512 lines
18 KiB
Python
512 lines
18 KiB
Python
"""
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Collection of tests asserting things that should be true for
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any index subclass except for MultiIndex. Makes use of the `index_flat`
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fixture defined in pandas/conftest.py.
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"""
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from copy import (
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copy,
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deepcopy,
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)
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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.compat import IS64
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from pandas.compat.numpy import np_version_gte1p25
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from pandas.core.dtypes.common import (
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is_integer_dtype,
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is_numeric_dtype,
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)
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import pandas as pd
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from pandas import (
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CategoricalIndex,
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MultiIndex,
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PeriodIndex,
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RangeIndex,
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)
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import pandas._testing as tm
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class TestCommon:
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@pytest.mark.parametrize("name", [None, "new_name"])
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def test_to_frame(self, name, index_flat, using_copy_on_write):
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# see GH#15230, GH#22580
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idx = index_flat
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if name:
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idx_name = name
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else:
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idx_name = idx.name or 0
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df = idx.to_frame(name=idx_name)
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assert df.index is idx
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assert len(df.columns) == 1
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assert df.columns[0] == idx_name
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if not using_copy_on_write:
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assert df[idx_name].values is not idx.values
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df = idx.to_frame(index=False, name=idx_name)
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assert df.index is not idx
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def test_droplevel(self, index_flat):
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# GH 21115
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# MultiIndex is tested separately in test_multi.py
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index = index_flat
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assert index.droplevel([]).equals(index)
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for level in [index.name, [index.name]]:
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if isinstance(index.name, tuple) and level is index.name:
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# GH 21121 : droplevel with tuple name
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continue
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msg = (
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"Cannot remove 1 levels from an index with 1 levels: at least one "
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"level must be left."
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)
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with pytest.raises(ValueError, match=msg):
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index.droplevel(level)
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for level in "wrong", ["wrong"]:
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with pytest.raises(
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KeyError,
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match=r"'Requested level \(wrong\) does not match index name \(None\)'",
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):
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index.droplevel(level)
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def test_constructor_non_hashable_name(self, index_flat):
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# GH 20527
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index = index_flat
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message = "Index.name must be a hashable type"
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renamed = [["1"]]
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# With .rename()
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with pytest.raises(TypeError, match=message):
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index.rename(name=renamed)
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# With .set_names()
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with pytest.raises(TypeError, match=message):
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index.set_names(names=renamed)
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def test_constructor_unwraps_index(self, index_flat):
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a = index_flat
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# Passing dtype is necessary for Index([True, False], dtype=object)
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# case.
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b = type(a)(a, dtype=a.dtype)
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tm.assert_equal(a._data, b._data)
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def test_to_flat_index(self, index_flat):
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# 22866
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index = index_flat
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result = index.to_flat_index()
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tm.assert_index_equal(result, index)
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def test_set_name_methods(self, index_flat):
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# MultiIndex tested separately
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index = index_flat
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new_name = "This is the new name for this index"
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original_name = index.name
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new_ind = index.set_names([new_name])
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assert new_ind.name == new_name
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assert index.name == original_name
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res = index.rename(new_name, inplace=True)
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# should return None
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assert res is None
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assert index.name == new_name
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assert index.names == [new_name]
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with pytest.raises(ValueError, match="Level must be None"):
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index.set_names("a", level=0)
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# rename in place just leaves tuples and other containers alone
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name = ("A", "B")
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index.rename(name, inplace=True)
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assert index.name == name
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assert index.names == [name]
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@pytest.mark.xfail
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def test_set_names_single_label_no_level(self, index_flat):
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with pytest.raises(TypeError, match="list-like"):
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# should still fail even if it would be the right length
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index_flat.set_names("a")
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def test_copy_and_deepcopy(self, index_flat):
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index = index_flat
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for func in (copy, deepcopy):
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idx_copy = func(index)
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assert idx_copy is not index
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assert idx_copy.equals(index)
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new_copy = index.copy(deep=True, name="banana")
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assert new_copy.name == "banana"
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def test_copy_name(self, index_flat):
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# GH#12309: Check that the "name" argument
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# passed at initialization is honored.
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index = index_flat
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first = type(index)(index, copy=True, name="mario")
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second = type(first)(first, copy=False)
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# Even though "copy=False", we want a new object.
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assert first is not second
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tm.assert_index_equal(first, second)
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# Not using tm.assert_index_equal() since names differ.
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assert index.equals(first)
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assert first.name == "mario"
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assert second.name == "mario"
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# TODO: belongs in series arithmetic tests?
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s1 = pd.Series(2, index=first)
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s2 = pd.Series(3, index=second[:-1])
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# See GH#13365
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s3 = s1 * s2
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assert s3.index.name == "mario"
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def test_copy_name2(self, index_flat):
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# GH#35592
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index = index_flat
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assert index.copy(name="mario").name == "mario"
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with pytest.raises(ValueError, match="Length of new names must be 1, got 2"):
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index.copy(name=["mario", "luigi"])
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msg = f"{type(index).__name__}.name must be a hashable type"
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with pytest.raises(TypeError, match=msg):
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index.copy(name=[["mario"]])
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def test_unique_level(self, index_flat):
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# don't test a MultiIndex here (as its tested separated)
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index = index_flat
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# GH 17896
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expected = index.drop_duplicates()
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for level in [0, index.name, None]:
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result = index.unique(level=level)
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tm.assert_index_equal(result, expected)
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msg = "Too many levels: Index has only 1 level, not 4"
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with pytest.raises(IndexError, match=msg):
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index.unique(level=3)
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msg = (
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rf"Requested level \(wrong\) does not match index name "
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rf"\({re.escape(index.name.__repr__())}\)"
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)
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with pytest.raises(KeyError, match=msg):
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index.unique(level="wrong")
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def test_unique(self, index_flat):
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# MultiIndex tested separately
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index = index_flat
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if not len(index):
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pytest.skip("Skip check for empty Index and MultiIndex")
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idx = index[[0] * 5]
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idx_unique = index[[0]]
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# We test against `idx_unique`, so first we make sure it's unique
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# and doesn't contain nans.
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assert idx_unique.is_unique is True
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try:
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assert idx_unique.hasnans is False
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except NotImplementedError:
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pass
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result = idx.unique()
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tm.assert_index_equal(result, idx_unique)
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# nans:
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if not index._can_hold_na:
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pytest.skip("Skip na-check if index cannot hold na")
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vals = index._values[[0] * 5]
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vals[0] = np.nan
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vals_unique = vals[:2]
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idx_nan = index._shallow_copy(vals)
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idx_unique_nan = index._shallow_copy(vals_unique)
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assert idx_unique_nan.is_unique is True
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assert idx_nan.dtype == index.dtype
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assert idx_unique_nan.dtype == index.dtype
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expected = idx_unique_nan
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for pos, i in enumerate([idx_nan, idx_unique_nan]):
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result = i.unique()
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tm.assert_index_equal(result, expected)
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@pytest.mark.filterwarnings("ignore:Period with BDay freq:FutureWarning")
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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def test_searchsorted_monotonic(self, index_flat, request):
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# GH17271
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index = index_flat
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# not implemented for tuple searches in MultiIndex
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# or Intervals searches in IntervalIndex
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if isinstance(index, pd.IntervalIndex):
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mark = pytest.mark.xfail(
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reason="IntervalIndex.searchsorted does not support Interval arg",
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raises=NotImplementedError,
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)
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request.applymarker(mark)
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# nothing to test if the index is empty
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if index.empty:
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pytest.skip("Skip check for empty Index")
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value = index[0]
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# determine the expected results (handle dupes for 'right')
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expected_left, expected_right = 0, (index == value).argmin()
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if expected_right == 0:
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# all values are the same, expected_right should be length
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expected_right = len(index)
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# test _searchsorted_monotonic in all cases
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# test searchsorted only for increasing
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if index.is_monotonic_increasing:
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ssm_left = index._searchsorted_monotonic(value, side="left")
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assert expected_left == ssm_left
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ssm_right = index._searchsorted_monotonic(value, side="right")
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assert expected_right == ssm_right
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ss_left = index.searchsorted(value, side="left")
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assert expected_left == ss_left
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ss_right = index.searchsorted(value, side="right")
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assert expected_right == ss_right
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elif index.is_monotonic_decreasing:
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ssm_left = index._searchsorted_monotonic(value, side="left")
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assert expected_left == ssm_left
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ssm_right = index._searchsorted_monotonic(value, side="right")
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assert expected_right == ssm_right
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else:
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# non-monotonic should raise.
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msg = "index must be monotonic increasing or decreasing"
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with pytest.raises(ValueError, match=msg):
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index._searchsorted_monotonic(value, side="left")
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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def test_drop_duplicates(self, index_flat, keep):
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# MultiIndex is tested separately
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index = index_flat
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if isinstance(index, RangeIndex):
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pytest.skip(
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"RangeIndex is tested in test_drop_duplicates_no_duplicates "
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"as it cannot hold duplicates"
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)
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if len(index) == 0:
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pytest.skip(
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"empty index is tested in test_drop_duplicates_no_duplicates "
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"as it cannot hold duplicates"
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)
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# make unique index
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holder = type(index)
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unique_values = list(set(index))
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dtype = index.dtype if is_numeric_dtype(index) else None
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unique_idx = holder(unique_values, dtype=dtype)
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# make duplicated index
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n = len(unique_idx)
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duplicated_selection = np.random.default_rng(2).choice(n, int(n * 1.5))
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idx = holder(unique_idx.values[duplicated_selection])
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# Series.duplicated is tested separately
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expected_duplicated = (
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pd.Series(duplicated_selection).duplicated(keep=keep).values
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)
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tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected_duplicated)
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# Series.drop_duplicates is tested separately
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expected_dropped = holder(pd.Series(idx).drop_duplicates(keep=keep))
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tm.assert_index_equal(idx.drop_duplicates(keep=keep), expected_dropped)
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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def test_drop_duplicates_no_duplicates(self, index_flat):
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# MultiIndex is tested separately
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index = index_flat
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# make unique index
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if isinstance(index, RangeIndex):
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# RangeIndex cannot have duplicates
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unique_idx = index
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else:
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holder = type(index)
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unique_values = list(set(index))
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dtype = index.dtype if is_numeric_dtype(index) else None
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unique_idx = holder(unique_values, dtype=dtype)
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# check on unique index
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expected_duplicated = np.array([False] * len(unique_idx), dtype="bool")
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tm.assert_numpy_array_equal(unique_idx.duplicated(), expected_duplicated)
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result_dropped = unique_idx.drop_duplicates()
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tm.assert_index_equal(result_dropped, unique_idx)
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# validate shallow copy
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assert result_dropped is not unique_idx
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def test_drop_duplicates_inplace(self, index):
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msg = r"drop_duplicates\(\) got an unexpected keyword argument"
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with pytest.raises(TypeError, match=msg):
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index.drop_duplicates(inplace=True)
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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def test_has_duplicates(self, index_flat):
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# MultiIndex tested separately in:
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# tests/indexes/multi/test_unique_and_duplicates.
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index = index_flat
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holder = type(index)
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if not len(index) or isinstance(index, RangeIndex):
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# MultiIndex tested separately in:
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# tests/indexes/multi/test_unique_and_duplicates.
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# RangeIndex is unique by definition.
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pytest.skip("Skip check for empty Index, MultiIndex, and RangeIndex")
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idx = holder([index[0]] * 5)
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assert idx.is_unique is False
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assert idx.has_duplicates is True
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@pytest.mark.parametrize(
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"dtype",
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["int64", "uint64", "float64", "category", "datetime64[ns]", "timedelta64[ns]"],
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)
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def test_astype_preserves_name(self, index, dtype):
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# https://github.com/pandas-dev/pandas/issues/32013
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if isinstance(index, MultiIndex):
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index.names = ["idx" + str(i) for i in range(index.nlevels)]
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else:
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index.name = "idx"
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warn = None
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if index.dtype.kind == "c" and dtype in ["float64", "int64", "uint64"]:
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# imaginary components discarded
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if np_version_gte1p25:
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warn = np.exceptions.ComplexWarning
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else:
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warn = np.ComplexWarning
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is_pyarrow_str = str(index.dtype) == "string[pyarrow]" and dtype == "category"
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try:
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# Some of these conversions cannot succeed so we use a try / except
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with tm.assert_produces_warning(
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warn,
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raise_on_extra_warnings=is_pyarrow_str,
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check_stacklevel=False,
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):
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result = index.astype(dtype)
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except (ValueError, TypeError, NotImplementedError, SystemError):
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return
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if isinstance(index, MultiIndex):
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assert result.names == index.names
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else:
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assert result.name == index.name
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def test_hasnans_isnans(self, index_flat):
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# GH#11343, added tests for hasnans / isnans
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index = index_flat
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# cases in indices doesn't include NaN
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idx = index.copy(deep=True)
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expected = np.array([False] * len(idx), dtype=bool)
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tm.assert_numpy_array_equal(idx._isnan, expected)
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assert idx.hasnans is False
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idx = index.copy(deep=True)
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values = idx._values
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if len(index) == 0:
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return
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elif is_integer_dtype(index.dtype):
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return
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elif index.dtype == bool:
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# values[1] = np.nan below casts to True!
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return
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values[1] = np.nan
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idx = type(index)(values)
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expected = np.array([False] * len(idx), dtype=bool)
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expected[1] = True
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tm.assert_numpy_array_equal(idx._isnan, expected)
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assert idx.hasnans is True
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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@pytest.mark.parametrize("na_position", [None, "middle"])
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def test_sort_values_invalid_na_position(index_with_missing, na_position):
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with pytest.raises(ValueError, match=f"invalid na_position: {na_position}"):
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index_with_missing.sort_values(na_position=na_position)
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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@pytest.mark.parametrize("na_position", ["first", "last"])
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def test_sort_values_with_missing(index_with_missing, na_position, request):
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# GH 35584. Test that sort_values works with missing values,
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# sort non-missing and place missing according to na_position
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if isinstance(index_with_missing, CategoricalIndex):
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request.applymarker(
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pytest.mark.xfail(
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reason="missing value sorting order not well-defined", strict=False
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)
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)
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missing_count = np.sum(index_with_missing.isna())
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not_na_vals = index_with_missing[index_with_missing.notna()].values
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sorted_values = np.sort(not_na_vals)
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if na_position == "first":
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sorted_values = np.concatenate([[None] * missing_count, sorted_values])
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else:
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sorted_values = np.concatenate([sorted_values, [None] * missing_count])
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# Explicitly pass dtype needed for Index backed by EA e.g. IntegerArray
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expected = type(index_with_missing)(sorted_values, dtype=index_with_missing.dtype)
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result = index_with_missing.sort_values(na_position=na_position)
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tm.assert_index_equal(result, expected)
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def test_ndarray_compat_properties(index):
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if isinstance(index, PeriodIndex) and not IS64:
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pytest.skip("Overflow")
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idx = index
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assert idx.T.equals(idx)
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assert idx.transpose().equals(idx)
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values = idx.values
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assert idx.shape == values.shape
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assert idx.ndim == values.ndim
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assert idx.size == values.size
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if not isinstance(index, (RangeIndex, MultiIndex)):
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# These two are not backed by an ndarray
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assert idx.nbytes == values.nbytes
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# test for validity
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idx.nbytes
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idx.values.nbytes
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def test_compare_read_only_array():
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# GH#57130
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arr = np.array([], dtype=object)
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arr.flags.writeable = False
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idx = pd.Index(arr)
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result = idx > 69
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assert result.dtype == bool
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