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