Home-IoT/craftbeerpi4-pione
1
0
Fork 0
mirror of https://github.com/PiBrewing/craftbeerpi4.git synced 2024-11-23 15:38:14 +01:00
Code Issues Projects Releases Packages Wiki Activity
craftbeerpi4-pione/venv/lib/python3.8/site-packages/pandas/tests/indexes/test_base.py
Manuel Fritsch aeabde8c4a plugin controller change
2021-01-30 22:29:33 +01:00

2619 lines
91 KiB
Python
Raw Blame History

from collections import defaultdict
from datetime import datetime, timedelta
from io import StringIO
import math
import operator
import re
import numpy as np
import pytest
import pandas._config.config as cf
from pandas._libs.tslib import Timestamp
from pandas.compat.numpy import np_datetime64_compat
from pandas.util._test_decorators import async_mark
from pandas.core.dtypes.generic import ABCIndex
import pandas as pd
from pandas import (
CategoricalIndex,
DataFrame,
DatetimeIndex,
Float64Index,
Int64Index,
PeriodIndex,
RangeIndex,
Series,
TimedeltaIndex,
UInt64Index,
date_range,
isna,
period_range,
)
import pandas._testing as tm
from pandas.core.indexes.api import (
Index,
MultiIndex,
_get_combined_index,
ensure_index,
ensure_index_from_sequences,
)
from pandas.tests.indexes.common import Base
class TestIndex(Base):
_holder = Index
def create_index(self) -> Index:
return Index(list("abcde"))
def test_can_hold_identifiers(self):
index = self.create_index()
key = index[0]
assert index._can_hold_identifiers_and_holds_name(key) is True
@pytest.mark.parametrize("index", ["datetime"], indirect=True)
def test_new_axis(self, index):
with tm.assert_produces_warning(FutureWarning):
# GH#30588 multi-dimensional indexing deprecated
new_index = index[None, :]
assert new_index.ndim == 2
assert isinstance(new_index, np.ndarray)
@pytest.mark.parametrize("index", ["int", "uint", "float"], indirect=True)
def test_copy_and_deepcopy(self, index):
new_copy2 = index.copy(dtype=int)
assert new_copy2.dtype.kind == "i"
def test_constructor_regular(self, index):
tm.assert_contains_all(index, index)
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_constructor_casting(self, index):
# casting
arr = np.array(index)
new_index = Index(arr)
tm.assert_contains_all(arr, new_index)
tm.assert_index_equal(index, new_index)
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_constructor_copy(self, index):
# copy
# index = self.create_index()
arr = np.array(index)
new_index = Index(arr, copy=True, name="name")
assert isinstance(new_index, Index)
assert new_index.name == "name"
tm.assert_numpy_array_equal(arr, new_index.values)
arr[0] = "SOMEBIGLONGSTRING"
assert new_index[0] != "SOMEBIGLONGSTRING"
# FIXME: dont leave commented-out
# what to do here?
# arr = np.array(5.)
# pytest.raises(Exception, arr.view, Index)
@pytest.mark.parametrize("cast_as_obj", [True, False])
@pytest.mark.parametrize(
"index",
[
pd.date_range(
"2015-01-01 10:00",
freq="D",
periods=3,
tz="US/Eastern",
name="Green Eggs & Ham",
), # DTI with tz
pd.date_range("2015-01-01 10:00", freq="D", periods=3), # DTI no tz
pd.timedelta_range("1 days", freq="D", periods=3), # td
pd.period_range("2015-01-01", freq="D", periods=3), # period
],
)
def test_constructor_from_index_dtlike(self, cast_as_obj, index):
if cast_as_obj:
result = pd.Index(index.astype(object))
else:
result = pd.Index(index)
tm.assert_index_equal(result, index)
if isinstance(index, pd.DatetimeIndex):
assert result.tz == index.tz
if cast_as_obj:
# GH#23524 check that Index(dti, dtype=object) does not
# incorrectly raise ValueError, and that nanoseconds are not
# dropped
index += pd.Timedelta(nanoseconds=50)
result = pd.Index(index, dtype=object)
assert result.dtype == np.object_
assert list(result) == list(index)
@pytest.mark.parametrize(
"index,has_tz",
[
(
pd.date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
True,
), # datetimetz
(pd.timedelta_range("1 days", freq="D", periods=3), False), # td
(pd.period_range("2015-01-01", freq="D", periods=3), False), # period
],
)
def test_constructor_from_series_dtlike(self, index, has_tz):
result = pd.Index(pd.Series(index))
tm.assert_index_equal(result, index)
if has_tz:
assert result.tz == index.tz
def test_constructor_from_series_freq(self):
# GH 6273
# create from a series, passing a freq
dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
expected = DatetimeIndex(dts, freq="MS")
s = Series(pd.to_datetime(dts))
result = DatetimeIndex(s, freq="MS")
tm.assert_index_equal(result, expected)
def test_constructor_from_frame_series_freq(self):
# GH 6273
# create from a series, passing a freq
dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
expected = DatetimeIndex(dts, freq="MS")
df = pd.DataFrame(np.random.rand(5, 3))
df["date"] = dts
result = DatetimeIndex(df["date"], freq="MS")
assert df["date"].dtype == object
expected.name = "date"
tm.assert_index_equal(result, expected)
expected = pd.Series(dts, name="date")
tm.assert_series_equal(df["date"], expected)
# GH 6274
# infer freq of same
freq = pd.infer_freq(df["date"])
assert freq == "MS"
@pytest.mark.parametrize(
"array",
[
np.arange(5),
np.array(["a", "b", "c"]),
date_range("2000-01-01", periods=3).values,
],
)
def test_constructor_ndarray_like(self, array):
# GH 5460#issuecomment-44474502
# it should be possible to convert any object that satisfies the numpy
# ndarray interface directly into an Index
class ArrayLike:
def __init__(self, array):
self.array = array
def __array__(self, dtype=None) -> np.ndarray:
return self.array
expected = pd.Index(array)
result = pd.Index(ArrayLike(array))
tm.assert_index_equal(result, expected)
def test_constructor_int_dtype_nan(self):
# see gh-15187
data = [np.nan]
expected = Float64Index(data)
result = Index(data, dtype="float")
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("dtype", ["int64", "uint64"])
def test_constructor_int_dtype_nan_raises(self, dtype):
# see gh-15187
data = [np.nan]
msg = "cannot convert"
with pytest.raises(ValueError, match=msg):
Index(data, dtype=dtype)
def test_constructor_no_pandas_array(self):
ser = pd.Series([1, 2, 3])
result = pd.Index(ser.array)
expected = pd.Index([1, 2, 3])
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"klass,dtype,na_val",
[
(pd.Float64Index, np.float64, np.nan),
(pd.DatetimeIndex, "datetime64[ns]", pd.NaT),
],
)
def test_index_ctor_infer_nan_nat(self, klass, dtype, na_val):
# GH 13467
na_list = [na_val, na_val]
expected = klass(na_list)
assert expected.dtype == dtype
result = Index(na_list)
tm.assert_index_equal(result, expected)
result = Index(np.array(na_list))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"vals,dtype",
[
([1, 2, 3, 4, 5], "int"),
([1.1, np.nan, 2.2, 3.0], "float"),
(["A", "B", "C", np.nan], "obj"),
],
)
def test_constructor_simple_new(self, vals, dtype):
index = Index(vals, name=dtype)
result = index._simple_new(index.values, dtype)
tm.assert_index_equal(result, index)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
np.array([1, 2, 3]),
np.array([1, 2, 3], dtype=int),
# below should coerce
[1.0, 2.0, 3.0],
np.array([1.0, 2.0, 3.0], dtype=float),
],
)
def test_constructor_dtypes_to_int64(self, vals):
index = Index(vals, dtype=int)
assert isinstance(index, Int64Index)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
[1.0, 2.0, 3.0],
np.array([1.0, 2.0, 3.0]),
np.array([1, 2, 3], dtype=int),
np.array([1.0, 2.0, 3.0], dtype=float),
],
)
def test_constructor_dtypes_to_float64(self, vals):
index = Index(vals, dtype=float)
assert isinstance(index, Float64Index)
@pytest.mark.parametrize(
"vals",
[
[1, 2, 3],
np.array([1, 2, 3], dtype=int),
np.array(
[np_datetime64_compat("2011-01-01"), np_datetime64_compat("2011-01-02")]
),
[datetime(2011, 1, 1), datetime(2011, 1, 2)],
],
)
def test_constructor_dtypes_to_categorical(self, vals):
index = Index(vals, dtype="category")
assert isinstance(index, CategoricalIndex)
@pytest.mark.parametrize("cast_index", [True, False])
@pytest.mark.parametrize(
"vals",
[
Index(
np.array(
[
np_datetime64_compat("2011-01-01"),
np_datetime64_compat("2011-01-02"),
]
)
),
Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),
],
)
def test_constructor_dtypes_to_datetime(self, cast_index, vals):
if cast_index:
index = Index(vals, dtype=object)
assert isinstance(index, Index)
assert index.dtype == object
else:
index = Index(vals)
assert isinstance(index, DatetimeIndex)
@pytest.mark.parametrize("cast_index", [True, False])
@pytest.mark.parametrize(
"vals",
[
np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),
[timedelta(1), timedelta(1)],
],
)
def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
if cast_index:
index = Index(vals, dtype=object)
assert isinstance(index, Index)
assert index.dtype == object
else:
index = Index(vals)
assert isinstance(index, TimedeltaIndex)
@pytest.mark.parametrize("attr", ["values", "asi8"])
@pytest.mark.parametrize("klass", [pd.Index, pd.DatetimeIndex])
def test_constructor_dtypes_datetime(self, tz_naive_fixture, attr, klass):
# Test constructing with a datetimetz dtype
# .values produces numpy datetimes, so these are considered naive
# .asi8 produces integers, so these are considered epoch timestamps
# ^the above will be true in a later version. Right now we `.view`
# the i8 values as NS_DTYPE, effectively treating them as wall times.
index = pd.date_range("2011-01-01", periods=5)
arg = getattr(index, attr)
index = index.tz_localize(tz_naive_fixture)
dtype = index.dtype
if attr == "asi8":
result = pd.DatetimeIndex(arg).tz_localize(tz_naive_fixture)
else:
result = klass(arg, tz=tz_naive_fixture)
tm.assert_index_equal(result, index)
if attr == "asi8":
result = pd.DatetimeIndex(arg).astype(dtype)
else:
result = klass(arg, dtype=dtype)
tm.assert_index_equal(result, index)
if attr == "asi8":
result = pd.DatetimeIndex(list(arg)).tz_localize(tz_naive_fixture)
else:
result = klass(list(arg), tz=tz_naive_fixture)
tm.assert_index_equal(result, index)
if attr == "asi8":
result = pd.DatetimeIndex(list(arg)).astype(dtype)
else:
result = klass(list(arg), dtype=dtype)
tm.assert_index_equal(result, index)
@pytest.mark.parametrize("attr", ["values", "asi8"])
@pytest.mark.parametrize("klass", [pd.Index, pd.TimedeltaIndex])
def test_constructor_dtypes_timedelta(self, attr, klass):
index = pd.timedelta_range("1 days", periods=5)
index = index._with_freq(None) # wont be preserved by constructors
dtype = index.dtype
values = getattr(index, attr)
result = klass(values, dtype=dtype)
tm.assert_index_equal(result, index)
result = klass(list(values), dtype=dtype)
tm.assert_index_equal(result, index)
@pytest.mark.parametrize("value", [[], iter([]), (_ for _ in [])])
@pytest.mark.parametrize(
"klass",
[
Index,
Float64Index,
Int64Index,
UInt64Index,
CategoricalIndex,
DatetimeIndex,
TimedeltaIndex,
],
)
def test_constructor_empty(self, value, klass):
empty = klass(value)
assert isinstance(empty, klass)
assert not len(empty)
@pytest.mark.parametrize(
"empty,klass",
[
(PeriodIndex([], freq="B"), PeriodIndex),
(PeriodIndex(iter([]), freq="B"), PeriodIndex),
(PeriodIndex((_ for _ in []), freq="B"), PeriodIndex),
(RangeIndex(step=1), pd.RangeIndex),
(MultiIndex(levels=[[1, 2], ["blue", "red"]], codes=[[], []]), MultiIndex),
],
)
def test_constructor_empty_special(self, empty, klass):
assert isinstance(empty, klass)
assert not len(empty)
def test_constructor_overflow_int64(self):
# see gh-15832
msg = (
"The elements provided in the data cannot "
"all be casted to the dtype int64"
)
with pytest.raises(OverflowError, match=msg):
Index([np.iinfo(np.uint64).max - 1], dtype="int64")
@pytest.mark.parametrize(
"index",
[
"datetime",
"float",
"int",
"period",
"range",
"repeats",
"timedelta",
"tuples",
"uint",
],
indirect=True,
)
def test_view_with_args(self, index):
index.view("i8")
@pytest.mark.parametrize(
"index",
[
"unicode",
"string",
pytest.param("categorical", marks=pytest.mark.xfail(reason="gh-25464")),
"bool",
"empty",
],
indirect=True,
)
def test_view_with_args_object_array_raises(self, index):
msg = "Cannot change data-type for object array"
with pytest.raises(TypeError, match=msg):
index.view("i8")
@pytest.mark.parametrize("index", ["int", "range"], indirect=True)
def test_astype(self, index):
casted = index.astype("i8")
# it works!
casted.get_loc(5)
# pass on name
index.name = "foobar"
casted = index.astype("i8")
assert casted.name == "foobar"
def test_equals_object(self):
# same
assert Index(["a", "b", "c"]).equals(Index(["a", "b", "c"]))
@pytest.mark.parametrize(
"comp", [Index(["a", "b"]), Index(["a", "b", "d"]), ["a", "b", "c"]]
)
def test_not_equals_object(self, comp):
assert not Index(["a", "b", "c"]).equals(comp)
def test_insert_missing(self, nulls_fixture):
# GH 22295
# test there is no mangling of NA values
expected = Index(["a", nulls_fixture, "b", "c"])
result = Index(list("abc")).insert(1, nulls_fixture)
tm.assert_index_equal(result, expected)
def test_delete_raises(self):
index = Index(["a", "b", "c", "d"], name="index")
msg = "index 5 is out of bounds for axis 0 with size 4"
with pytest.raises(IndexError, match=msg):
index.delete(5)
def test_identical(self):
# index
i1 = Index(["a", "b", "c"])
i2 = Index(["a", "b", "c"])
assert i1.identical(i2)
i1 = i1.rename("foo")
assert i1.equals(i2)
assert not i1.identical(i2)
i2 = i2.rename("foo")
assert i1.identical(i2)
i3 = Index([("a", "a"), ("a", "b"), ("b", "a")])
i4 = Index([("a", "a"), ("a", "b"), ("b", "a")], tupleize_cols=False)
assert not i3.identical(i4)
def test_is_(self):
ind = Index(range(10))
assert ind.is_(ind)
assert ind.is_(ind.view().view().view().view())
assert not ind.is_(Index(range(10)))
assert not ind.is_(ind.copy())
assert not ind.is_(ind.copy(deep=False))
assert not ind.is_(ind[:])
assert not ind.is_(np.array(range(10)))
# quasi-implementation dependent
assert ind.is_(ind.view())
ind2 = ind.view()
ind2.name = "bob"
assert ind.is_(ind2)
assert ind2.is_(ind)
# doesn't matter if Indices are *actually* views of underlying data,
assert not ind.is_(Index(ind.values))
arr = np.array(range(1, 11))
ind1 = Index(arr, copy=False)
ind2 = Index(arr, copy=False)
assert not ind1.is_(ind2)
@pytest.mark.parametrize("index", ["datetime"], indirect=True)
def test_asof(self, index):
d = index[0]
assert index.asof(d) == d
assert isna(index.asof(d - timedelta(1)))
d = index[-1]
assert index.asof(d + timedelta(1)) == d
d = index[0].to_pydatetime()
assert isinstance(index.asof(d), Timestamp)
def test_asof_datetime_partial(self):
index = pd.date_range("2010-01-01", periods=2, freq="m")
expected = Timestamp("2010-02-28")
result = index.asof("2010-02")
assert result == expected
assert not isinstance(result, Index)
def test_nanosecond_index_access(self):
s = Series([Timestamp("20130101")]).values.view("i8")[0]
r = DatetimeIndex([s + 50 + i for i in range(100)])
x = Series(np.random.randn(100), index=r)
first_value = x.asof(x.index[0])
# this does not yet work, as parsing strings is done via dateutil
# assert first_value == x['2013-01-01 00:00:00.000000050+0000']
expected_ts = np_datetime64_compat("2013-01-01 00:00:00.000000050+0000", "ns")
assert first_value == x[Timestamp(expected_ts)]
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_booleanindex(self, index):
bool_index = np.ones(len(index), dtype=bool)
bool_index[5:30:2] = False
sub_index = index[bool_index]
for i, val in enumerate(sub_index):
assert sub_index.get_loc(val) == i
sub_index = index[list(bool_index)]
for i, val in enumerate(sub_index):
assert sub_index.get_loc(val) == i
def test_fancy(self):
index = self.create_index()
sl = index[[1, 2, 3]]
for i in sl:
assert i == sl[sl.get_loc(i)]
@pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
@pytest.mark.parametrize("dtype", [np.int_, np.bool_])
def test_empty_fancy(self, index, dtype):
empty_arr = np.array([], dtype=dtype)
empty_index = type(index)([])
assert index[[]].identical(empty_index)
assert index[empty_arr].identical(empty_index)
@pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
def test_empty_fancy_raises(self, index):
# pd.DatetimeIndex is excluded, because it overrides getitem and should
# be tested separately.
empty_farr = np.array([], dtype=np.float_)
empty_index = type(index)([])
assert index[[]].identical(empty_index)
# np.ndarray only accepts ndarray of int & bool dtypes, so should Index
msg = r"arrays used as indices must be of integer \(or boolean\) type"
with pytest.raises(IndexError, match=msg):
index[empty_farr]
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_intersection(self, index, sort):
first = index[:20]
second = index[:10]
intersect = first.intersection(second, sort=sort)
if sort is None:
tm.assert_index_equal(intersect, second.sort_values())
assert tm.equalContents(intersect, second)
# Corner cases
inter = first.intersection(first, sort=sort)
assert inter is first
@pytest.mark.parametrize(
"index2,keeps_name",
[
(Index([3, 4, 5, 6, 7], name="index"), True), # preserve same name
(Index([3, 4, 5, 6, 7], name="other"), False), # drop diff names
(Index([3, 4, 5, 6, 7]), False),
],
)
def test_intersection_name_preservation(self, index2, keeps_name, sort):
index1 = Index([1, 2, 3, 4, 5], name="index")
expected = Index([3, 4, 5])
result = index1.intersection(index2, sort)
if keeps_name:
expected.name = "index"
assert result.name == expected.name
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("index", ["string"], indirect=True)
@pytest.mark.parametrize(
"first_name,second_name,expected_name",
[("A", "A", "A"), ("A", "B", None), (None, "B", None)],
)
def test_intersection_name_preservation2(
self, index, first_name, second_name, expected_name, sort
):
first = index[5:20]
second = index[:10]
first.name = first_name
second.name = second_name
intersect = first.intersection(second, sort=sort)
assert intersect.name == expected_name
@pytest.mark.parametrize(
"index2,keeps_name",
[
(Index([4, 7, 6, 5, 3], name="index"), True),
(Index([4, 7, 6, 5, 3], name="other"), False),
],
)
def test_intersection_monotonic(self, index2, keeps_name, sort):
index1 = Index([5, 3, 2, 4, 1], name="index")
expected = Index([5, 3, 4])
if keeps_name:
expected.name = "index"
result = index1.intersection(index2, sort=sort)
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"index2,expected_arr",
[(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B"])],
)
def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):
# non-monotonic non-unique
index1 = Index(["A", "B", "A", "C"])
expected = Index(expected_arr, dtype="object")
result = index1.intersection(index2, sort=sort)
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
def test_intersect_str_dates(self, sort):
dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
i1 = Index(dt_dates, dtype=object)
i2 = Index(["aa"], dtype=object)
result = i2.intersection(i1, sort=sort)
assert len(result) == 0
@pytest.mark.xfail(reason="Not implemented")
def test_intersection_equal_sort_true(self):
# TODO decide on True behaviour
idx = pd.Index(["c", "a", "b"])
sorted_ = pd.Index(["a", "b", "c"])
tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)
def test_chained_union(self, sort):
# Chained unions handles names correctly
i1 = Index([1, 2], name="i1")
i2 = Index([5, 6], name="i2")
i3 = Index([3, 4], name="i3")
union = i1.union(i2.union(i3, sort=sort), sort=sort)
expected = i1.union(i2, sort=sort).union(i3, sort=sort)
tm.assert_index_equal(union, expected)
j1 = Index([1, 2], name="j1")
j2 = Index([], name="j2")
j3 = Index([], name="j3")
union = j1.union(j2.union(j3, sort=sort), sort=sort)
expected = j1.union(j2, sort=sort).union(j3, sort=sort)
tm.assert_index_equal(union, expected)
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_union(self, index, sort):
first = index[5:20]
second = index[:10]
everything = index[:20]
union = first.union(second, sort=sort)
if sort is None:
tm.assert_index_equal(union, everything.sort_values())
assert tm.equalContents(union, everything)
@pytest.mark.parametrize("slice_", [slice(None), slice(0)])
def test_union_sort_other_special(self, slice_):
# https://github.com/pandas-dev/pandas/issues/24959
idx = pd.Index([1, 0, 2])
# default, sort=None
other = idx[slice_]
tm.assert_index_equal(idx.union(other), idx)
tm.assert_index_equal(other.union(idx), idx)
# sort=False
tm.assert_index_equal(idx.union(other, sort=False), idx)
@pytest.mark.xfail(reason="Not implemented")
@pytest.mark.parametrize("slice_", [slice(None), slice(0)])
def test_union_sort_special_true(self, slice_):
# TODO decide on True behaviour
# sort=True
idx = pd.Index([1, 0, 2])
# default, sort=None
other = idx[slice_]
result = idx.union(other, sort=True)
expected = pd.Index([0, 1, 2])
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("klass", [np.array, Series, list])
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_union_from_iterables(self, index, klass, sort):
# GH 10149
first = index[5:20]
second = index[:10]
everything = index[:20]
case = klass(second.values)
result = first.union(case, sort=sort)
if sort is None:
tm.assert_index_equal(result, everything.sort_values())
assert tm.equalContents(result, everything)
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_union_identity(self, index, sort):
first = index[5:20]
union = first.union(first, sort=sort)
# i.e. identity is not preserved when sort is True
assert (union is first) is (not sort)
# This should no longer be the same object, since [] is not consistent,
# both objects will be recast to dtype('O')
union = first.union([], sort=sort)
assert (union is first) is (not sort)
union = Index([]).union(first, sort=sort)
assert (union is first) is (not sort)
@pytest.mark.parametrize("first_list", [list("ba"), list()])
@pytest.mark.parametrize("second_list", [list("ab"), list()])
@pytest.mark.parametrize(
"first_name, second_name, expected_name",
[("A", "B", None), (None, "B", None), ("A", None, None)],
)
def test_union_name_preservation(
self, first_list, second_list, first_name, second_name, expected_name, sort
):
first = Index(first_list, name=first_name)
second = Index(second_list, name=second_name)
union = first.union(second, sort=sort)
vals = set(first_list).union(second_list)
if sort is None and len(first_list) > 0 and len(second_list) > 0:
expected = Index(sorted(vals), name=expected_name)
tm.assert_index_equal(union, expected)
else:
expected = Index(vals, name=expected_name)
assert tm.equalContents(union, expected)
def test_union_dt_as_obj(self, sort):
# TODO: Replace with fixturesult
index = self.create_index()
date_index = pd.date_range("2019-01-01", periods=10)
first_cat = index.union(date_index)
second_cat = index.union(index)
if date_index.dtype == np.object_:
appended = np.append(index, date_index)
else:
appended = np.append(index, date_index.astype("O"))
assert tm.equalContents(first_cat, appended)
assert tm.equalContents(second_cat, index)
tm.assert_contains_all(index, first_cat)
tm.assert_contains_all(index, second_cat)
tm.assert_contains_all(date_index, first_cat)
def test_map_identity_mapping(self, index):
# GH 12766
tm.assert_index_equal(index, index.map(lambda x: x))
def test_map_with_tuples(self):
# GH 12766
# Test that returning a single tuple from an Index
# returns an Index.
index = tm.makeIntIndex(3)
result = tm.makeIntIndex(3).map(lambda x: (x,))
expected = Index([(i,) for i in index])
tm.assert_index_equal(result, expected)
# Test that returning a tuple from a map of a single index
# returns a MultiIndex object.
result = index.map(lambda x: (x, x == 1))
expected = MultiIndex.from_tuples([(i, i == 1) for i in index])
tm.assert_index_equal(result, expected)
def test_map_with_tuples_mi(self):
# Test that returning a single object from a MultiIndex
# returns an Index.
first_level = ["foo", "bar", "baz"]
multi_index = MultiIndex.from_tuples(zip(first_level, [1, 2, 3]))
reduced_index = multi_index.map(lambda x: x[0])
tm.assert_index_equal(reduced_index, Index(first_level))
@pytest.mark.parametrize(
"attr", ["makeDateIndex", "makePeriodIndex", "makeTimedeltaIndex"]
)
def test_map_tseries_indices_return_index(self, attr):
index = getattr(tm, attr)(10)
expected = Index([1] * 10)
result = index.map(lambda x: 1)
tm.assert_index_equal(expected, result)
def test_map_tseries_indices_accsr_return_index(self):
date_index = tm.makeDateIndex(24, freq="h", name="hourly")
expected = Index(range(24), name="hourly")
tm.assert_index_equal(expected, date_index.map(lambda x: x.hour))
@pytest.mark.parametrize(
"mapper",
[
lambda values, index: {i: e for e, i in zip(values, index)},
lambda values, index: pd.Series(values, index),
],
)
def test_map_dictlike_simple(self, mapper):
# GH 12756
expected = Index(["foo", "bar", "baz"])
index = tm.makeIntIndex(3)
result = index.map(mapper(expected.values, index))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"mapper",
[
lambda values, index: {i: e for e, i in zip(values, index)},
lambda values, index: pd.Series(values, index),
],
)
def test_map_dictlike(self, index, mapper):
# GH 12756
if isinstance(index, CategoricalIndex):
# Tested in test_categorical
return
elif not index.is_unique:
# Cannot map duplicated index
return
if index.empty:
# to match proper result coercion for uints
expected = Index([])
else:
expected = Index(np.arange(len(index), 0, -1))
result = index.map(mapper(expected, index))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"mapper",
[Series(["foo", 2.0, "baz"], index=[0, 2, -1]), {0: "foo", 2: 2.0, -1: "baz"}],
)
def test_map_with_non_function_missing_values(self, mapper):
# GH 12756
expected = Index([2.0, np.nan, "foo"])
result = Index([2, 1, 0]).map(mapper)
tm.assert_index_equal(expected, result)
def test_map_na_exclusion(self):
index = Index([1.5, np.nan, 3, np.nan, 5])
result = index.map(lambda x: x * 2, na_action="ignore")
expected = index * 2
tm.assert_index_equal(result, expected)
def test_map_defaultdict(self):
index = Index([1, 2, 3])
default_dict = defaultdict(lambda: "blank")
default_dict[1] = "stuff"
result = index.map(default_dict)
expected = Index(["stuff", "blank", "blank"])
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("name,expected", [("foo", "foo"), ("bar", None)])
def test_append_empty_preserve_name(self, name, expected):
left = Index([], name="foo")
right = Index([1, 2, 3], name=name)
result = left.append(right)
assert result.name == expected
@pytest.mark.parametrize("index", ["string"], indirect=True)
@pytest.mark.parametrize("second_name,expected", [(None, None), ("name", "name")])
def test_difference_name_preservation(self, index, second_name, expected, sort):
first = index[5:20]
second = index[:10]
answer = index[10:20]
first.name = "name"
second.name = second_name
result = first.difference(second, sort=sort)
assert tm.equalContents(result, answer)
if expected is None:
assert result.name is None
else:
assert result.name == expected
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_difference_empty_arg(self, index, sort):
first = index[5:20]
first.name = "name"
result = first.difference([], sort)
assert tm.equalContents(result, first)
assert result.name == first.name
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_difference_identity(self, index, sort):
first = index[5:20]
first.name = "name"
result = first.difference(first, sort)
assert len(result) == 0
assert result.name == first.name
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_difference_sort(self, index, sort):
first = index[5:20]
second = index[:10]
result = first.difference(second, sort)
expected = index[10:20]
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
def test_symmetric_difference(self, sort):
# smoke
index1 = Index([5, 2, 3, 4], name="index1")
index2 = Index([2, 3, 4, 1])
result = index1.symmetric_difference(index2, sort=sort)
expected = Index([5, 1])
assert tm.equalContents(result, expected)
assert result.name is None
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
# __xor__ syntax
expected = index1 ^ index2
assert tm.equalContents(result, expected)
assert result.name is None
@pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
def test_difference_incomparable(self, opname):
a = pd.Index([3, pd.Timestamp("2000"), 1])
b = pd.Index([2, pd.Timestamp("1999"), 1])
op = operator.methodcaller(opname, b)
# sort=None, the default
result = op(a)
expected = pd.Index([3, pd.Timestamp("2000"), 2, pd.Timestamp("1999")])
if opname == "difference":
expected = expected[:2]
tm.assert_index_equal(result, expected)
# sort=False
op = operator.methodcaller(opname, b, sort=False)
result = op(a)
tm.assert_index_equal(result, expected)
@pytest.mark.xfail(reason="Not implemented")
@pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
def test_difference_incomparable_true(self, opname):
# TODO decide on True behaviour
# # sort=True, raises
a = pd.Index([3, pd.Timestamp("2000"), 1])
b = pd.Index([2, pd.Timestamp("1999"), 1])
op = operator.methodcaller(opname, b, sort=True)
with pytest.raises(TypeError, match="Cannot compare"):
op(a)
def test_symmetric_difference_mi(self, sort):
index1 = MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3]))
index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])
result = index1.symmetric_difference(index2, sort=sort)
expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
assert tm.equalContents(result, expected)
@pytest.mark.parametrize(
"index2,expected",
[
(Index([0, 1, np.nan]), Index([2.0, 3.0, 0.0])),
(Index([0, 1]), Index([np.nan, 2.0, 3.0, 0.0])),
],
)
def test_symmetric_difference_missing(self, index2, expected, sort):
# GH 13514 change: {nan} - {nan} == {}
# (GH 6444, sorting of nans, is no longer an issue)
index1 = Index([1, np.nan, 2, 3])
result = index1.symmetric_difference(index2, sort=sort)
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
def test_symmetric_difference_non_index(self, sort):
index1 = Index([1, 2, 3, 4], name="index1")
index2 = np.array([2, 3, 4, 5])
expected = Index([1, 5])
result = index1.symmetric_difference(index2, sort=sort)
assert tm.equalContents(result, expected)
assert result.name == "index1"
result = index1.symmetric_difference(index2, result_name="new_name", sort=sort)
assert tm.equalContents(result, expected)
assert result.name == "new_name"
def test_difference_type(self, index, sort):
# GH 20040
# If taking difference of a set and itself, it
# needs to preserve the type of the index
if not index.is_unique:
return
result = index.difference(index, sort=sort)
expected = index.drop(index)
tm.assert_index_equal(result, expected)
def test_intersection_difference(self, index, sort):
# GH 20040
# Test that the intersection of an index with an
# empty index produces the same index as the difference
# of an index with itself. Test for all types
if not index.is_unique:
return
inter = index.intersection(index.drop(index))
diff = index.difference(index, sort=sort)
tm.assert_index_equal(inter, diff)
def test_is_mixed_deprecated(self):
# GH#32922
index = self.create_index()
with tm.assert_produces_warning(FutureWarning):
index.is_mixed()
@pytest.mark.parametrize(
"index, expected",
[
("string", False),
("bool", False),
("categorical", False),
("int", True),
("datetime", False),
("float", True),
],
indirect=["index"],
)
def test_is_numeric(self, index, expected):
assert index.is_numeric() is expected
@pytest.mark.parametrize(
"index, expected",
[
("string", True),
("bool", True),
("categorical", False),
("int", False),
("datetime", False),
("float", False),
],
indirect=["index"],
)
def test_is_object(self, index, expected):
assert index.is_object() is expected
@pytest.mark.parametrize(
"index, expected",
[
("string", False),
("bool", False),
("categorical", False),
("int", False),
("datetime", True),
("float", False),
],
indirect=["index"],
)
def test_is_all_dates(self, index, expected):
assert index.is_all_dates is expected
def test_summary(self, index):
self._check_method_works(Index._summary, index)
def test_summary_bug(self):
# GH3869`
ind = Index(["{other}%s", "~:{range}:0"], name="A")
result = ind._summary()
# shouldn't be formatted accidentally.
assert "~:{range}:0" in result
assert "{other}%s" in result
def test_format_different_scalar_lengths(self):
# GH35439
idx = Index(["aaaaaaaaa", "b"])
expected = ["aaaaaaaaa", "b"]
assert idx.format() == expected
def test_format_bug(self):
# GH 14626
# windows has different precision on datetime.datetime.now (it doesn't
# include us since the default for Timestamp shows these but Index
# formatting does not we are skipping)
now = datetime.now()
if not str(now).endswith("000"):
index = Index([now])
formatted = index.format()
expected = [str(index[0])]
assert formatted == expected
Index([]).format()
@pytest.mark.parametrize("vals", [[1, 2.0 + 3.0j, 4.0], ["a", "b", "c"]])
def test_format_missing(self, vals, nulls_fixture):
# 2845
vals = list(vals) # Copy for each iteration
vals.append(nulls_fixture)
index = Index(vals)
formatted = index.format()
expected = [str(index[0]), str(index[1]), str(index[2]), "NaN"]
assert formatted == expected
assert index[3] is nulls_fixture
def test_format_with_name_time_info(self):
# bug I fixed 12/20/2011
dates = date_range("2011-01-01 04:00:00", periods=10, name="something")
formatted = dates.format(name=True)
assert formatted[0] == "something"
def test_format_datetime_with_time(self):
t = Index([datetime(2012, 2, 7), datetime(2012, 2, 7, 23)])
result = t.format()
expected = ["2012-02-07 00:00:00", "2012-02-07 23:00:00"]
assert len(result) == 2
assert result == expected
@pytest.mark.parametrize("op", ["any", "all"])
def test_logical_compat(self, op):
index = self.create_index()
assert getattr(index, op)() == getattr(index.values, op)()
def _check_method_works(self, method, index):
method(index)
def test_get_indexer(self):
index1 = Index([1, 2, 3, 4, 5])
index2 = Index([2, 4, 6])
r1 = index1.get_indexer(index2)
e1 = np.array([1, 3, -1], dtype=np.intp)
tm.assert_almost_equal(r1, e1)
@pytest.mark.parametrize("reverse", [True, False])
@pytest.mark.parametrize(
"expected,method",
[
(np.array([-1, 0, 0, 1, 1], dtype=np.intp), "pad"),
(np.array([-1, 0, 0, 1, 1], dtype=np.intp), "ffill"),
(np.array([0, 0, 1, 1, 2], dtype=np.intp), "backfill"),
(np.array([0, 0, 1, 1, 2], dtype=np.intp), "bfill"),
],
)
def test_get_indexer_methods(self, reverse, expected, method):
index1 = Index([1, 2, 3, 4, 5])
index2 = Index([2, 4, 6])
if reverse:
index1 = index1[::-1]
expected = expected[::-1]
result = index2.get_indexer(index1, method=method)
tm.assert_almost_equal(result, expected)
def test_get_indexer_invalid(self):
# GH10411
index = Index(np.arange(10))
with pytest.raises(ValueError, match="tolerance argument"):
index.get_indexer([1, 0], tolerance=1)
with pytest.raises(ValueError, match="limit argument"):
index.get_indexer([1, 0], limit=1)
@pytest.mark.parametrize(
"method, tolerance, indexer, expected",
[
("pad", None, [0, 5, 9], [0, 5, 9]),
("backfill", None, [0, 5, 9], [0, 5, 9]),
("nearest", None, [0, 5, 9], [0, 5, 9]),
("pad", 0, [0, 5, 9], [0, 5, 9]),
("backfill", 0, [0, 5, 9], [0, 5, 9]),
("nearest", 0, [0, 5, 9], [0, 5, 9]),
("pad", None, [0.2, 1.8, 8.5], [0, 1, 8]),
("backfill", None, [0.2, 1.8, 8.5], [1, 2, 9]),
("nearest", None, [0.2, 1.8, 8.5], [0, 2, 9]),
("pad", 1, [0.2, 1.8, 8.5], [0, 1, 8]),
("backfill", 1, [0.2, 1.8, 8.5], [1, 2, 9]),
("nearest", 1, [0.2, 1.8, 8.5], [0, 2, 9]),
("pad", 0.2, [0.2, 1.8, 8.5], [0, -1, -1]),
("backfill", 0.2, [0.2, 1.8, 8.5], [-1, 2, -1]),
("nearest", 0.2, [0.2, 1.8, 8.5], [0, 2, -1]),
],
)
def test_get_indexer_nearest(self, method, tolerance, indexer, expected):
index = Index(np.arange(10))
actual = index.get_indexer(indexer, method=method, tolerance=tolerance)
tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
@pytest.mark.parametrize("listtype", [list, tuple, Series, np.array])
@pytest.mark.parametrize(
"tolerance, expected",
list(
zip(
[[0.3, 0.3, 0.1], [0.2, 0.1, 0.1], [0.1, 0.5, 0.5]],
[[0, 2, -1], [0, -1, -1], [-1, 2, 9]],
)
),
)
def test_get_indexer_nearest_listlike_tolerance(
self, tolerance, expected, listtype
):
index = Index(np.arange(10))
actual = index.get_indexer(
[0.2, 1.8, 8.5], method="nearest", tolerance=listtype(tolerance)
)
tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
def test_get_indexer_nearest_error(self):
index = Index(np.arange(10))
with pytest.raises(ValueError, match="limit argument"):
index.get_indexer([1, 0], method="nearest", limit=1)
with pytest.raises(ValueError, match="tolerance size must match"):
index.get_indexer([1, 0], method="nearest", tolerance=[1, 2, 3])
@pytest.mark.parametrize(
"method,expected",
[("pad", [8, 7, 0]), ("backfill", [9, 8, 1]), ("nearest", [9, 7, 0])],
)
def test_get_indexer_nearest_decreasing(self, method, expected):
index = Index(np.arange(10))[::-1]
actual = index.get_indexer([0, 5, 9], method=method)
tm.assert_numpy_array_equal(actual, np.array([9, 4, 0], dtype=np.intp))
actual = index.get_indexer([0.2, 1.8, 8.5], method=method)
tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
@pytest.mark.parametrize(
"method,expected",
[
("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
],
)
def test_get_indexer_strings(self, method, expected):
index = pd.Index(["b", "c"])
actual = index.get_indexer(["a", "b", "c", "d"], method=method)
tm.assert_numpy_array_equal(actual, expected)
def test_get_indexer_strings_raises(self):
index = pd.Index(["b", "c"])
msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
with pytest.raises(TypeError, match=msg):
index.get_indexer(["a", "b", "c", "d"], method="nearest")
with pytest.raises(TypeError, match=msg):
index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
with pytest.raises(TypeError, match=msg):
index.get_indexer(
["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
)
@pytest.mark.parametrize("idx_class", [Int64Index, RangeIndex, Float64Index])
def test_get_indexer_numeric_index_boolean_target(self, idx_class):
# GH 16877
numeric_index = idx_class(RangeIndex((4)))
result = numeric_index.get_indexer([True, False, True])
expected = np.array([-1, -1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
def test_get_indexer_with_NA_values(
self, unique_nulls_fixture, unique_nulls_fixture2
):
# GH 22332
# check pairwise, that no pair of na values
# is mangled
if unique_nulls_fixture is unique_nulls_fixture2:
return # skip it, values are not unique
arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
index = pd.Index(arr, dtype=object)
result = index.get_indexer(
[unique_nulls_fixture, unique_nulls_fixture2, "Unknown"]
)
expected = np.array([0, 1, -1], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])
def test_get_loc(self, method):
index = pd.Index([0, 1, 2])
assert index.get_loc(1, method=method) == 1
if method:
assert index.get_loc(1, method=method, tolerance=0) == 1
@pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])
def test_get_loc_raises_bad_label(self, method):
index = pd.Index([0, 1, 2])
if method:
msg = "not supported between"
else:
msg = "invalid key"
with pytest.raises(TypeError, match=msg):
index.get_loc([1, 2], method=method)
@pytest.mark.parametrize(
"method,loc", [("pad", 1), ("backfill", 2), ("nearest", 1)]
)
def test_get_loc_tolerance(self, method, loc):
index = pd.Index([0, 1, 2])
assert index.get_loc(1.1, method) == loc
assert index.get_loc(1.1, method, tolerance=1) == loc
@pytest.mark.parametrize("method", ["pad", "backfill", "nearest"])
def test_get_loc_outside_tolerance_raises(self, method):
index = pd.Index([0, 1, 2])
with pytest.raises(KeyError, match="1.1"):
index.get_loc(1.1, method, tolerance=0.05)
def test_get_loc_bad_tolerance_raises(self):
index = pd.Index([0, 1, 2])
with pytest.raises(ValueError, match="must be numeric"):
index.get_loc(1.1, "nearest", tolerance="invalid")
def test_get_loc_tolerance_no_method_raises(self):
index = pd.Index([0, 1, 2])
with pytest.raises(ValueError, match="tolerance .* valid if"):
index.get_loc(1.1, tolerance=1)
def test_get_loc_raises_missized_tolerance(self):
index = pd.Index([0, 1, 2])
with pytest.raises(ValueError, match="tolerance size must match"):
index.get_loc(1.1, "nearest", tolerance=[1, 1])
def test_get_loc_raises_object_nearest(self):
index = pd.Index(["a", "c"])
with pytest.raises(TypeError, match="unsupported operand type"):
index.get_loc("a", method="nearest")
def test_get_loc_raises_object_tolerance(self):
index = pd.Index(["a", "c"])
with pytest.raises(TypeError, match="unsupported operand type"):
index.get_loc("a", method="pad", tolerance="invalid")
@pytest.mark.parametrize("dtype", [int, float])
def test_slice_locs(self, dtype):
index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
n = len(index)
assert index.slice_locs(start=2) == (2, n)
assert index.slice_locs(start=3) == (3, n)
assert index.slice_locs(3, 8) == (3, 6)
assert index.slice_locs(5, 10) == (3, n)
assert index.slice_locs(end=8) == (0, 6)
assert index.slice_locs(end=9) == (0, 7)
# reversed
index2 = index[::-1]
assert index2.slice_locs(8, 2) == (2, 6)
assert index2.slice_locs(7, 3) == (2, 5)
@pytest.mark.parametrize("dtype", [int, float])
def test_slice_float_locs(self, dtype):
index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
n = len(index)
assert index.slice_locs(5.0, 10.0) == (3, n)
assert index.slice_locs(4.5, 10.5) == (3, 8)
index2 = index[::-1]
assert index2.slice_locs(8.5, 1.5) == (2, 6)
assert index2.slice_locs(10.5, -1) == (0, n)
def test_slice_locs_dup(self):
index = Index(["a", "a", "b", "c", "d", "d"])
assert index.slice_locs("a", "d") == (0, 6)
assert index.slice_locs(end="d") == (0, 6)
assert index.slice_locs("a", "c") == (0, 4)
assert index.slice_locs("b", "d") == (2, 6)
index2 = index[::-1]
assert index2.slice_locs("d", "a") == (0, 6)
assert index2.slice_locs(end="a") == (0, 6)
assert index2.slice_locs("d", "b") == (0, 4)
assert index2.slice_locs("c", "a") == (2, 6)
@pytest.mark.parametrize("dtype", [int, float])
def test_slice_locs_dup_numeric(self, dtype):
index = Index(np.array([10, 12, 12, 14], dtype=dtype))
assert index.slice_locs(12, 12) == (1, 3)
assert index.slice_locs(11, 13) == (1, 3)
index2 = index[::-1]
assert index2.slice_locs(12, 12) == (1, 3)
assert index2.slice_locs(13, 11) == (1, 3)
def test_slice_locs_na(self):
index = Index([np.nan, 1, 2])
assert index.slice_locs(1) == (1, 3)
assert index.slice_locs(np.nan) == (0, 3)
index = Index([0, np.nan, np.nan, 1, 2])
assert index.slice_locs(np.nan) == (1, 5)
def test_slice_locs_na_raises(self):
index = Index([np.nan, 1, 2])
with pytest.raises(KeyError, match=""):
index.slice_locs(start=1.5)
with pytest.raises(KeyError, match=""):
index.slice_locs(end=1.5)
@pytest.mark.parametrize(
"in_slice,expected",
[
(pd.IndexSlice[::-1], "yxdcb"),
(pd.IndexSlice["b":"y":-1], ""), # type: ignore
(pd.IndexSlice["b"::-1], "b"), # type: ignore
(pd.IndexSlice[:"b":-1], "yxdcb"), # type: ignore
(pd.IndexSlice[:"y":-1], "y"), # type: ignore
(pd.IndexSlice["y"::-1], "yxdcb"), # type: ignore
(pd.IndexSlice["y"::-4], "yb"), # type: ignore
# absent labels
(pd.IndexSlice[:"a":-1], "yxdcb"), # type: ignore
(pd.IndexSlice[:"a":-2], "ydb"), # type: ignore
(pd.IndexSlice["z"::-1], "yxdcb"), # type: ignore
(pd.IndexSlice["z"::-3], "yc"), # type: ignore
(pd.IndexSlice["m"::-1], "dcb"), # type: ignore
(pd.IndexSlice[:"m":-1], "yx"), # type: ignore
(pd.IndexSlice["a":"a":-1], ""), # type: ignore
(pd.IndexSlice["z":"z":-1], ""), # type: ignore
(pd.IndexSlice["m":"m":-1], ""), # type: ignore
],
)
def test_slice_locs_negative_step(self, in_slice, expected):
index = Index(list("bcdxy"))
s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
result = index[s_start : s_stop : in_slice.step]
expected = pd.Index(list(expected))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
def test_drop_by_str_label(self, index):
n = len(index)
drop = index[list(range(5, 10))]
dropped = index.drop(drop)
expected = index[list(range(5)) + list(range(10, n))]
tm.assert_index_equal(dropped, expected)
dropped = index.drop(index[0])
expected = index[1:]
tm.assert_index_equal(dropped, expected)
@pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
@pytest.mark.parametrize("keys", [["foo", "bar"], ["1", "bar"]])
def test_drop_by_str_label_raises_missing_keys(self, index, keys):
with pytest.raises(KeyError, match=""):
index.drop(keys)
@pytest.mark.parametrize("index", ["string", "int", "float"], indirect=True)
def test_drop_by_str_label_errors_ignore(self, index):
n = len(index)
drop = index[list(range(5, 10))]
mixed = drop.tolist() + ["foo"]
dropped = index.drop(mixed, errors="ignore")
expected = index[list(range(5)) + list(range(10, n))]
tm.assert_index_equal(dropped, expected)
dropped = index.drop(["foo", "bar"], errors="ignore")
expected = index[list(range(n))]
tm.assert_index_equal(dropped, expected)
def test_drop_by_numeric_label_loc(self):
# TODO: Parametrize numeric and str tests after self.strIndex fixture
index = Index([1, 2, 3])
dropped = index.drop(1)
expected = Index([2, 3])
tm.assert_index_equal(dropped, expected)
def test_drop_by_numeric_label_raises_missing_keys(self):
index = Index([1, 2, 3])
with pytest.raises(KeyError, match=""):
index.drop([3, 4])
@pytest.mark.parametrize(
"key,expected", [(4, Index([1, 2, 3])), ([3, 4, 5], Index([1, 2]))]
)
def test_drop_by_numeric_label_errors_ignore(self, key, expected):
index = Index([1, 2, 3])
dropped = index.drop(key, errors="ignore")
tm.assert_index_equal(dropped, expected)
@pytest.mark.parametrize(
"values",
[["a", "b", ("c", "d")], ["a", ("c", "d"), "b"], [("c", "d"), "a", "b"]],
)
@pytest.mark.parametrize("to_drop", [[("c", "d"), "a"], ["a", ("c", "d")]])
def test_drop_tuple(self, values, to_drop):
# GH 18304
index = pd.Index(values)
expected = pd.Index(["b"])
result = index.drop(to_drop)
tm.assert_index_equal(result, expected)
removed = index.drop(to_drop[0])
for drop_me in to_drop[1], [to_drop[1]]:
result = removed.drop(drop_me)
tm.assert_index_equal(result, expected)
removed = index.drop(to_drop[1])
msg = fr"\"\[{re.escape(to_drop[1].__repr__())}\] not found in axis\""
for drop_me in to_drop[1], [to_drop[1]]:
with pytest.raises(KeyError, match=msg):
removed.drop(drop_me)
@pytest.mark.parametrize(
"method,expected,sort",
[
(
"intersection",
np.array(
[(1, "A"), (2, "A"), (1, "B"), (2, "B")],
dtype=[("num", int), ("let", "a1")],
),
False,
),
(
"intersection",
np.array(
[(1, "A"), (1, "B"), (2, "A"), (2, "B")],
dtype=[("num", int), ("let", "a1")],
),
None,
),
(
"union",
np.array(
[(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],
dtype=[("num", int), ("let", "a1")],
),
None,
),
],
)
def test_tuple_union_bug(self, method, expected, sort):
index1 = Index(
np.array(
[(1, "A"), (2, "A"), (1, "B"), (2, "B")],
dtype=[("num", int), ("let", "a1")],
)
)
index2 = Index(
np.array(
[(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],
dtype=[("num", int), ("let", "a1")],
)
)
result = getattr(index1, method)(index2, sort=sort)
assert result.ndim == 1
expected = Index(expected)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"attr",
[
"is_monotonic_increasing",
"is_monotonic_decreasing",
"_is_strictly_monotonic_increasing",
"_is_strictly_monotonic_decreasing",
],
)
def test_is_monotonic_incomparable(self, attr):
index = Index([5, datetime.now(), 7])
assert not getattr(index, attr)
def test_set_value_deprecated(self):
# GH 28621
idx = self.create_index()
arr = np.array([1, 2, 3])
with tm.assert_produces_warning(FutureWarning):
idx.set_value(arr, idx[1], 80)
assert arr[1] == 80
@pytest.mark.parametrize(
"index", ["string", "int", "datetime", "timedelta"], indirect=True
)
def test_get_value(self, index):
# TODO: Remove function? GH 19728
values = np.random.randn(100)
value = index[67]
with pytest.raises(AttributeError, match="has no attribute '_values'"):
# Index.get_value requires a Series, not an ndarray
with tm.assert_produces_warning(FutureWarning):
index.get_value(values, value)
with tm.assert_produces_warning(FutureWarning):
result = index.get_value(Series(values, index=values), value)
tm.assert_almost_equal(result, values[67])
@pytest.mark.parametrize("values", [["foo", "bar", "quux"], {"foo", "bar", "quux"}])
@pytest.mark.parametrize(
"index,expected",
[
(Index(["qux", "baz", "foo", "bar"]), np.array([False, False, True, True])),
(Index([]), np.array([], dtype=bool)), # empty
],
)
def test_isin(self, values, index, expected):
result = index.isin(values)
tm.assert_numpy_array_equal(result, expected)
def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
# Test cartesian product of null fixtures and ensure that we don't
# mangle the various types (save a corner case with PyPy)
# all nans are the same
if (
isinstance(nulls_fixture, float)
and isinstance(nulls_fixture2, float)
and math.isnan(nulls_fixture)
and math.isnan(nulls_fixture2)
):
tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, True]),
)
elif nulls_fixture is nulls_fixture2: # should preserve NA type
tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, True]),
)
else:
tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, False]),
)
def test_isin_nan_common_float64(self, nulls_fixture):
if nulls_fixture is pd.NaT:
pytest.skip("pd.NaT not compatible with Float64Index")
# Float64Index overrides isin, so must be checked separately
if nulls_fixture is pd.NA:
pytest.xfail("Float64Index cannot contain pd.NA")
tm.assert_numpy_array_equal(
Float64Index([1.0, nulls_fixture]).isin([np.nan]), np.array([False, True])
)
# we cannot compare NaT with NaN
tm.assert_numpy_array_equal(
Float64Index([1.0, nulls_fixture]).isin([pd.NaT]), np.array([False, False])
)
@pytest.mark.parametrize("level", [0, -1])
@pytest.mark.parametrize(
"index",
[
Index(["qux", "baz", "foo", "bar"]),
# Float64Index overrides isin, so must be checked separately
Float64Index([1.0, 2.0, 3.0, 4.0]),
],
)
def test_isin_level_kwarg(self, level, index):
values = index.tolist()[-2:] + ["nonexisting"]
expected = np.array([False, False, True, True])
tm.assert_numpy_array_equal(expected, index.isin(values, level=level))
index.name = "foobar"
tm.assert_numpy_array_equal(expected, index.isin(values, level="foobar"))
def test_isin_level_kwarg_bad_level_raises(self, index):
for level in [10, index.nlevels, -(index.nlevels + 1)]:
with pytest.raises(IndexError, match="Too many levels"):
index.isin([], level=level)
@pytest.mark.parametrize("label", [1.0, "foobar", "xyzzy", np.nan])
def test_isin_level_kwarg_bad_label_raises(self, label, index):
if isinstance(index, MultiIndex):
index = index.rename(["foo", "bar"] + index.names[2:])
msg = f"'Level {label} not found'"
else:
index = index.rename("foo")
msg = fr"Requested level \({label}\) does not match index name \(foo\)"
with pytest.raises(KeyError, match=msg):
index.isin([], level=label)
@pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
def test_isin_empty(self, empty):
# see gh-16991
index = Index(["a", "b"])
expected = np.array([False, False])
result = index.isin(empty)
tm.assert_numpy_array_equal(expected, result)
@pytest.mark.parametrize(
"values",
[
[1, 2, 3, 4],
[1.0, 2.0, 3.0, 4.0],
[True, True, True, True],
["foo", "bar", "baz", "qux"],
pd.date_range("2018-01-01", freq="D", periods=4),
],
)
def test_boolean_cmp(self, values):
index = Index(values)
result = index == values
expected = np.array([True, True, True, True], dtype=bool)
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("index", ["string"], indirect=True)
@pytest.mark.parametrize("name,level", [(None, 0), ("a", "a")])
def test_get_level_values(self, index, name, level):
expected = index.copy()
if name:
expected.name = name
result = expected.get_level_values(level)
tm.assert_index_equal(result, expected)
def test_slice_keep_name(self):
index = Index(["a", "b"], name="asdf")
assert index.name == index[1:].name
@pytest.mark.parametrize(
"index",
["unicode", "string", "datetime", "int", "uint", "float"],
indirect=True,
)
def test_join_self(self, index, join_type):
joined = index.join(index, how=join_type)
assert index is joined
@pytest.mark.parametrize("method", ["strip", "rstrip", "lstrip"])
def test_str_attribute(self, method):
# GH9068
index = Index([" jack", "jill ", " jesse ", "frank"])
expected = Index([getattr(str, method)(x) for x in index.values])
result = getattr(index.str, method)()
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
Index(range(5)),
tm.makeDateIndex(10),
MultiIndex.from_tuples([("foo", "1"), ("bar", "3")]),
period_range(start="2000", end="2010", freq="A"),
],
)
def test_str_attribute_raises(self, index):
with pytest.raises(AttributeError, match="only use .str accessor"):
index.str.repeat(2)
@pytest.mark.parametrize(
"expand,expected",
[
(None, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
(False, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
(
True,
MultiIndex.from_tuples(
[("a", "b", "c"), ("d", "e", np.nan), ("f", np.nan, np.nan)]
),
),
],
)
def test_str_split(self, expand, expected):
index = Index(["a b c", "d e", "f"])
if expand is not None:
result = index.str.split(expand=expand)
else:
result = index.str.split()
tm.assert_index_equal(result, expected)
def test_str_bool_return(self):
# test boolean case, should return np.array instead of boolean Index
index = Index(["a1", "a2", "b1", "b2"])
result = index.str.startswith("a")
expected = np.array([True, True, False, False])
tm.assert_numpy_array_equal(result, expected)
assert isinstance(result, np.ndarray)
def test_str_bool_series_indexing(self):
index = Index(["a1", "a2", "b1", "b2"])
s = Series(range(4), index=index)
result = s[s.index.str.startswith("a")]
expected = Series(range(2), index=["a1", "a2"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"index,expected", [(Index(list("abcd")), True), (Index(range(4)), False)]
)
def test_tab_completion(self, index, expected):
# GH 9910
result = "str" in dir(index)
assert result == expected
def test_indexing_doesnt_change_class(self):
index = Index([1, 2, 3, "a", "b", "c"])
assert index[1:3].identical(pd.Index([2, 3], dtype=np.object_))
assert index[[0, 1]].identical(pd.Index([1, 2], dtype=np.object_))
def test_outer_join_sort(self):
left_index = Index(np.random.permutation(15))
right_index = tm.makeDateIndex(10)
with tm.assert_produces_warning(RuntimeWarning):
result = left_index.join(right_index, how="outer")
# right_index in this case because DatetimeIndex has join precedence
# over Int64Index
with tm.assert_produces_warning(RuntimeWarning):
expected = right_index.astype(object).union(left_index.astype(object))
tm.assert_index_equal(result, expected)
def test_nan_first_take_datetime(self):
index = Index([pd.NaT, Timestamp("20130101"), Timestamp("20130102")])
result = index.take([-1, 0, 1])
expected = Index([index[-1], index[0], index[1]])
tm.assert_index_equal(result, expected)
def test_take_fill_value(self):
# GH 12631
index = pd.Index(list("ABC"), name="xxx")
result = index.take(np.array([1, 0, -1]))
expected = pd.Index(list("BAC"), name="xxx")
tm.assert_index_equal(result, expected)
# fill_value
result = index.take(np.array([1, 0, -1]), fill_value=True)
expected = pd.Index(["B", "A", np.nan], name="xxx")
tm.assert_index_equal(result, expected)
# allow_fill=False
result = index.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
expected = pd.Index(["B", "A", "C"], name="xxx")
tm.assert_index_equal(result, expected)
def test_take_fill_value_none_raises(self):
index = pd.Index(list("ABC"), name="xxx")
msg = (
"When allow_fill=True and fill_value is not None, "
"all indices must be >= -1"
)
with pytest.raises(ValueError, match=msg):
index.take(np.array([1, 0, -2]), fill_value=True)
with pytest.raises(ValueError, match=msg):
index.take(np.array([1, 0, -5]), fill_value=True)
def test_take_bad_bounds_raises(self):
index = pd.Index(list("ABC"), name="xxx")
with pytest.raises(IndexError, match="out of bounds"):
index.take(np.array([1, -5]))
@pytest.mark.parametrize("name", [None, "foobar"])
@pytest.mark.parametrize(
"labels",
[
[],
np.array([]),
["A", "B", "C"],
["C", "B", "A"],
np.array(["A", "B", "C"]),
np.array(["C", "B", "A"]),
# Must preserve name even if dtype changes
pd.date_range("20130101", periods=3).values,
pd.date_range("20130101", periods=3).tolist(),
],
)
def test_reindex_preserves_name_if_target_is_list_or_ndarray(self, name, labels):
# GH6552
index = pd.Index([0, 1, 2])
index.name = name
assert index.reindex(labels)[0].name == name
@pytest.mark.parametrize("labels", [[], np.array([]), np.array([], dtype=np.int64)])
def test_reindex_preserves_type_if_target_is_empty_list_or_array(self, labels):
# GH7774
index = pd.Index(list("abc"))
assert index.reindex(labels)[0].dtype.type == np.object_
@pytest.mark.parametrize(
"labels,dtype",
[
(pd.Int64Index([]), np.int64),
(pd.Float64Index([]), np.float64),
(pd.DatetimeIndex([]), np.datetime64),
],
)
def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self, labels, dtype):
# GH7774
index = pd.Index(list("abc"))
assert index.reindex(labels)[0].dtype.type == dtype
def test_reindex_no_type_preserve_target_empty_mi(self):
index = pd.Index(list("abc"))
result = index.reindex(
pd.MultiIndex([pd.Int64Index([]), pd.Float64Index([])], [[], []])
)[0]
assert result.levels[0].dtype.type == np.int64
assert result.levels[1].dtype.type == np.float64
def test_groupby(self):
index = Index(range(5))
result = index.groupby(np.array([1, 1, 2, 2, 2]))
expected = {1: pd.Index([0, 1]), 2: pd.Index([2, 3, 4])}
tm.assert_dict_equal(result, expected)
@pytest.mark.parametrize(
"mi,expected",
[
(MultiIndex.from_tuples([(1, 2), (4, 5)]), np.array([True, True])),
(MultiIndex.from_tuples([(1, 2), (4, 6)]), np.array([True, False])),
],
)
def test_equals_op_multiindex(self, mi, expected):
# GH9785
# test comparisons of multiindex
df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])
result = df.index == mi
tm.assert_numpy_array_equal(result, expected)
def test_equals_op_multiindex_identify(self):
df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])
result = df.index == df.index
expected = np.array([True, True])
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)]),
Index(["foo", "bar", "baz"]),
],
)
def test_equals_op_mismatched_multiindex_raises(self, index):
df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])
with pytest.raises(ValueError, match="Lengths must match"):
df.index == index
def test_equals_op_index_vs_mi_same_length(self):
mi = MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)])
index = Index(["foo", "bar", "baz"])
result = mi == index
expected = np.array([False, False, False])
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("dt_conv", [pd.to_datetime, pd.to_timedelta])
def test_dt_conversion_preserves_name(self, dt_conv):
# GH 10875
index = pd.Index(["01:02:03", "01:02:04"], name="label")
assert index.name == dt_conv(index).name
@pytest.mark.parametrize(
"index,expected",
[
# ASCII
# short
(
pd.Index(["a", "bb", "ccc"]),
"""Index(['a', 'bb', 'ccc'], dtype='object')""",
),
# multiple lines
(
pd.Index(["a", "bb", "ccc"] * 10),
"""\
Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',
'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',
'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
dtype='object')""",
),
# truncated
(
pd.Index(["a", "bb", "ccc"] * 100),
"""\
Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
...
'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
dtype='object', length=300)""",
),
# Non-ASCII
# short
(
pd.Index(["あ", "いい", "ううう"]),
"""Index(['あ', 'いい', 'ううう'], dtype='object')""",
),
# multiple lines
(
pd.Index(["あ", "いい", "ううう"] * 10),
(
"Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
"'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
"'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう'],\n"
" dtype='object')"
),
),
# truncated
(
pd.Index(["あ", "いい", "ううう"] * 100),
(
"Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "
"'あ', 'いい', 'ううう', 'あ',\n"
" ...\n"
" 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう', 'あ', 'いい', 'ううう'],\n"
" dtype='object', length=300)"
),
),
],
)
def test_string_index_repr(self, index, expected):
result = repr(index)
assert result == expected
@pytest.mark.parametrize(
"index,expected",
[
# short
(
pd.Index(["あ", "いい", "ううう"]),
("Index(['あ', 'いい', 'ううう'], dtype='object')"),
),
# multiple lines
(
pd.Index(["あ", "いい", "ううう"] * 10),
(
"Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ', 'いい', 'ううう'],\n"
" dtype='object')"
""
),
),
# truncated
(
pd.Index(["あ", "いい", "ううう"] * 100),
(
"Index(['あ', 'いい', 'ううう', 'あ', 'いい', "
"'ううう', 'あ', 'いい', 'ううう',\n"
" 'あ',\n"
" ...\n"
" 'ううう', 'あ', 'いい', 'ううう', 'あ', "
"'いい', 'ううう', 'あ', 'いい',\n"
" 'ううう'],\n"
" dtype='object', length=300)"
),
),
],
)
def test_string_index_repr_with_unicode_option(self, index, expected):
# Enable Unicode option -----------------------------------------
with cf.option_context("display.unicode.east_asian_width", True):
result = repr(index)
assert result == expected
def test_cached_properties_not_settable(self):
index = pd.Index([1, 2, 3])
with pytest.raises(AttributeError, match="Can't set attribute"):
index.is_unique = False
@async_mark()
async def test_tab_complete_warning(self, ip):
# https://github.com/pandas-dev/pandas/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; idx = pd.Index([1, 2])"
await ip.run_code(code)
# GH 31324 newer jedi version raises Deprecation warning
import jedi
if jedi.__version__ < "0.16.0":
warning = tm.assert_produces_warning(None)
else:
warning = tm.assert_produces_warning(
DeprecationWarning, check_stacklevel=False
)
with warning:
with provisionalcompleter("ignore"):
list(ip.Completer.completions("idx.", 4))
def test_contains_method_removed(self, index):
# GH#30103 method removed for all types except IntervalIndex
if isinstance(index, pd.IntervalIndex):
index.contains(1)
else:
msg = f"'{type(index).__name__}' object has no attribute 'contains'"
with pytest.raises(AttributeError, match=msg):
index.contains(1)
class TestMixedIntIndex(Base):
# Mostly the tests from common.py for which the results differ
# in py2 and py3 because ints and strings are uncomparable in py3
# (GH 13514)
_holder = Index
@pytest.fixture(params=[[0, "a", 1, "b", 2, "c"]], ids=["mixedIndex"])
def index(self, request):
return Index(request.param)
def create_index(self) -> Index:
return Index([0, "a", 1, "b", 2, "c"])
def test_argsort(self):
index = self.create_index()
with pytest.raises(TypeError, match="'>|<' not supported"):
index.argsort()
def test_numpy_argsort(self):
index = self.create_index()
with pytest.raises(TypeError, match="'>|<' not supported"):
np.argsort(index)
def test_copy_name(self):
# Check that "name" argument passed at initialization is honoured
# GH12309
index = self.create_index()
first = type(index)(index, copy=True, name="mario")
second = type(first)(first, copy=False)
# Even though "copy=False", we want a new object.
assert first is not second
tm.assert_index_equal(first, second)
assert first.name == "mario"
assert second.name == "mario"
s1 = Series(2, index=first)
s2 = Series(3, index=second[:-1])
s3 = s1 * s2
assert s3.index.name == "mario"
def test_copy_name2(self):
# Check that adding a "name" parameter to the copy is honored
# GH14302
index = pd.Index([1, 2], name="MyName")
index1 = index.copy()
tm.assert_index_equal(index, index1)
index2 = index.copy(name="NewName")
tm.assert_index_equal(index, index2, check_names=False)
assert index.name == "MyName"
assert index2.name == "NewName"
index3 = index.copy(names=["NewName"])
tm.assert_index_equal(index, index3, check_names=False)
assert index.name == "MyName"
assert index.names == ["MyName"]
assert index3.name == "NewName"
assert index3.names == ["NewName"]
def test_unique_na(self):
idx = pd.Index([2, np.nan, 2, 1], name="my_index")
expected = pd.Index([2, np.nan, 1], name="my_index")
result = idx.unique()
tm.assert_index_equal(result, expected)
def test_logical_compat(self):
index = self.create_index()
assert index.all() == index.values.all()
assert index.any() == index.values.any()
@pytest.mark.parametrize("how", ["any", "all"])
@pytest.mark.parametrize("dtype", [None, object, "category"])
@pytest.mark.parametrize(
"vals,expected",
[
([1, 2, 3], [1, 2, 3]),
([1.0, 2.0, 3.0], [1.0, 2.0, 3.0]),
([1.0, 2.0, np.nan, 3.0], [1.0, 2.0, 3.0]),
(["A", "B", "C"], ["A", "B", "C"]),
(["A", np.nan, "B", "C"], ["A", "B", "C"]),
],
)
def test_dropna(self, how, dtype, vals, expected):
# GH 6194
index = pd.Index(vals, dtype=dtype)
result = index.dropna(how=how)
expected = pd.Index(expected, dtype=dtype)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("how", ["any", "all"])
@pytest.mark.parametrize(
"index,expected",
[
(
pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
),
(
pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", pd.NaT]),
pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
),
(
pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
),
(
pd.TimedeltaIndex([pd.NaT, "1 days", "2 days", "3 days", pd.NaT]),
pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),
),
(
pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
),
(
pd.PeriodIndex(["2012-02", "2012-04", "NaT", "2012-05"], freq="M"),
pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
),
],
)
def test_dropna_dt_like(self, how, index, expected):
result = index.dropna(how=how)
tm.assert_index_equal(result, expected)
def test_dropna_invalid_how_raises(self):
msg = "invalid how option: xxx"
with pytest.raises(ValueError, match=msg):
pd.Index([1, 2, 3]).dropna(how="xxx")
def test_get_combined_index(self):
result = _get_combined_index([])
expected = Index([])
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
pd.Index([np.nan]),
pd.Index([np.nan, 1]),
pd.Index([1, 2, np.nan]),
pd.Index(["a", "b", np.nan]),
pd.to_datetime(["NaT"]),
pd.to_datetime(["NaT", "2000-01-01"]),
pd.to_datetime(["2000-01-01", "NaT", "2000-01-02"]),
pd.to_timedelta(["1 day", "NaT"]),
],
)
def test_is_monotonic_na(self, index):
assert index.is_monotonic_increasing is False
assert index.is_monotonic_decreasing is False
assert index._is_strictly_monotonic_increasing is False
assert index._is_strictly_monotonic_decreasing is False
def test_repr_summary(self):
with cf.option_context("display.max_seq_items", 10):
result = repr(pd.Index(np.arange(1000)))
assert len(result) < 200
assert "..." in result
@pytest.mark.parametrize("klass", [Series, DataFrame])
def test_int_name_format(self, klass):
index = Index(["a", "b", "c"], name=0)
result = klass(list(range(3)), index=index)
assert "0" in repr(result)
def test_str_to_bytes_raises(self):
# GH 26447
index = Index([str(x) for x in range(10)])
msg = "^'str' object cannot be interpreted as an integer$"
with pytest.raises(TypeError, match=msg):
bytes(index)
def test_intersect_str_dates(self):
dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
index1 = Index(dt_dates, dtype=object)
index2 = Index(["aa"], dtype=object)
result = index2.intersection(index1)
expected = Index([], dtype=object)
tm.assert_index_equal(result, expected)
def test_index_repr_bool_nan(self):
# GH32146
arr = Index([True, False, np.nan], dtype=object)
exp1 = arr.format()
out1 = ["True", "False", "NaN"]
assert out1 == exp1
exp2 = repr(arr)
out2 = "Index([True, False, nan], dtype='object')"
assert out2 == exp2
@pytest.mark.filterwarnings("ignore:elementwise comparison failed:FutureWarning")
def test_index_with_tuple_bool(self):
# GH34123
# TODO: remove tupleize_cols=False once correct behaviour is restored
# TODO: also this op right now produces FutureWarning from numpy
idx = Index([("a", "b"), ("b", "c"), ("c", "a")], tupleize_cols=False)
result = idx == ("c", "a",)
expected = np.array([False, False, True])
tm.assert_numpy_array_equal(result, expected)
class TestIndexUtils:
@pytest.mark.parametrize(
"data, names, expected",
[
([[1, 2, 3]], None, Index([1, 2, 3])),
([[1, 2, 3]], ["name"], Index([1, 2, 3], name="name")),
(
[["a", "a"], ["c", "d"]],
None,
MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]]),
),
(
[["a", "a"], ["c", "d"]],
["L1", "L2"],
MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]], names=["L1", "L2"]),
),
],
)
def test_ensure_index_from_sequences(self, data, names, expected):
result = ensure_index_from_sequences(data, names)
tm.assert_index_equal(result, expected)
def test_ensure_index_mixed_closed_intervals(self):
# GH27172
intervals = [
pd.Interval(0, 1, closed="left"),
pd.Interval(1, 2, closed="right"),
pd.Interval(2, 3, closed="neither"),
pd.Interval(3, 4, closed="both"),
]
result = ensure_index(intervals)
expected = Index(intervals, dtype=object)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
"opname",
[
"eq",
"ne",
"le",
"lt",
"ge",
"gt",
"add",
"radd",
"sub",
"rsub",
"mul",
"rmul",
"truediv",
"rtruediv",
"floordiv",
"rfloordiv",
"pow",
"rpow",
"mod",
"divmod",
],
)
def test_generated_op_names(opname, index):
if isinstance(index, ABCIndex) and opname == "rsub":
# pd.Index.__rsub__ does not exist; though the method does exist
# for subclasses. see GH#19723
return
opname = f"__{opname}__"
method = getattr(index, opname)
assert method.__name__ == opname
@pytest.mark.parametrize("index_maker", tm.index_subclass_makers_generator())
def test_index_subclass_constructor_wrong_kwargs(index_maker):
# GH #19348
with pytest.raises(TypeError, match="unexpected keyword argument"):
index_maker(foo="bar")
def test_deprecated_fastpath():
msg = "[Uu]nexpected keyword argument"
with pytest.raises(TypeError, match=msg):
pd.Index(np.array(["a", "b"], dtype=object), name="test", fastpath=True)
with pytest.raises(TypeError, match=msg):
pd.Int64Index(np.array([1, 2, 3], dtype="int64"), name="test", fastpath=True)
with pytest.raises(TypeError, match=msg):
pd.RangeIndex(0, 5, 2, name="test", fastpath=True)
with pytest.raises(TypeError, match=msg):
pd.CategoricalIndex(["a", "b", "c"], name="test", fastpath=True)
def test_shape_of_invalid_index():
# Currently, it is possible to create "invalid" index objects backed by
# a multi-dimensional array (see https://github.com/pandas-dev/pandas/issues/27125
# about this). However, as long as this is not solved in general,this test ensures
# that the returned shape is consistent with this underlying array for
# compat with matplotlib (see https://github.com/pandas-dev/pandas/issues/27775)
idx = pd.Index([0, 1, 2, 3])
with tm.assert_produces_warning(FutureWarning):
# GH#30588 multi-dimensional indexing deprecated
assert idx[:, None].shape == (4, 1)
def test_validate_1d_input():
# GH#27125 check that we do not have >1-dimensional input
msg = "Index data must be 1-dimensional"
arr = np.arange(8).reshape(2, 2, 2)
with pytest.raises(ValueError, match=msg):
pd.Index(arr)
with pytest.raises(ValueError, match=msg):
pd.Float64Index(arr.astype(np.float64))
with pytest.raises(ValueError, match=msg):
pd.Int64Index(arr.astype(np.int64))
with pytest.raises(ValueError, match=msg):
pd.UInt64Index(arr.astype(np.uint64))
df = pd.DataFrame(arr.reshape(4, 2))
with pytest.raises(ValueError, match=msg):
pd.Index(df)
# GH#13601 trying to assign a multi-dimensional array to an index is not
# allowed
ser = pd.Series(0, range(4))
with pytest.raises(ValueError, match=msg):
ser.index = np.array([[2, 3]] * 4)
def test_convert_almost_null_slice(index):
# slice with None at both ends, but not step
key = slice(None, None, "foo")
if isinstance(index, pd.IntervalIndex):
msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
with pytest.raises(ValueError, match=msg):
index._convert_slice_indexer(key, "loc")
else:
msg = "'>=' not supported between instances of 'str' and 'int'"
with pytest.raises(TypeError, match=msg):
index._convert_slice_indexer(key, "loc")
dtlike_dtypes = [
np.dtype("timedelta64[ns]"),
np.dtype("datetime64[ns]"),
pd.DatetimeTZDtype("ns", "Asia/Tokyo"),
pd.PeriodDtype("ns"),
]
@pytest.mark.parametrize("ldtype", dtlike_dtypes)
@pytest.mark.parametrize("rdtype", dtlike_dtypes)
def test_get_indexer_non_unique_wrong_dtype(ldtype, rdtype):
vals = np.tile(3600 * 10 ** 9 * np.arange(3), 2)
def construct(dtype):
if dtype is dtlike_dtypes[-1]:
# PeriodArray will try to cast ints to strings
return pd.DatetimeIndex(vals).astype(dtype)
return pd.Index(vals, dtype=dtype)
left = construct(ldtype)
right = construct(rdtype)
result = left.get_indexer_non_unique(right)
if ldtype is rdtype:
ex1 = np.array([0, 3, 1, 4, 2, 5] * 2, dtype=np.intp)
ex2 = np.array([], dtype=np.intp)
tm.assert_numpy_array_equal(result[0], ex1)
tm.assert_numpy_array_equal(result[1], ex2.astype(np.int64))
else:
no_matches = np.array([-1] * 6, dtype=np.intp)
tm.assert_numpy_array_equal(result[0], no_matches)
tm.assert_numpy_array_equal(result[1], no_matches)
Reference in a new issue View git blame Copy permalink