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| import numpy as np
| import pytest
|
| import pandas._libs.index as _index
| from pandas.errors import PerformanceWarning
|
| import pandas as pd
| from pandas import (
| DataFrame,
| Index,
| MultiIndex,
| Series,
| )
| import pandas._testing as tm
|
|
| class TestMultiIndexBasic:
| def test_multiindex_perf_warn(self):
| df = DataFrame(
| {
| "jim": [0, 0, 1, 1],
| "joe": ["x", "x", "z", "y"],
| "jolie": np.random.rand(4),
| }
| ).set_index(["jim", "joe"])
|
| with tm.assert_produces_warning(PerformanceWarning):
| df.loc[(1, "z")]
|
| df = df.iloc[[2, 1, 3, 0]]
| with tm.assert_produces_warning(PerformanceWarning):
| df.loc[(0,)]
|
| def test_indexing_over_hashtable_size_cutoff(self):
| n = 10000
|
| old_cutoff = _index._SIZE_CUTOFF
| _index._SIZE_CUTOFF = 20000
|
| s = Series(np.arange(n), MultiIndex.from_arrays((["a"] * n, np.arange(n))))
|
| # hai it works!
| assert s[("a", 5)] == 5
| assert s[("a", 6)] == 6
| assert s[("a", 7)] == 7
|
| _index._SIZE_CUTOFF = old_cutoff
|
| def test_multi_nan_indexing(self):
| # GH 3588
| df = DataFrame(
| {
| "a": ["R1", "R2", np.nan, "R4"],
| "b": ["C1", "C2", "C3", "C4"],
| "c": [10, 15, np.nan, 20],
| }
| )
| result = df.set_index(["a", "b"], drop=False)
| expected = DataFrame(
| {
| "a": ["R1", "R2", np.nan, "R4"],
| "b": ["C1", "C2", "C3", "C4"],
| "c": [10, 15, np.nan, 20],
| },
| index=[
| Index(["R1", "R2", np.nan, "R4"], name="a"),
| Index(["C1", "C2", "C3", "C4"], name="b"),
| ],
| )
| tm.assert_frame_equal(result, expected)
|
| def test_exclusive_nat_column_indexing(self):
| # GH 38025
| # test multi indexing when one column exclusively contains NaT values
| df = DataFrame(
| {
| "a": [pd.NaT, pd.NaT, pd.NaT, pd.NaT],
| "b": ["C1", "C2", "C3", "C4"],
| "c": [10, 15, np.nan, 20],
| }
| )
| df = df.set_index(["a", "b"])
| expected = DataFrame(
| {
| "c": [10, 15, np.nan, 20],
| },
| index=[
| Index([pd.NaT, pd.NaT, pd.NaT, pd.NaT], name="a"),
| Index(["C1", "C2", "C3", "C4"], name="b"),
| ],
| )
| tm.assert_frame_equal(df, expected)
|
| def test_nested_tuples_duplicates(self):
| # GH#30892
|
| dti = pd.to_datetime(["20190101", "20190101", "20190102"])
| idx = Index(["a", "a", "c"])
| mi = MultiIndex.from_arrays([dti, idx], names=["index1", "index2"])
|
| df = DataFrame({"c1": [1, 2, 3], "c2": [np.nan, np.nan, np.nan]}, index=mi)
|
| expected = DataFrame({"c1": df["c1"], "c2": [1.0, 1.0, np.nan]}, index=mi)
|
| df2 = df.copy(deep=True)
| df2.loc[(dti[0], "a"), "c2"] = 1.0
| tm.assert_frame_equal(df2, expected)
|
| df3 = df.copy(deep=True)
| df3.loc[[(dti[0], "a")], "c2"] = 1.0
| tm.assert_frame_equal(df3, expected)
|
| def test_multiindex_with_datatime_level_preserves_freq(self):
| # https://github.com/pandas-dev/pandas/issues/35563
| idx = Index(range(2), name="A")
| dti = pd.date_range("2020-01-01", periods=7, freq="D", name="B")
| mi = MultiIndex.from_product([idx, dti])
| df = DataFrame(np.random.randn(14, 2), index=mi)
| result = df.loc[0].index
| tm.assert_index_equal(result, dti)
| assert result.freq == dti.freq
|
| def test_multiindex_complex(self):
| # GH#42145
| complex_data = [1 + 2j, 4 - 3j, 10 - 1j]
| non_complex_data = [3, 4, 5]
| result = DataFrame(
| {
| "x": complex_data,
| "y": non_complex_data,
| "z": non_complex_data,
| }
| )
| result.set_index(["x", "y"], inplace=True)
| expected = DataFrame(
| {"z": non_complex_data},
| index=MultiIndex.from_arrays(
| [complex_data, non_complex_data],
| names=("x", "y"),
| ),
| )
| tm.assert_frame_equal(result, expected)
|
| def test_rename_multiindex_with_duplicates(self):
| # GH 38015
| mi = MultiIndex.from_tuples([("A", "cat"), ("B", "cat"), ("B", "cat")])
| df = DataFrame(index=mi)
| df = df.rename(index={"A": "Apple"}, level=0)
|
| mi2 = MultiIndex.from_tuples([("Apple", "cat"), ("B", "cat"), ("B", "cat")])
| expected = DataFrame(index=mi2)
| tm.assert_frame_equal(df, expected)
|
| def test_series_align_multiindex_with_nan_overlap_only(self):
| # GH 38439
| mi1 = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]])
| mi2 = MultiIndex.from_arrays([[np.nan, 82.0], [np.nan, np.nan]])
| ser1 = Series([1, 2], index=mi1)
| ser2 = Series([1, 2], index=mi2)
| result1, result2 = ser1.align(ser2)
|
| mi = MultiIndex.from_arrays([[81.0, 82.0, np.nan], [np.nan, np.nan, np.nan]])
| expected1 = Series([1.0, np.nan, 2.0], index=mi)
| expected2 = Series([np.nan, 2.0, 1.0], index=mi)
|
| tm.assert_series_equal(result1, expected1)
| tm.assert_series_equal(result2, expected2)
|
| def test_series_align_multiindex_with_nan(self):
| # GH 38439
| mi1 = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]])
| mi2 = MultiIndex.from_arrays([[np.nan, 81.0], [np.nan, np.nan]])
| ser1 = Series([1, 2], index=mi1)
| ser2 = Series([1, 2], index=mi2)
| result1, result2 = ser1.align(ser2)
|
| mi = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]])
| expected1 = Series([1, 2], index=mi)
| expected2 = Series([2, 1], index=mi)
|
| tm.assert_series_equal(result1, expected1)
| tm.assert_series_equal(result2, expected2)
|
| def test_nunique_smoke(self):
| # GH 34019
| n = DataFrame([[1, 2], [1, 2]]).set_index([0, 1]).index.nunique()
| assert n == 1
|
| def test_multiindex_repeated_keys(self):
| # GH19414
| tm.assert_series_equal(
| Series([1, 2], MultiIndex.from_arrays([["a", "b"]])).loc[
| ["a", "a", "b", "b"]
| ],
| Series([1, 1, 2, 2], MultiIndex.from_arrays([["a", "a", "b", "b"]])),
| )
|
| def test_multiindex_with_na_missing_key(self):
| # GH46173
| df = DataFrame.from_dict(
| {
| ("foo",): [1, 2, 3],
| ("bar",): [5, 6, 7],
| (None,): [8, 9, 0],
| }
| )
| with pytest.raises(KeyError, match="missing_key"):
| df[[("missing_key",)]]
|
|
