def test_equals_numeric_other_index_type(self, other):
i = Float64Index([1.0, 2.0])
assert i.equals(other)
assert other.equals(i)
def create_index(self):
return Float64Index(np.arange(5, dtype='float64'))
def test_get_indexer_nan(self):
# GH#7820
result = Float64Index([1, 2, np.nan]).get_indexer([np.nan])
expected = np.array([2], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
def test_astype_from_object(self):
index = Index([1.0, np.nan, 0.2], dtype='object')
result = index.astype(float)
expected = Float64Index([1.0, np.nan, 0.2])
self.assertEqual(result.dtype, expected.dtype)
tm.assert_index_equal(result, expected)
def test_ufunc_compat(self):
idx = self._holder(np.arange(5, dtype='int64'))
result = np.sin(idx)
expected = Float64Index(np.sin(np.arange(5, dtype='int64')))
tm.assert_index_equal(result, expected)
def to_julian_date(self) -> Float64Index:
from pandas.core.indexes.api import Float64Index
arr = self._data.to_julian_date()
return Float64Index._simple_new(arr, name=self.name)
def test_numeric_compat(self):
idx = self.create_index()
didx = idx * idx
result = idx * 1
tm.assert_index_equal(result, idx)
result = 1 * idx
tm.assert_index_equal(result, idx)
# in general not true for RangeIndex
if not isinstance(idx, RangeIndex):
result = idx * idx
tm.assert_index_equal(result, idx**2)
# truediv under PY3
result = idx / 1
expected = idx
if PY3:
expected = expected.astype('float64')
tm.assert_index_equal(result, expected)
result = idx / 2
if PY3:
expected = expected.astype('float64')
expected = Index(idx.values / 2)
tm.assert_index_equal(result, expected)
result = idx // 1
tm.assert_index_equal(result, idx)
result = idx * np.array(5, dtype='int64')
tm.assert_index_equal(result, idx * 5)
arr_dtype = 'uint64' if isinstance(idx, UInt64Index) else 'int64'
result = idx * np.arange(5, dtype=arr_dtype)
tm.assert_index_equal(result, didx)
result = idx * Series(np.arange(5, dtype=arr_dtype))
tm.assert_index_equal(result, didx)
result = idx * Series(np.arange(5, dtype='float64') + 0.1)
expected = Float64Index(
np.arange(5, dtype='float64') *
(np.arange(5, dtype='float64') + 0.1))
tm.assert_index_equal(result, expected)
# invalid
self.assertRaises(TypeError,
lambda: idx * date_range('20130101', periods=5))
self.assertRaises(ValueError, lambda: idx * idx[0:3])
self.assertRaises(ValueError, lambda: idx * np.array([1, 2]))
result = divmod(idx, 2)
with np.errstate(all='ignore'):
div, mod = divmod(idx.values, 2)
expected = Index(div), Index(mod)
for r, e in zip(result, expected):
tm.assert_index_equal(r, e)
result = divmod(idx, full_like(idx.values, 2))
with np.errstate(all='ignore'):
div, mod = divmod(idx.values, full_like(idx.values, 2))
expected = Index(div), Index(mod)
for r, e in zip(result, expected):
tm.assert_index_equal(r, e)
result = divmod(idx, Series(full_like(idx.values, 2)))
with np.errstate(all='ignore'):
div, mod = divmod(
idx.values,
full_like(idx.values, 2),
)
expected = Index(div), Index(mod)
for r, e in zip(result, expected):
tm.assert_index_equal(r, e)
# test power calculations both ways, GH 14973
expected = pd.Float64Index(2.0**idx.values)
result = 2.0**idx
tm.assert_index_equal(result, expected)
expected = pd.Float64Index(idx.values**2.0)
result = idx**2.0
tm.assert_index_equal(result, expected)
def setup_method(self, method):
self.series_ints = Series(np.random.rand(4), index=np.arange(0, 8, 2))
self.frame_ints = DataFrame(np.random.randn(4, 4),
index=np.arange(0, 8, 2),
columns=np.arange(0, 12, 3))
self.series_uints = Series(np.random.rand(4),
index=UInt64Index(np.arange(0, 8, 2)))
self.frame_uints = DataFrame(
np.random.randn(4, 4),
index=UInt64Index(range(0, 8, 2)),
columns=UInt64Index(range(0, 12, 3)),
)
self.series_floats = Series(np.random.rand(4),
index=Float64Index(range(0, 8, 2)))
self.frame_floats = DataFrame(
np.random.randn(4, 4),
index=Float64Index(range(0, 8, 2)),
columns=Float64Index(range(0, 12, 3)),
)
m_idces = [
MultiIndex.from_product([[1, 2], [3, 4]]),
MultiIndex.from_product([[5, 6], [7, 8]]),
MultiIndex.from_product([[9, 10], [11, 12]]),
]
self.series_multi = Series(np.random.rand(4), index=m_idces[0])
self.frame_multi = DataFrame(np.random.randn(4, 4),
index=m_idces[0],
columns=m_idces[1])
self.series_labels = Series(np.random.randn(4), index=list("abcd"))
self.frame_labels = DataFrame(np.random.randn(4, 4),
index=list("abcd"),
columns=list("ABCD"))
self.series_mixed = Series(np.random.randn(4), index=[2, 4, "null", 8])
self.frame_mixed = DataFrame(np.random.randn(4, 4),
index=[2, 4, "null", 8])
self.series_ts = Series(np.random.randn(4),
index=date_range("20130101", periods=4))
self.frame_ts = DataFrame(np.random.randn(4, 4),
index=date_range("20130101", periods=4))
dates_rev = date_range("20130101",
periods=4).sort_values(ascending=False)
self.series_ts_rev = Series(np.random.randn(4), index=dates_rev)
self.frame_ts_rev = DataFrame(np.random.randn(4, 4), index=dates_rev)
self.frame_empty = DataFrame()
self.series_empty = Series(dtype=object)
# form agglomerates
for kind in self._kinds:
d = dict()
for typ in self._typs:
d[typ] = getattr(self, "{kind}_{typ}".format(kind=kind,
typ=typ))
setattr(self, kind, d)
def setup_method(self, method):
self.indices = dict(mixed=Float64Index([1.5, 2, 3, 4, 5]),
float=Float64Index(np.arange(5) * 2.5),
mixed_dec=Float64Index([5, 4, 3, 2, 1.5]),
float_dec=Float64Index(np.arange(4, -1, -1) * 2.5))
self.setup_indices()
def setup_method(self, method):
self.series_ints = Series(np.random.rand(4), index=lrange(0, 8, 2))
self.frame_ints = DataFrame(np.random.randn(4, 4),
index=lrange(0, 8, 2),
columns=lrange(0, 12, 3))
with catch_warnings(record=True):
self.panel_ints = Panel(np.random.rand(4, 4, 4),
items=lrange(0, 8, 2),
major_axis=lrange(0, 12, 3),
minor_axis=lrange(0, 16, 4))
self.series_uints = Series(np.random.rand(4),
index=UInt64Index(lrange(0, 8, 2)))
self.frame_uints = DataFrame(np.random.randn(4, 4),
index=UInt64Index(lrange(0, 8, 2)),
columns=UInt64Index(lrange(0, 12, 3)))
with catch_warnings(record=True):
self.panel_uints = Panel(np.random.rand(4, 4, 4),
items=UInt64Index(lrange(0, 8, 2)),
major_axis=UInt64Index(lrange(0, 12, 3)),
minor_axis=UInt64Index(lrange(0, 16, 4)))
self.series_floats = Series(np.random.rand(4),
index=Float64Index(range(0, 8, 2)))
self.frame_floats = DataFrame(np.random.randn(4, 4),
index=Float64Index(range(0, 8, 2)),
columns=Float64Index(range(0, 12, 3)))
with catch_warnings(record=True):
self.panel_floats = Panel(np.random.rand(4, 4, 4),
items=Float64Index(range(0, 8, 2)),
major_axis=Float64Index(range(0, 12, 3)),
minor_axis=Float64Index(range(0, 16, 4)))
m_idces = [
MultiIndex.from_product([[1, 2], [3, 4]]),
MultiIndex.from_product([[5, 6], [7, 8]]),
MultiIndex.from_product([[9, 10], [11, 12]])
]
self.series_multi = Series(np.random.rand(4), index=m_idces[0])
self.frame_multi = DataFrame(np.random.randn(4, 4),
index=m_idces[0],
columns=m_idces[1])
with catch_warnings(record=True):
self.panel_multi = Panel(np.random.rand(4, 4, 4),
items=m_idces[0],
major_axis=m_idces[1],
minor_axis=m_idces[2])
self.series_labels = Series(np.random.randn(4), index=list('abcd'))
self.frame_labels = DataFrame(np.random.randn(4, 4),
index=list('abcd'),
columns=list('ABCD'))
with catch_warnings(record=True):
self.panel_labels = Panel(np.random.randn(4, 4, 4),
items=list('abcd'),
major_axis=list('ABCD'),
minor_axis=list('ZYXW'))
self.series_mixed = Series(np.random.randn(4), index=[2, 4, 'null', 8])
self.frame_mixed = DataFrame(np.random.randn(4, 4),
index=[2, 4, 'null', 8])
with catch_warnings(record=True):
self.panel_mixed = Panel(np.random.randn(4, 4, 4),
items=[2, 4, 'null', 8])
self.series_ts = Series(np.random.randn(4),
index=date_range('20130101', periods=4))
self.frame_ts = DataFrame(np.random.randn(4, 4),
index=date_range('20130101', periods=4))
with catch_warnings(record=True):
self.panel_ts = Panel(np.random.randn(4, 4, 4),
items=date_range('20130101', periods=4))
dates_rev = (date_range('20130101',
periods=4).sort_values(ascending=False))
self.series_ts_rev = Series(np.random.randn(4), index=dates_rev)
self.frame_ts_rev = DataFrame(np.random.randn(4, 4), index=dates_rev)
with catch_warnings(record=True):
self.panel_ts_rev = Panel(np.random.randn(4, 4, 4),
items=dates_rev)
self.frame_empty = DataFrame({})
self.series_empty = Series({})
with catch_warnings(record=True):
self.panel_empty = Panel({})
# form agglomerates
for o in self._objs:
d = dict()
for t in self._typs:
d[t] = getattr(self, '%s_%s' % (o, t), None)
setattr(self, o, d)
def test_insert(self, nulls_fixture):
# GH 18295 (test missing)
index = self.create_index()
expected = Float64Index([index[0], np.nan] + list(index[1:]))
result = index.insert(1, nulls_fixture)
tm.assert_index_equal(result, expected)
def test_tdi_div_tdlike_scalar_with_nat(self, delta):
rng = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo')
expected = Float64Index([12, np.nan, 24], name='foo')
result = rng / delta
tm.assert_index_equal(result, expected)
def setup(self):
N = 100000
a = np.arange(N)
self.ind = Float64Index(a * 4.8000000418824129e-08)
def test_contains_float64_nans(self):
index = Float64Index([1.0, 2.0, np.nan])
assert np.nan in index
def test_doesnt_contain_all_the_things(self):
i = Float64Index([np.nan])
assert not i.isin([0]).item()
assert not i.isin([1]).item()
assert i.isin([np.nan]).item()
def test_contains_not_nans(self):
i = Float64Index([1.0, 2.0, np.nan])
assert 1.0 in i
def test_array_equivalent(dtype_equal):
assert array_equivalent(np.array([np.nan, np.nan]),
np.array([np.nan, np.nan]),
dtype_equal=dtype_equal)
assert array_equivalent(
np.array([np.nan, 1, np.nan]),
np.array([np.nan, 1, np.nan]),
dtype_equal=dtype_equal,
)
assert array_equivalent(
np.array([np.nan, None], dtype="object"),
np.array([np.nan, None], dtype="object"),
dtype_equal=dtype_equal,
)
# Check the handling of nested arrays in array_equivalent_object
assert array_equivalent(
np.array([np.array([np.nan, None], dtype="object"), None],
dtype="object"),
np.array([np.array([np.nan, None], dtype="object"), None],
dtype="object"),
dtype_equal=dtype_equal,
)
assert array_equivalent(
np.array([np.nan, 1 + 1j], dtype="complex"),
np.array([np.nan, 1 + 1j], dtype="complex"),
dtype_equal=dtype_equal,
)
assert not array_equivalent(
np.array([np.nan, 1 + 1j], dtype="complex"),
np.array([np.nan, 1 + 2j], dtype="complex"),
dtype_equal=dtype_equal,
)
assert not array_equivalent(
np.array([np.nan, 1, np.nan]),
np.array([np.nan, 2, np.nan]),
dtype_equal=dtype_equal,
)
assert not array_equivalent(np.array(["a", "b", "c", "d"]),
np.array(["e", "e"]),
dtype_equal=dtype_equal)
assert array_equivalent(Float64Index([0, np.nan]),
Float64Index([0, np.nan]),
dtype_equal=dtype_equal)
assert not array_equivalent(Float64Index([0, np.nan]),
Float64Index([1, np.nan]),
dtype_equal=dtype_equal)
assert array_equivalent(DatetimeIndex([0, np.nan]),
DatetimeIndex([0, np.nan]),
dtype_equal=dtype_equal)
assert not array_equivalent(DatetimeIndex([0, np.nan]),
DatetimeIndex([1, np.nan]),
dtype_equal=dtype_equal)
assert array_equivalent(
TimedeltaIndex([0, np.nan]),
TimedeltaIndex([0, np.nan]),
dtype_equal=dtype_equal,
)
assert not array_equivalent(
TimedeltaIndex([0, np.nan]),
TimedeltaIndex([1, np.nan]),
dtype_equal=dtype_equal,
)
assert array_equivalent(
DatetimeIndex([0, np.nan], tz="US/Eastern"),
DatetimeIndex([0, np.nan], tz="US/Eastern"),
dtype_equal=dtype_equal,
)
assert not array_equivalent(
DatetimeIndex([0, np.nan], tz="US/Eastern"),
DatetimeIndex([1, np.nan], tz="US/Eastern"),
dtype_equal=dtype_equal,
)
# The rest are not dtype_equal
assert not array_equivalent(DatetimeIndex([0, np.nan]),
DatetimeIndex([0, np.nan], tz="US/Eastern"))
assert not array_equivalent(
DatetimeIndex([0, np.nan], tz="CET"),
DatetimeIndex([0, np.nan], tz="US/Eastern"),
)
assert not array_equivalent(DatetimeIndex([0, np.nan]),
TimedeltaIndex([0, np.nan]))
def test_insert(self):
# GH 18295 (test missing)
expected = Float64Index([0, np.nan, 1, 2, 3, 4])
for na in (np.nan, pd.NaT, None):
result = self.create_index().insert(1, na)
tm.assert_index_equal(result, expected)
def test_floating_misc(self):
# related 236
# scalar/slicing of a float index
s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64)
# label based slicing
result1 = s[1.0:3.0]
result2 = s.loc[1.0:3.0]
result3 = s.loc[1.0:3.0]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
# exact indexing when found
result1 = s[5.0]
result2 = s.loc[5.0]
result3 = s.loc[5.0]
assert result1 == result2
assert result1 == result3
result1 = s[5]
result2 = s.loc[5]
result3 = s.loc[5]
assert result1 == result2
assert result1 == result3
assert s[5.0] == s[5]
# value not found (and no fallbacking at all)
# scalar integers
pytest.raises(KeyError, lambda: s.loc[4])
pytest.raises(KeyError, lambda: s.loc[4])
pytest.raises(KeyError, lambda: s[4])
# fancy floats/integers create the correct entry (as nan)
# fancy tests
expected = Series([2, 0], index=Float64Index([5.0, 0.0]))
for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float
assert_series_equal(s[fancy_idx], expected)
assert_series_equal(s.loc[fancy_idx], expected)
assert_series_equal(s.loc[fancy_idx], expected)
expected = Series([2, 0], index=Index([5, 0], dtype='int64'))
for fancy_idx in [[5, 0], np.array([5, 0])]: # int
assert_series_equal(s[fancy_idx], expected)
assert_series_equal(s.loc[fancy_idx], expected)
assert_series_equal(s.loc[fancy_idx], expected)
# all should return the same as we are slicing 'the same'
result1 = s.loc[2:5]
result2 = s.loc[2.0:5.0]
result3 = s.loc[2.0:5]
result4 = s.loc[2.1:5]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, result4)
# previously this did fallback indexing
result1 = s[2:5]
result2 = s[2.0:5.0]
result3 = s[2.0:5]
result4 = s[2.1:5]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, result4)
result1 = s.loc[2:5]
result2 = s.loc[2.0:5.0]
result3 = s.loc[2.0:5]
result4 = s.loc[2.1:5]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, result4)
# combined test
result1 = s.loc[2:5]
result2 = s.loc[2:5]
result3 = s[2:5]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
# list selection
result1 = s[[0.0, 5, 10]]
result2 = s.loc[[0.0, 5, 10]]
result3 = s.loc[[0.0, 5, 10]]
result4 = s.iloc[[0, 2, 4]]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, result4)
result1 = s[[1.6, 5, 10]]
result2 = s.loc[[1.6, 5, 10]]
result3 = s.loc[[1.6, 5, 10]]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, Series([np.nan, 2, 4], index=[1.6, 5,
10]))
result1 = s[[0, 1, 2]]
result2 = s.loc[[0, 1, 2]]
result3 = s.loc[[0, 1, 2]]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1,
Series([0.0, np.nan, np.nan], index=[0, 1, 2]))
result1 = s.loc[[2.5, 5]]
result2 = s.loc[[2.5, 5]]
assert_series_equal(result1, result2)
assert_series_equal(result1, Series([1, 2], index=[2.5, 5.0]))
result1 = s[[2.5]]
result2 = s.loc[[2.5]]
result3 = s.loc[[2.5]]
assert_series_equal(result1, result2)
assert_series_equal(result1, result3)
assert_series_equal(result1, Series([1], index=[2.5]))
def index(self, request):
return Float64Index(request.param)
def test_doesnt_contain_all_the_things(self):
i = Float64Index([np.nan])
self.assertFalse(i.isin([0]).item())
self.assertFalse(i.isin([1]).item())
self.assertTrue(i.isin([np.nan]).item())
def mixed_index(self):
return Float64Index([1.5, 2, 3, 4, 5])
class Base:
"""
Common tests for all variations of IntervalIndex construction. Input data
to be supplied in breaks format, then converted by the subclass method
get_kwargs_from_breaks to the expected format.
"""
@pytest.mark.parametrize(
"breaks",
[
[3, 14, 15, 92, 653],
np.arange(10, dtype="int64"),
Int64Index(range(-10, 11)),
Float64Index(np.arange(20, 30, 0.5)),
date_range("20180101", periods=10),
date_range("20180101", periods=10, tz="US/Eastern"),
timedelta_range("1 day", periods=10),
],
)
def test_constructor(self, constructor, breaks, closed, name):
result_kwargs = self.get_kwargs_from_breaks(breaks, closed)
result = constructor(closed=closed, name=name, **result_kwargs)
assert result.closed == closed
assert result.name == name
assert result.dtype.subtype == getattr(breaks, "dtype", "int64")
tm.assert_index_equal(result.left, Index(breaks[:-1]))
tm.assert_index_equal(result.right, Index(breaks[1:]))
@pytest.mark.parametrize(
"breaks, subtype",
[
(Int64Index([0, 1, 2, 3, 4]), "float64"),
(Int64Index([0, 1, 2, 3, 4]), "datetime64[ns]"),
(Int64Index([0, 1, 2, 3, 4]), "timedelta64[ns]"),
(Float64Index([0, 1, 2, 3, 4]), "int64"),
(date_range("2017-01-01", periods=5), "int64"),
(timedelta_range("1 day", periods=5), "int64"),
],
)
def test_constructor_dtype(self, constructor, breaks, subtype):
# GH 19262: conversion via dtype parameter
expected_kwargs = self.get_kwargs_from_breaks(breaks.astype(subtype))
expected = constructor(**expected_kwargs)
result_kwargs = self.get_kwargs_from_breaks(breaks)
iv_dtype = IntervalDtype(subtype)
for dtype in (iv_dtype, str(iv_dtype)):
result = constructor(dtype=dtype, **result_kwargs)
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("breaks",
[[np.nan] * 2, [np.nan] * 4, [np.nan] * 50])
def test_constructor_nan(self, constructor, breaks, closed):
# GH 18421
result_kwargs = self.get_kwargs_from_breaks(breaks)
result = constructor(closed=closed, **result_kwargs)
expected_subtype = np.float64
expected_values = np.array(breaks[:-1], dtype=object)
assert result.closed == closed
assert result.dtype.subtype == expected_subtype
tm.assert_numpy_array_equal(np.array(result), expected_values)
@pytest.mark.parametrize(
"breaks",
[
[],
np.array([], dtype="int64"),
np.array([], dtype="float64"),
np.array([], dtype="datetime64[ns]"),
np.array([], dtype="timedelta64[ns]"),
],
)
def test_constructor_empty(self, constructor, breaks, closed):
# GH 18421
result_kwargs = self.get_kwargs_from_breaks(breaks)
result = constructor(closed=closed, **result_kwargs)
expected_values = np.array([], dtype=object)
expected_subtype = getattr(breaks, "dtype", np.int64)
assert result.empty
assert result.closed == closed
assert result.dtype.subtype == expected_subtype
tm.assert_numpy_array_equal(np.array(result), expected_values)
@pytest.mark.parametrize(
"breaks",
[
tuple("0123456789"),
list("abcdefghij"),
np.array(list("abcdefghij"), dtype=object),
np.array(list("abcdefghij"), dtype="<U1"),
],
)
def test_constructor_string(self, constructor, breaks):
# GH 19016
msg = ("category, object, and string subtypes are not supported "
"for IntervalIndex")
with pytest.raises(TypeError, match=msg):
constructor(**self.get_kwargs_from_breaks(breaks))
@pytest.mark.parametrize("cat_constructor",
[Categorical, CategoricalIndex])
def test_constructor_categorical_valid(self, constructor, cat_constructor):
# GH 21243/21253
if isinstance(constructor, partial) and constructor.func is Index:
# Index is defined to create CategoricalIndex from categorical data
pytest.skip()
breaks = np.arange(10, dtype="int64")
expected = IntervalIndex.from_breaks(breaks)
cat_breaks = cat_constructor(breaks)
result_kwargs = self.get_kwargs_from_breaks(cat_breaks)
result = constructor(**result_kwargs)
tm.assert_index_equal(result, expected)
def test_generic_errors(self, constructor):
# filler input data to be used when supplying invalid kwargs
filler = self.get_kwargs_from_breaks(range(10))
# invalid closed
msg = "invalid option for 'closed': invalid"
with pytest.raises(ValueError, match=msg):
constructor(closed="invalid", **filler)
# unsupported dtype
msg = "dtype must be an IntervalDtype, got int64"
with pytest.raises(TypeError, match=msg):
constructor(dtype="int64", **filler)
# invalid dtype
msg = "data type [\"']invalid[\"'] not understood"
with pytest.raises(TypeError, match=msg):
constructor(dtype="invalid", **filler)
# no point in nesting periods in an IntervalIndex
periods = period_range("2000-01-01", periods=10)
periods_kwargs = self.get_kwargs_from_breaks(periods)
msg = "Period dtypes are not supported, use a PeriodIndex instead"
with pytest.raises(ValueError, match=msg):
constructor(**periods_kwargs)
# decreasing values
decreasing_kwargs = self.get_kwargs_from_breaks(range(10, -1, -1))
msg = "left side of interval must be <= right side"
with pytest.raises(ValueError, match=msg):
constructor(**decreasing_kwargs)
def float_index(self):
return Float64Index([0.0, 2.5, 5.0, 7.5, 10.0])
def setUp(self):
self.indices = dict(mixed=Float64Index([1.5, 2, 3, 4, 5]),
float=Float64Index(np.arange(5) * 2.5))
self.setup_indices()
def create_index(self) -> Float64Index:
return Float64Index(np.arange(5, dtype="float64"))
def test_contains_nans(self):
i = Float64Index([1.0, 2.0, np.nan])
self.assertTrue(np.nan in i)
Examples
--------
>>> arr = RangeIndex(5)
>>> arr / zeros
Float64Index([nan, inf, inf, inf, inf], dtype='float64')
"""
return request.param
# ------------------------------------------------------------------
# Vector Fixtures
@pytest.fixture(
params=[
Float64Index(np.arange(5, dtype="float64")),
Int64Index(np.arange(5, dtype="int64")),
UInt64Index(np.arange(5, dtype="uint64")),
RangeIndex(5),
],
ids=lambda x: type(x).__name__,
)
def numeric_idx(request):
"""
Several types of numeric-dtypes Index objects
"""
return request.param
# ------------------------------------------------------------------
# Scalar Fixtures
