from __future__ import annotations
|
|
from datetime import datetime
|
import gc
|
|
import numpy as np
|
import pytest
|
|
from pandas._libs.tslibs import Timestamp
|
|
from pandas.core.dtypes.common import (
|
is_datetime64tz_dtype,
|
is_integer_dtype,
|
)
|
from pandas.core.dtypes.dtypes import CategoricalDtype
|
|
import pandas as pd
|
from pandas import (
|
CategoricalIndex,
|
DatetimeIndex,
|
Index,
|
IntervalIndex,
|
MultiIndex,
|
PeriodIndex,
|
RangeIndex,
|
Series,
|
TimedeltaIndex,
|
isna,
|
)
|
import pandas._testing as tm
|
from pandas.core.arrays import BaseMaskedArray
|
|
|
class Base:
|
"""
|
Base class for index sub-class tests.
|
"""
|
|
_index_cls: type[Index]
|
|
@pytest.fixture
|
def simple_index(self):
|
raise NotImplementedError("Method not implemented")
|
|
def create_index(self) -> Index:
|
raise NotImplementedError("Method not implemented")
|
|
def test_pickle_compat_construction(self):
|
# need an object to create with
|
msg = "|".join(
|
[
|
r"Index\(\.\.\.\) must be called with a collection of some "
|
r"kind, None was passed",
|
r"DatetimeIndex\(\) must be called with a collection of some "
|
r"kind, None was passed",
|
r"TimedeltaIndex\(\) must be called with a collection of some "
|
r"kind, None was passed",
|
r"__new__\(\) missing 1 required positional argument: 'data'",
|
r"__new__\(\) takes at least 2 arguments \(1 given\)",
|
]
|
)
|
with pytest.raises(TypeError, match=msg):
|
self._index_cls()
|
|
def test_shift(self, simple_index):
|
# GH8083 test the base class for shift
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idx = simple_index
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msg = (
|
f"This method is only implemented for DatetimeIndex, PeriodIndex and "
|
f"TimedeltaIndex; Got type {type(idx).__name__}"
|
)
|
with pytest.raises(NotImplementedError, match=msg):
|
idx.shift(1)
|
with pytest.raises(NotImplementedError, match=msg):
|
idx.shift(1, 2)
|
|
def test_constructor_name_unhashable(self, simple_index):
|
# GH#29069 check that name is hashable
|
# See also same-named test in tests.series.test_constructors
|
idx = simple_index
|
with pytest.raises(TypeError, match="Index.name must be a hashable type"):
|
type(idx)(idx, name=[])
|
|
def test_create_index_existing_name(self, simple_index):
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# GH11193, when an existing index is passed, and a new name is not
|
# specified, the new index should inherit the previous object name
|
expected = simple_index
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if not isinstance(expected, MultiIndex):
|
expected.name = "foo"
|
result = Index(expected)
|
tm.assert_index_equal(result, expected)
|
|
result = Index(expected, name="bar")
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expected.name = "bar"
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tm.assert_index_equal(result, expected)
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else:
|
expected.names = ["foo", "bar"]
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result = Index(expected)
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tm.assert_index_equal(
|
result,
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Index(
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Index(
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[
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("foo", "one"),
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("foo", "two"),
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("bar", "one"),
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("baz", "two"),
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("qux", "one"),
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("qux", "two"),
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],
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dtype="object",
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),
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names=["foo", "bar"],
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),
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)
|
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result = Index(expected, names=["A", "B"])
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tm.assert_index_equal(
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result,
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Index(
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Index(
|
[
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("foo", "one"),
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("foo", "two"),
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("bar", "one"),
|
("baz", "two"),
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("qux", "one"),
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("qux", "two"),
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],
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dtype="object",
|
),
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names=["A", "B"],
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),
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)
|
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def test_numeric_compat(self, simple_index):
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idx = simple_index
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# Check that this doesn't cover MultiIndex case, if/when it does,
|
# we can remove multi.test_compat.test_numeric_compat
|
assert not isinstance(idx, MultiIndex)
|
if type(idx) is Index:
|
return
|
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typ = type(idx._data).__name__
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cls = type(idx).__name__
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lmsg = "|".join(
|
[
|
rf"unsupported operand type\(s\) for \*: '{typ}' and 'int'",
|
"cannot perform (__mul__|__truediv__|__floordiv__) with "
|
f"this index type: ({cls}|{typ})",
|
]
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)
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with pytest.raises(TypeError, match=lmsg):
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idx * 1
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rmsg = "|".join(
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[
|
rf"unsupported operand type\(s\) for \*: 'int' and '{typ}'",
|
"cannot perform (__rmul__|__rtruediv__|__rfloordiv__) with "
|
f"this index type: ({cls}|{typ})",
|
]
|
)
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with pytest.raises(TypeError, match=rmsg):
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1 * idx
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div_err = lmsg.replace("*", "/")
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with pytest.raises(TypeError, match=div_err):
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idx / 1
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div_err = rmsg.replace("*", "/")
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with pytest.raises(TypeError, match=div_err):
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1 / idx
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floordiv_err = lmsg.replace("*", "//")
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with pytest.raises(TypeError, match=floordiv_err):
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idx // 1
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floordiv_err = rmsg.replace("*", "//")
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with pytest.raises(TypeError, match=floordiv_err):
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1 // idx
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def test_logical_compat(self, simple_index):
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idx = simple_index
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with pytest.raises(TypeError, match="cannot perform all"):
|
idx.all()
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with pytest.raises(TypeError, match="cannot perform any"):
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idx.any()
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def test_repr_roundtrip(self, simple_index):
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idx = simple_index
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tm.assert_index_equal(eval(repr(idx)), idx)
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def test_repr_max_seq_item_setting(self, simple_index):
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# GH10182
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idx = simple_index
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idx = idx.repeat(50)
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with pd.option_context("display.max_seq_items", None):
|
repr(idx)
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assert "..." not in str(idx)
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def test_ensure_copied_data(self, index):
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# Check the "copy" argument of each Index.__new__ is honoured
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# GH12309
|
init_kwargs = {}
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if isinstance(index, PeriodIndex):
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# Needs "freq" specification:
|
init_kwargs["freq"] = index.freq
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elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
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# RangeIndex cannot be initialized from data
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# MultiIndex and CategoricalIndex are tested separately
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return
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elif index.dtype == object and index.inferred_type == "boolean":
|
init_kwargs["dtype"] = index.dtype
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index_type = type(index)
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result = index_type(index.values, copy=True, **init_kwargs)
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if is_datetime64tz_dtype(index.dtype):
|
result = result.tz_localize("UTC").tz_convert(index.tz)
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if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
|
index = index._with_freq(None)
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tm.assert_index_equal(index, result)
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if isinstance(index, PeriodIndex):
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# .values an object array of Period, thus copied
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result = index_type(ordinal=index.asi8, copy=False, **init_kwargs)
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tm.assert_numpy_array_equal(index.asi8, result.asi8, check_same="same")
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elif isinstance(index, IntervalIndex):
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# checked in test_interval.py
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pass
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elif type(index) is Index and not isinstance(index.dtype, np.dtype):
|
result = index_type(index.values, copy=False, **init_kwargs)
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tm.assert_index_equal(result, index)
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if isinstance(index._values, BaseMaskedArray):
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assert np.shares_memory(index._values._data, result._values._data)
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tm.assert_numpy_array_equal(
|
index._values._data, result._values._data, check_same="same"
|
)
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assert np.shares_memory(index._values._mask, result._values._mask)
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tm.assert_numpy_array_equal(
|
index._values._mask, result._values._mask, check_same="same"
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)
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elif index.dtype == "string[python]":
|
assert np.shares_memory(index._values._ndarray, result._values._ndarray)
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tm.assert_numpy_array_equal(
|
index._values._ndarray, result._values._ndarray, check_same="same"
|
)
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elif index.dtype == "string[pyarrow]":
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assert tm.shares_memory(result._values, index._values)
|
else:
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raise NotImplementedError(index.dtype)
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else:
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result = index_type(index.values, copy=False, **init_kwargs)
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tm.assert_numpy_array_equal(index.values, result.values, check_same="same")
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def test_memory_usage(self, index):
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index._engine.clear_mapping()
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result = index.memory_usage()
|
if index.empty:
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# we report 0 for no-length
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assert result == 0
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return
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# non-zero length
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index.get_loc(index[0])
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result2 = index.memory_usage()
|
result3 = index.memory_usage(deep=True)
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# RangeIndex, IntervalIndex
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# don't have engines
|
# Index[EA] has engine but it does not have a Hashtable .mapping
|
if not isinstance(index, (RangeIndex, IntervalIndex)) and not (
|
type(index) is Index and not isinstance(index.dtype, np.dtype)
|
):
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assert result2 > result
|
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if index.inferred_type == "object":
|
assert result3 > result2
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def test_argsort(self, index):
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# separately tested
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if isinstance(index, CategoricalIndex):
|
return
|
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result = index.argsort()
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expected = np.array(index).argsort()
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tm.assert_numpy_array_equal(result, expected, check_dtype=False)
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def test_numpy_argsort(self, index):
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result = np.argsort(index)
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expected = index.argsort()
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tm.assert_numpy_array_equal(result, expected)
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result = np.argsort(index, kind="mergesort")
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expected = index.argsort(kind="mergesort")
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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(index, (CategoricalIndex, RangeIndex)):
|
msg = "the 'axis' parameter is not supported"
|
with pytest.raises(ValueError, match=msg):
|
np.argsort(index, axis=1)
|
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msg = "the 'order' parameter is not supported"
|
with pytest.raises(ValueError, match=msg):
|
np.argsort(index, order=("a", "b"))
|
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def test_repeat(self, simple_index):
|
rep = 2
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idx = simple_index.copy()
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new_index_cls = idx._constructor
|
expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
|
tm.assert_index_equal(idx.repeat(rep), expected)
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idx = simple_index
|
rep = np.arange(len(idx))
|
expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
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tm.assert_index_equal(idx.repeat(rep), expected)
|
|
def test_numpy_repeat(self, simple_index):
|
rep = 2
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idx = simple_index
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expected = idx.repeat(rep)
|
tm.assert_index_equal(np.repeat(idx, rep), expected)
|
|
msg = "the 'axis' parameter is not supported"
|
with pytest.raises(ValueError, match=msg):
|
np.repeat(idx, rep, axis=0)
|
|
def test_where(self, listlike_box, simple_index):
|
klass = listlike_box
|
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idx = simple_index
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if isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
|
# where does not preserve freq
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idx = idx._with_freq(None)
|
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cond = [True] * len(idx)
|
result = idx.where(klass(cond))
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expected = idx
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tm.assert_index_equal(result, expected)
|
|
cond = [False] + [True] * len(idx[1:])
|
expected = Index([idx._na_value] + idx[1:].tolist(), dtype=idx.dtype)
|
result = idx.where(klass(cond))
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tm.assert_index_equal(result, expected)
|
|
def test_insert_base(self, index):
|
result = index[1:4]
|
|
if not len(index):
|
return
|
|
# test 0th element
|
assert index[0:4].equals(result.insert(0, index[0]))
|
|
def test_insert_out_of_bounds(self, index):
|
# TypeError/IndexError matches what np.insert raises in these cases
|
|
if len(index) > 0:
|
err = TypeError
|
else:
|
err = IndexError
|
if len(index) == 0:
|
# 0 vs 0.5 in error message varies with numpy version
|
msg = "index (0|0.5) is out of bounds for axis 0 with size 0"
|
else:
|
msg = "slice indices must be integers or None or have an __index__ method"
|
with pytest.raises(err, match=msg):
|
index.insert(0.5, "foo")
|
|
msg = "|".join(
|
[
|
r"index -?\d+ is out of bounds for axis 0 with size \d+",
|
"loc must be an integer between",
|
]
|
)
|
with pytest.raises(IndexError, match=msg):
|
index.insert(len(index) + 1, 1)
|
|
with pytest.raises(IndexError, match=msg):
|
index.insert(-len(index) - 1, 1)
|
|
def test_delete_base(self, index):
|
if not len(index):
|
return
|
|
if isinstance(index, RangeIndex):
|
# tested in class
|
return
|
|
expected = index[1:]
|
result = index.delete(0)
|
assert result.equals(expected)
|
assert result.name == expected.name
|
|
expected = index[:-1]
|
result = index.delete(-1)
|
assert result.equals(expected)
|
assert result.name == expected.name
|
|
length = len(index)
|
msg = f"index {length} is out of bounds for axis 0 with size {length}"
|
with pytest.raises(IndexError, match=msg):
|
index.delete(length)
|
|
def test_equals(self, index):
|
if isinstance(index, IntervalIndex):
|
# IntervalIndex tested separately, the index.equals(index.astype(object))
|
# fails for IntervalIndex
|
return
|
|
is_ea_idx = type(index) is Index and not isinstance(index.dtype, np.dtype)
|
|
assert index.equals(index)
|
assert index.equals(index.copy())
|
if not is_ea_idx:
|
# doesn't hold for e.g. IntegerDtype
|
assert index.equals(index.astype(object))
|
|
assert not index.equals(list(index))
|
assert not index.equals(np.array(index))
|
|
# Cannot pass in non-int64 dtype to RangeIndex
|
if not isinstance(index, RangeIndex) and not is_ea_idx:
|
same_values = Index(index, dtype=object)
|
assert index.equals(same_values)
|
assert same_values.equals(index)
|
|
if index.nlevels == 1:
|
# do not test MultiIndex
|
assert not index.equals(Series(index))
|
|
def test_equals_op(self, simple_index):
|
# GH9947, GH10637
|
index_a = simple_index
|
|
n = len(index_a)
|
index_b = index_a[0:-1]
|
index_c = index_a[0:-1].append(index_a[-2:-1])
|
index_d = index_a[0:1]
|
|
msg = "Lengths must match|could not be broadcast"
|
with pytest.raises(ValueError, match=msg):
|
index_a == index_b
|
expected1 = np.array([True] * n)
|
expected2 = np.array([True] * (n - 1) + [False])
|
tm.assert_numpy_array_equal(index_a == index_a, expected1)
|
tm.assert_numpy_array_equal(index_a == index_c, expected2)
|
|
# test comparisons with numpy arrays
|
array_a = np.array(index_a)
|
array_b = np.array(index_a[0:-1])
|
array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
|
array_d = np.array(index_a[0:1])
|
with pytest.raises(ValueError, match=msg):
|
index_a == array_b
|
tm.assert_numpy_array_equal(index_a == array_a, expected1)
|
tm.assert_numpy_array_equal(index_a == array_c, expected2)
|
|
# test comparisons with Series
|
series_a = Series(array_a)
|
series_b = Series(array_b)
|
series_c = Series(array_c)
|
series_d = Series(array_d)
|
with pytest.raises(ValueError, match=msg):
|
index_a == series_b
|
|
tm.assert_numpy_array_equal(index_a == series_a, expected1)
|
tm.assert_numpy_array_equal(index_a == series_c, expected2)
|
|
# cases where length is 1 for one of them
|
with pytest.raises(ValueError, match="Lengths must match"):
|
index_a == index_d
|
with pytest.raises(ValueError, match="Lengths must match"):
|
index_a == series_d
|
with pytest.raises(ValueError, match="Lengths must match"):
|
index_a == array_d
|
msg = "Can only compare identically-labeled Series objects"
|
with pytest.raises(ValueError, match=msg):
|
series_a == series_d
|
with pytest.raises(ValueError, match="Lengths must match"):
|
series_a == array_d
|
|
# comparing with a scalar should broadcast; note that we are excluding
|
# MultiIndex because in this case each item in the index is a tuple of
|
# length 2, and therefore is considered an array of length 2 in the
|
# comparison instead of a scalar
|
if not isinstance(index_a, MultiIndex):
|
expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
|
# assuming the 2nd to last item is unique in the data
|
item = index_a[-2]
|
tm.assert_numpy_array_equal(index_a == item, expected3)
|
tm.assert_series_equal(series_a == item, Series(expected3))
|
|
def test_format(self, simple_index):
|
# GH35439
|
idx = simple_index
|
expected = [str(x) for x in idx]
|
assert idx.format() == expected
|
|
def test_format_empty(self):
|
# GH35712
|
empty_idx = self._index_cls([])
|
assert empty_idx.format() == []
|
assert empty_idx.format(name=True) == [""]
|
|
def test_fillna(self, index):
|
# GH 11343
|
if len(index) == 0:
|
return
|
elif index.dtype == bool:
|
# can't hold NAs
|
return
|
elif isinstance(index, Index) and is_integer_dtype(index.dtype):
|
return
|
elif isinstance(index, MultiIndex):
|
idx = index.copy(deep=True)
|
msg = "isna is not defined for MultiIndex"
|
with pytest.raises(NotImplementedError, match=msg):
|
idx.fillna(idx[0])
|
else:
|
idx = index.copy(deep=True)
|
result = idx.fillna(idx[0])
|
tm.assert_index_equal(result, idx)
|
assert result is not idx
|
|
msg = "'value' must be a scalar, passed: "
|
with pytest.raises(TypeError, match=msg):
|
idx.fillna([idx[0]])
|
|
idx = index.copy(deep=True)
|
values = idx._values
|
|
values[1] = np.nan
|
|
idx = type(index)(values)
|
|
msg = "does not support 'downcast'"
|
with pytest.raises(NotImplementedError, match=msg):
|
# For now at least, we only raise if there are NAs present
|
idx.fillna(idx[0], downcast="infer")
|
|
expected = np.array([False] * len(idx), dtype=bool)
|
expected[1] = True
|
tm.assert_numpy_array_equal(idx._isnan, expected)
|
assert idx.hasnans is True
|
|
def test_nulls(self, index):
|
# this is really a smoke test for the methods
|
# as these are adequately tested for function elsewhere
|
if len(index) == 0:
|
tm.assert_numpy_array_equal(index.isna(), np.array([], dtype=bool))
|
elif isinstance(index, MultiIndex):
|
idx = index.copy()
|
msg = "isna is not defined for MultiIndex"
|
with pytest.raises(NotImplementedError, match=msg):
|
idx.isna()
|
elif not index.hasnans:
|
tm.assert_numpy_array_equal(index.isna(), np.zeros(len(index), dtype=bool))
|
tm.assert_numpy_array_equal(index.notna(), np.ones(len(index), dtype=bool))
|
else:
|
result = isna(index)
|
tm.assert_numpy_array_equal(index.isna(), result)
|
tm.assert_numpy_array_equal(index.notna(), ~result)
|
|
def test_empty(self, simple_index):
|
# GH 15270
|
idx = simple_index
|
assert not idx.empty
|
assert idx[:0].empty
|
|
def test_join_self_unique(self, join_type, simple_index):
|
idx = simple_index
|
if idx.is_unique:
|
joined = idx.join(idx, how=join_type)
|
assert (idx == joined).all()
|
|
def test_map(self, simple_index):
|
# callable
|
idx = simple_index
|
|
result = idx.map(lambda x: x)
|
# RangeIndex are equivalent to the similar Index with int64 dtype
|
tm.assert_index_equal(result, idx, exact="equiv")
|
|
@pytest.mark.parametrize(
|
"mapper",
|
[
|
lambda values, index: {i: e for e, i in zip(values, index)},
|
lambda values, index: Series(values, index),
|
],
|
)
|
def test_map_dictlike(self, mapper, simple_index):
|
idx = simple_index
|
if isinstance(idx, CategoricalIndex):
|
# FIXME: this fails with CategoricalIndex bc it goes through
|
# Categorical.map which ends up calling get_indexer with
|
# non-unique values, which raises. This _should_ work fine for
|
# CategoricalIndex.
|
pytest.skip(f"skipping tests for {type(idx)}")
|
|
identity = mapper(idx.values, idx)
|
|
result = idx.map(identity)
|
# RangeIndex are equivalent to the similar Index with int64 dtype
|
tm.assert_index_equal(result, idx, exact="equiv")
|
|
# empty mappable
|
dtype = None
|
if idx.dtype.kind == "f":
|
dtype = idx.dtype
|
|
expected = Index([np.nan] * len(idx), dtype=dtype)
|
result = idx.map(mapper(expected, idx))
|
tm.assert_index_equal(result, expected)
|
|
def test_map_str(self, simple_index):
|
# GH 31202
|
idx = simple_index
|
result = idx.map(str)
|
expected = Index([str(x) for x in idx], dtype=object)
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize("copy", [True, False])
|
@pytest.mark.parametrize("name", [None, "foo"])
|
@pytest.mark.parametrize("ordered", [True, False])
|
def test_astype_category(self, copy, name, ordered, simple_index):
|
# GH 18630
|
idx = simple_index
|
if name:
|
idx = idx.rename(name)
|
|
# standard categories
|
dtype = CategoricalDtype(ordered=ordered)
|
result = idx.astype(dtype, copy=copy)
|
expected = CategoricalIndex(idx, name=name, ordered=ordered)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
# non-standard categories
|
dtype = CategoricalDtype(idx.unique().tolist()[:-1], ordered)
|
result = idx.astype(dtype, copy=copy)
|
expected = CategoricalIndex(idx, name=name, dtype=dtype)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
if ordered is False:
|
# dtype='category' defaults to ordered=False, so only test once
|
result = idx.astype("category", copy=copy)
|
expected = CategoricalIndex(idx, name=name)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
def test_is_unique(self, simple_index):
|
# initialize a unique index
|
index = simple_index.drop_duplicates()
|
assert index.is_unique is True
|
|
# empty index should be unique
|
index_empty = index[:0]
|
assert index_empty.is_unique is True
|
|
# test basic dupes
|
index_dup = index.insert(0, index[0])
|
assert index_dup.is_unique is False
|
|
# single NA should be unique
|
index_na = index.insert(0, np.nan)
|
assert index_na.is_unique is True
|
|
# multiple NA should not be unique
|
index_na_dup = index_na.insert(0, np.nan)
|
assert index_na_dup.is_unique is False
|
|
@pytest.mark.arm_slow
|
def test_engine_reference_cycle(self, simple_index):
|
# GH27585
|
index = simple_index
|
nrefs_pre = len(gc.get_referrers(index))
|
index._engine
|
assert len(gc.get_referrers(index)) == nrefs_pre
|
|
def test_getitem_2d_deprecated(self, simple_index):
|
# GH#30588, GH#31479
|
idx = simple_index
|
msg = "Multi-dimensional indexing"
|
with pytest.raises(ValueError, match=msg):
|
idx[:, None]
|
|
if not isinstance(idx, RangeIndex):
|
# GH#44051 RangeIndex already raised pre-2.0 with a different message
|
with pytest.raises(ValueError, match=msg):
|
idx[True]
|
with pytest.raises(ValueError, match=msg):
|
idx[False]
|
else:
|
msg = "only integers, slices"
|
with pytest.raises(IndexError, match=msg):
|
idx[True]
|
with pytest.raises(IndexError, match=msg):
|
idx[False]
|
|
def test_copy_shares_cache(self, simple_index):
|
# GH32898, GH36840
|
idx = simple_index
|
idx.get_loc(idx[0]) # populates the _cache.
|
copy = idx.copy()
|
|
assert copy._cache is idx._cache
|
|
def test_shallow_copy_shares_cache(self, simple_index):
|
# GH32669, GH36840
|
idx = simple_index
|
idx.get_loc(idx[0]) # populates the _cache.
|
shallow_copy = idx._view()
|
|
assert shallow_copy._cache is idx._cache
|
|
shallow_copy = idx._shallow_copy(idx._data)
|
assert shallow_copy._cache is not idx._cache
|
assert shallow_copy._cache == {}
|
|
def test_index_groupby(self, simple_index):
|
idx = simple_index[:5]
|
to_groupby = np.array([1, 2, np.nan, 2, 1])
|
tm.assert_dict_equal(
|
idx.groupby(to_groupby), {1.0: idx[[0, 4]], 2.0: idx[[1, 3]]}
|
)
|
|
to_groupby = DatetimeIndex(
|
[
|
datetime(2011, 11, 1),
|
datetime(2011, 12, 1),
|
pd.NaT,
|
datetime(2011, 12, 1),
|
datetime(2011, 11, 1),
|
],
|
tz="UTC",
|
).values
|
|
ex_keys = [Timestamp("2011-11-01"), Timestamp("2011-12-01")]
|
expected = {ex_keys[0]: idx[[0, 4]], ex_keys[1]: idx[[1, 3]]}
|
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
|
|
def test_append_preserves_dtype(self, simple_index):
|
# In particular Index with dtype float32
|
index = simple_index
|
N = len(index)
|
|
result = index.append(index)
|
assert result.dtype == index.dtype
|
tm.assert_index_equal(result[:N], index, check_exact=True)
|
tm.assert_index_equal(result[N:], index, check_exact=True)
|
|
alt = index.take(list(range(N)) * 2)
|
tm.assert_index_equal(result, alt, check_exact=True)
|
|
def test_inv(self, simple_index):
|
idx = simple_index
|
|
if idx.dtype.kind in ["i", "u"]:
|
res = ~idx
|
expected = Index(~idx.values, name=idx.name)
|
tm.assert_index_equal(res, expected)
|
|
# check that we are matching Series behavior
|
res2 = ~Series(idx)
|
tm.assert_series_equal(res2, Series(expected))
|
else:
|
if idx.dtype.kind == "f":
|
msg = "ufunc 'invert' not supported for the input types"
|
else:
|
msg = "bad operand"
|
with pytest.raises(TypeError, match=msg):
|
~idx
|
|
# check that we get the same behavior with Series
|
with pytest.raises(TypeError, match=msg):
|
~Series(idx)
|
|
def test_is_boolean_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(FutureWarning):
|
idx.is_boolean()
|
|
def test_is_floating_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(FutureWarning):
|
idx.is_floating()
|
|
def test_is_integer_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(FutureWarning):
|
idx.is_integer()
|
|
def test_holds_integer_deprecated(self, simple_index):
|
# GH50243
|
idx = simple_index
|
msg = f"{type(idx).__name__}.holds_integer is deprecated. "
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
idx.holds_integer()
|
|
def test_is_numeric_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(
|
FutureWarning,
|
match=f"{type(idx).__name__}.is_numeric is deprecated. ",
|
):
|
idx.is_numeric()
|
|
def test_is_categorical_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(
|
FutureWarning,
|
match=r"Use pandas\.api\.types\.is_categorical_dtype instead",
|
):
|
idx.is_categorical()
|
|
def test_is_interval_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(FutureWarning):
|
idx.is_interval()
|
|
def test_is_object_is_deprecated(self, simple_index):
|
# GH50042
|
idx = simple_index
|
with tm.assert_produces_warning(FutureWarning):
|
idx.is_object()
|
|
|
class NumericBase(Base):
|
"""
|
Base class for numeric index (incl. RangeIndex) sub-class tests.
|
"""
|
|
def test_constructor_unwraps_index(self, dtype):
|
index_cls = self._index_cls
|
|
idx = Index([1, 2], dtype=dtype)
|
result = index_cls(idx)
|
expected = np.array([1, 2], dtype=idx.dtype)
|
tm.assert_numpy_array_equal(result._data, expected)
|
|
def test_where(self):
|
# Tested in numeric.test_indexing
|
pass
|
|
def test_can_hold_identifiers(self, simple_index):
|
idx = simple_index
|
key = idx[0]
|
assert idx._can_hold_identifiers_and_holds_name(key) is False
|
|
def test_view(self, dtype):
|
index_cls = self._index_cls
|
|
idx = index_cls([], dtype=dtype, name="Foo")
|
idx_view = idx.view()
|
assert idx_view.name == "Foo"
|
|
idx_view = idx.view(dtype)
|
tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
|
|
idx_view = idx.view(index_cls)
|
tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
|
|
def test_format(self, simple_index):
|
# GH35439
|
idx = simple_index
|
max_width = max(len(str(x)) for x in idx)
|
expected = [str(x).ljust(max_width) for x in idx]
|
assert idx.format() == expected
|
|
def test_numeric_compat(self):
|
pass # override Base method
|
|
def test_insert_non_na(self, simple_index):
|
# GH#43921 inserting an element that we know we can hold should
|
# not change dtype or type (except for RangeIndex)
|
index = simple_index
|
|
result = index.insert(0, index[0])
|
|
expected = Index([index[0]] + list(index), dtype=index.dtype)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
def test_insert_na(self, nulls_fixture, simple_index):
|
# GH 18295 (test missing)
|
index = simple_index
|
na_val = nulls_fixture
|
|
if na_val is pd.NaT:
|
expected = Index([index[0], pd.NaT] + list(index[1:]), dtype=object)
|
else:
|
expected = Index([index[0], np.nan] + list(index[1:]))
|
# GH#43921 we preserve float dtype
|
if index.dtype.kind == "f":
|
expected = Index(expected, dtype=index.dtype)
|
|
result = index.insert(1, na_val)
|
tm.assert_index_equal(result, expected, exact=True)
|
|
def test_arithmetic_explicit_conversions(self):
|
# GH 8608
|
# add/sub are overridden explicitly for Float/Int Index
|
index_cls = self._index_cls
|
if index_cls is RangeIndex:
|
idx = RangeIndex(5)
|
else:
|
idx = index_cls(np.arange(5, dtype="int64"))
|
|
# float conversions
|
arr = np.arange(5, dtype="int64") * 3.2
|
expected = Index(arr, dtype=np.float64)
|
fidx = idx * 3.2
|
tm.assert_index_equal(fidx, expected)
|
fidx = 3.2 * idx
|
tm.assert_index_equal(fidx, expected)
|
|
# interops with numpy arrays
|
expected = Index(arr, dtype=np.float64)
|
a = np.zeros(5, dtype="float64")
|
result = fidx - a
|
tm.assert_index_equal(result, expected)
|
|
expected = Index(-arr, dtype=np.float64)
|
a = np.zeros(5, dtype="float64")
|
result = a - fidx
|
tm.assert_index_equal(result, expected)
|
|
@pytest.mark.parametrize("complex_dtype", [np.complex64, np.complex128])
|
def test_astype_to_complex(self, complex_dtype, simple_index):
|
result = simple_index.astype(complex_dtype)
|
|
assert type(result) is Index and result.dtype == complex_dtype
|
|
def test_cast_string(self, dtype):
|
result = self._index_cls(["0", "1", "2"], dtype=dtype)
|
expected = self._index_cls([0, 1, 2], dtype=dtype)
|
tm.assert_index_equal(result, expected)
|
