]", "str")]
)
def test_remove_unused_levels_large(first_type, second_type):
    # GH16556

    # because tests should be deterministic (and this test in particular
    # checks that levels are removed, which is not the case for every
    # random input):
    rng = np.random.RandomState(4)  # seed is arbitrary value that works

    size = 1 << 16
    df = DataFrame(
        dict(
            first=rng.randint(0, 1 << 13, size).astype(first_type),
            second=rng.randint(0, 1 << 10, size).astype(second_type),
            third=rng.rand(size),
        )
    )
    df = df.groupby(["first", "second"]).sum()
    df = df[df.third < 0.1]

    result = df.index.remove_unused_levels()
    assert len(result.levels[0]) < len(df.index.levels[0])
    assert len(result.levels[1]) < len(df.index.levels[1])
    assert result.equals(df.index)

    expected = df.reset_index().set_index(["first", "second"]).index
    tm.assert_index_equal(result, expected)


@pytest.mark.parametrize("level0", [["a", "d", "b"], ["a", "d", "b", "unused"]])
@pytest.mark.parametrize(
    "level1", [["w", "x", "y", "z"], ["w", "x", "y", "z", "unused"]]
)
def test_remove_unused_nan(level0, level1):
    # GH 18417
    mi = pd.MultiIndex(
        levels=[level0, level1], codes=[[0, 2, -1, 1, -1], [0, 1, 2, 3, 2]]
    )

    result = mi.remove_unused_levels()
    tm.assert_index_equal(result, mi)
    for level in 0, 1:
        assert "unused" not in result.levels[level]


def test_argsort(idx):
    result = idx.argsort()
    expected = idx.values.argsort()
    tm.assert_numpy_array_equal(result, expected)