281 lines
8.4 KiB
Python
281 lines
8.4 KiB
Python
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import re
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import numpy as np
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import pytest
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from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
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import pandas as pd
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from pandas import (
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IntervalIndex,
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MultiIndex,
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RangeIndex,
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)
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import pandas._testing as tm
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from pandas.core.api import Int64Index
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def test_labels_dtypes():
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# GH 8456
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i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
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assert i.codes[0].dtype == "int8"
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assert i.codes[1].dtype == "int8"
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i = MultiIndex.from_product([["a"], range(40)])
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assert i.codes[1].dtype == "int8"
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i = MultiIndex.from_product([["a"], range(400)])
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assert i.codes[1].dtype == "int16"
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i = MultiIndex.from_product([["a"], range(40000)])
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assert i.codes[1].dtype == "int32"
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i = MultiIndex.from_product([["a"], range(1000)])
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assert (i.codes[0] >= 0).all()
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assert (i.codes[1] >= 0).all()
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def test_values_boxed():
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tuples = [
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(1, pd.Timestamp("2000-01-01")),
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(2, pd.NaT),
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(3, pd.Timestamp("2000-01-03")),
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(1, pd.Timestamp("2000-01-04")),
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(2, pd.Timestamp("2000-01-02")),
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(3, pd.Timestamp("2000-01-03")),
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]
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result = MultiIndex.from_tuples(tuples)
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expected = construct_1d_object_array_from_listlike(tuples)
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tm.assert_numpy_array_equal(result.values, expected)
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# Check that code branches for boxed values produce identical results
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tm.assert_numpy_array_equal(result.values[:4], result[:4].values)
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def test_values_multiindex_datetimeindex():
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# Test to ensure we hit the boxing / nobox part of MI.values
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ints = np.arange(10**18, 10**18 + 5)
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naive = pd.DatetimeIndex(ints)
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aware = pd.DatetimeIndex(ints, tz="US/Central")
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idx = MultiIndex.from_arrays([naive, aware])
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result = idx.values
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outer = pd.DatetimeIndex([x[0] for x in result])
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tm.assert_index_equal(outer, naive)
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inner = pd.DatetimeIndex([x[1] for x in result])
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tm.assert_index_equal(inner, aware)
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# n_lev > n_lab
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result = idx[:2].values
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outer = pd.DatetimeIndex([x[0] for x in result])
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tm.assert_index_equal(outer, naive[:2])
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inner = pd.DatetimeIndex([x[1] for x in result])
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tm.assert_index_equal(inner, aware[:2])
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def test_values_multiindex_periodindex():
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# Test to ensure we hit the boxing / nobox part of MI.values
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ints = np.arange(2007, 2012)
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pidx = pd.PeriodIndex(ints, freq="D")
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idx = MultiIndex.from_arrays([ints, pidx])
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result = idx.values
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outer = Int64Index([x[0] for x in result])
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tm.assert_index_equal(outer, Int64Index(ints))
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inner = pd.PeriodIndex([x[1] for x in result])
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tm.assert_index_equal(inner, pidx)
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# n_lev > n_lab
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result = idx[:2].values
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outer = Int64Index([x[0] for x in result])
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tm.assert_index_equal(outer, Int64Index(ints[:2]))
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inner = pd.PeriodIndex([x[1] for x in result])
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tm.assert_index_equal(inner, pidx[:2])
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def test_consistency():
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# need to construct an overflow
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major_axis = list(range(70000))
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minor_axis = list(range(10))
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major_codes = np.arange(70000)
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minor_codes = np.repeat(range(10), 7000)
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# the fact that is works means it's consistent
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index = MultiIndex(
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levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
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)
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# inconsistent
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major_codes = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])
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minor_codes = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])
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index = MultiIndex(
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levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
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)
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assert index.is_unique is False
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@pytest.mark.slow
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def test_hash_collisions():
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# non-smoke test that we don't get hash collisions
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index = MultiIndex.from_product(
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[np.arange(1000), np.arange(1000)], names=["one", "two"]
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)
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result = index.get_indexer(index.values)
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tm.assert_numpy_array_equal(result, np.arange(len(index), dtype="intp"))
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for i in [0, 1, len(index) - 2, len(index) - 1]:
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result = index.get_loc(index[i])
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assert result == i
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def test_dims():
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pass
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def test_take_invalid_kwargs():
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vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]]
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idx = MultiIndex.from_product(vals, names=["str", "dt"])
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indices = [1, 2]
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msg = r"take\(\) got an unexpected keyword argument 'foo'"
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with pytest.raises(TypeError, match=msg):
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idx.take(indices, foo=2)
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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idx.take(indices, out=indices)
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msg = "the 'mode' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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idx.take(indices, mode="clip")
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def test_isna_behavior(idx):
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# should not segfault GH5123
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# NOTE: if MI representation changes, may make sense to allow
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# isna(MI)
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msg = "isna is not defined for MultiIndex"
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with pytest.raises(NotImplementedError, match=msg):
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pd.isna(idx)
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def test_large_multiindex_error():
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# GH12527
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df_below_1000000 = pd.DataFrame(
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1, index=MultiIndex.from_product([[1, 2], range(499999)]), columns=["dest"]
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)
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with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
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df_below_1000000.loc[(-1, 0), "dest"]
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with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
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df_below_1000000.loc[(3, 0), "dest"]
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df_above_1000000 = pd.DataFrame(
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1, index=MultiIndex.from_product([[1, 2], range(500001)]), columns=["dest"]
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)
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with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
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df_above_1000000.loc[(-1, 0), "dest"]
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with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
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df_above_1000000.loc[(3, 0), "dest"]
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def test_million_record_attribute_error():
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# GH 18165
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r = list(range(1000000))
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df = pd.DataFrame(
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{"a": r, "b": r}, index=MultiIndex.from_tuples([(x, x) for x in r])
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)
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msg = "'Series' object has no attribute 'foo'"
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with pytest.raises(AttributeError, match=msg):
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df["a"].foo()
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def test_can_hold_identifiers(idx):
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key = idx[0]
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assert idx._can_hold_identifiers_and_holds_name(key) is True
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def test_metadata_immutable(idx):
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levels, codes = idx.levels, idx.codes
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# shouldn't be able to set at either the top level or base level
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mutable_regex = re.compile("does not support mutable operations")
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with pytest.raises(TypeError, match=mutable_regex):
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levels[0] = levels[0]
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with pytest.raises(TypeError, match=mutable_regex):
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levels[0][0] = levels[0][0]
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# ditto for labels
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with pytest.raises(TypeError, match=mutable_regex):
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codes[0] = codes[0]
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with pytest.raises(ValueError, match="assignment destination is read-only"):
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codes[0][0] = codes[0][0]
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# and for names
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names = idx.names
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with pytest.raises(TypeError, match=mutable_regex):
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names[0] = names[0]
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def test_level_setting_resets_attributes():
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ind = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
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assert ind.is_monotonic_increasing
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with tm.assert_produces_warning(FutureWarning):
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ind.set_levels([["A", "B"], [1, 3, 2]], inplace=True)
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# if this fails, probably didn't reset the cache correctly.
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assert not ind.is_monotonic_increasing
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def test_rangeindex_fallback_coercion_bug():
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# GH 12893
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foo = pd.DataFrame(np.arange(100).reshape((10, 10)))
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bar = pd.DataFrame(np.arange(100).reshape((10, 10)))
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df = pd.concat({"foo": foo.stack(), "bar": bar.stack()}, axis=1)
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df.index.names = ["fizz", "buzz"]
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str(df)
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expected = pd.DataFrame(
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{"bar": np.arange(100), "foo": np.arange(100)},
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index=MultiIndex.from_product([range(10), range(10)], names=["fizz", "buzz"]),
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)
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tm.assert_frame_equal(df, expected, check_like=True)
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result = df.index.get_level_values("fizz")
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expected = Int64Index(np.arange(10), name="fizz").repeat(10)
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tm.assert_index_equal(result, expected)
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result = df.index.get_level_values("buzz")
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expected = Int64Index(np.tile(np.arange(10), 10), name="buzz")
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tm.assert_index_equal(result, expected)
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def test_memory_usage(idx):
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result = idx.memory_usage()
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if len(idx):
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idx.get_loc(idx[0])
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result2 = idx.memory_usage()
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result3 = idx.memory_usage(deep=True)
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# RangeIndex, IntervalIndex
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# don't have engines
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if not isinstance(idx, (RangeIndex, IntervalIndex)):
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assert result2 > result
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if idx.inferred_type == "object":
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assert result3 > result2
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else:
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# we report 0 for no-length
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assert result == 0
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def test_nlevels(idx):
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assert idx.nlevels == 2
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