def setUp(self):
self.indices = dict(index=UInt64Index(
[2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]))
self.setup_indices()
def create_index(self):
return UInt64Index(np.arange(5, dtype='uint64'))
def create_index(self):
# compat with shared Int64/Float64 tests; use index_large for UInt64 only tests
return UInt64Index(np.arange(5, dtype="uint64"))
def setup_method(self, method):
vals = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
self.indices = dict(index=UInt64Index(vals),
index_dec=UInt64Index(reversed(vals)))
self.setup_indices()
def setup_method(self, method):
self.indices = dict(index=UInt64Index(
[2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]))
self.setup_indices()
def test_constructor_does_not_cast_to_float(self):
# https://github.com/numpy/numpy/issues/19146
values = [0, np.iinfo(np.uint64).max]
result = UInt64Index(values)
assert list(result) == values
class TestContains:
@pytest.mark.parametrize(
"index,val",
[
(Index([0, 1, 2]), 2),
(Index([0, 1, "2"]), "2"),
(Index([0, 1, 2, np.inf, 4]), 4),
(Index([0, 1, 2, np.nan, 4]), 4),
(Index([0, 1, 2, np.inf]), np.inf),
(Index([0, 1, 2, np.nan]), np.nan),
],
)
def test_index_contains(self, index, val):
assert val in index
@pytest.mark.parametrize(
"index,val",
[
(Index([0, 1, 2]), "2"),
(Index([0, 1, "2"]), 2),
(Index([0, 1, 2, np.inf]), 4),
(Index([0, 1, 2, np.nan]), 4),
(Index([0, 1, 2, np.inf]), np.nan),
(Index([0, 1, 2, np.nan]), np.inf),
# Checking if np.inf in Int64Index should not cause an OverflowError
# Related to GH 16957
(Int64Index([0, 1, 2]), np.inf),
(Int64Index([0, 1, 2]), np.nan),
(UInt64Index([0, 1, 2]), np.inf),
(UInt64Index([0, 1, 2]), np.nan),
],
)
def test_index_not_contains(self, index, val):
assert val not in index
@pytest.mark.parametrize("index,val", [(Index([0, 1, "2"]), 0),
(Index([0, 1, "2"]), "2")])
def test_mixed_index_contains(self, index, val):
# GH#19860
assert val in index
@pytest.mark.parametrize("index,val", [(Index([0, 1, "2"]), "1"),
(Index([0, 1, "2"]), 2)])
def test_mixed_index_not_contains(self, index, val):
# GH#19860
assert val not in index
def test_contains_with_float_index(self):
# GH#22085
integer_index = Int64Index([0, 1, 2, 3])
uinteger_index = UInt64Index([0, 1, 2, 3])
float_index = Float64Index([0.1, 1.1, 2.2, 3.3])
for index in (integer_index, uinteger_index):
assert 1.1 not in index
assert 1.0 in index
assert 1 in index
assert 1.1 in float_index
assert 1.0 not in float_index
assert 1 not in float_index
def test_astype_uint(self):
arr = period_range("2000", periods=2, name="idx")
expected = UInt64Index(np.array([10957, 10958], dtype="uint64"),
name="idx")
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
def setUp(self):
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_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 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()
# 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.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 create_index(self) -> UInt64Index:
# compat with shared Int64/Float64 tests
return UInt64Index(np.arange(5, dtype="uint64"))
def makeUIntIndex(k=10, name=None):
base_idx = makeNumericIndex(k, name=name, dtype="uint64")
return UInt64Index(base_idx)
def indices(self, request):
return UInt64Index(request.param)
def test_astype_column_metadata(self, dtype):
# GH#19920
columns = UInt64Index([100, 200, 300], name="foo")
df = DataFrame(np.arange(15).reshape(5, 3), columns=columns)
df = df.astype(dtype)
tm.assert_index_equal(df.columns, columns)
def index_large(self):
# large values used in TestUInt64Index where no compat needed with Int64/Float64
large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
return UInt64Index(large)
>>> 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
