193 lines
6.5 KiB
Python
193 lines
6.5 KiB
Python
import re
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas._libs import index as libindex
|
|
|
|
import pandas as pd
|
|
|
|
|
|
@pytest.fixture(
|
|
params=[
|
|
(libindex.Int64Engine, np.int64),
|
|
(libindex.Int32Engine, np.int32),
|
|
(libindex.Int16Engine, np.int16),
|
|
(libindex.Int8Engine, np.int8),
|
|
(libindex.UInt64Engine, np.uint64),
|
|
(libindex.UInt32Engine, np.uint32),
|
|
(libindex.UInt16Engine, np.uint16),
|
|
(libindex.UInt8Engine, np.uint8),
|
|
(libindex.Float64Engine, np.float64),
|
|
(libindex.Float32Engine, np.float32),
|
|
],
|
|
ids=lambda x: x[0].__name__,
|
|
)
|
|
def numeric_indexing_engine_type_and_dtype(request):
|
|
return request.param
|
|
|
|
|
|
class TestDatetimeEngine:
|
|
@pytest.mark.parametrize(
|
|
"scalar",
|
|
[
|
|
pd.Timedelta(pd.Timestamp("2016-01-01").asm8.view("m8[ns]")),
|
|
pd.Timestamp("2016-01-01")._value,
|
|
pd.Timestamp("2016-01-01").to_pydatetime(),
|
|
pd.Timestamp("2016-01-01").to_datetime64(),
|
|
],
|
|
)
|
|
def test_not_contains_requires_timestamp(self, scalar):
|
|
dti1 = pd.date_range("2016-01-01", periods=3)
|
|
dti2 = dti1.insert(1, pd.NaT) # non-monotonic
|
|
dti3 = dti1.insert(3, dti1[0]) # non-unique
|
|
dti4 = pd.date_range("2016-01-01", freq="ns", periods=2_000_000)
|
|
dti5 = dti4.insert(0, dti4[0]) # over size threshold, not unique
|
|
|
|
msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
|
|
for dti in [dti1, dti2, dti3, dti4, dti5]:
|
|
with pytest.raises(TypeError, match=msg):
|
|
scalar in dti._engine
|
|
|
|
with pytest.raises(KeyError, match=msg):
|
|
dti._engine.get_loc(scalar)
|
|
|
|
|
|
class TestTimedeltaEngine:
|
|
@pytest.mark.parametrize(
|
|
"scalar",
|
|
[
|
|
pd.Timestamp(pd.Timedelta(days=42).asm8.view("datetime64[ns]")),
|
|
pd.Timedelta(days=42)._value,
|
|
pd.Timedelta(days=42).to_pytimedelta(),
|
|
pd.Timedelta(days=42).to_timedelta64(),
|
|
],
|
|
)
|
|
def test_not_contains_requires_timedelta(self, scalar):
|
|
tdi1 = pd.timedelta_range("42 days", freq="9h", periods=1234)
|
|
tdi2 = tdi1.insert(1, pd.NaT) # non-monotonic
|
|
tdi3 = tdi1.insert(3, tdi1[0]) # non-unique
|
|
tdi4 = pd.timedelta_range("42 days", freq="ns", periods=2_000_000)
|
|
tdi5 = tdi4.insert(0, tdi4[0]) # over size threshold, not unique
|
|
|
|
msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
|
|
for tdi in [tdi1, tdi2, tdi3, tdi4, tdi5]:
|
|
with pytest.raises(TypeError, match=msg):
|
|
scalar in tdi._engine
|
|
|
|
with pytest.raises(KeyError, match=msg):
|
|
tdi._engine.get_loc(scalar)
|
|
|
|
|
|
class TestNumericEngine:
|
|
def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
|
|
engine_type, dtype = numeric_indexing_engine_type_and_dtype
|
|
num = 1000
|
|
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
|
|
|
|
# monotonic increasing
|
|
engine = engine_type(arr)
|
|
assert engine.is_monotonic_increasing is True
|
|
assert engine.is_monotonic_decreasing is False
|
|
|
|
# monotonic decreasing
|
|
engine = engine_type(arr[::-1])
|
|
assert engine.is_monotonic_increasing is False
|
|
assert engine.is_monotonic_decreasing is True
|
|
|
|
# neither monotonic increasing or decreasing
|
|
arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
|
|
engine = engine_type(arr[::-1])
|
|
assert engine.is_monotonic_increasing is False
|
|
assert engine.is_monotonic_decreasing is False
|
|
|
|
def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
|
|
engine_type, dtype = numeric_indexing_engine_type_and_dtype
|
|
|
|
# unique
|
|
arr = np.array([1, 3, 2], dtype=dtype)
|
|
engine = engine_type(arr)
|
|
assert engine.is_unique is True
|
|
|
|
# not unique
|
|
arr = np.array([1, 2, 1], dtype=dtype)
|
|
engine = engine_type(arr)
|
|
assert engine.is_unique is False
|
|
|
|
def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
|
|
engine_type, dtype = numeric_indexing_engine_type_and_dtype
|
|
|
|
# unique
|
|
arr = np.array([1, 2, 3], dtype=dtype)
|
|
engine = engine_type(arr)
|
|
assert engine.get_loc(2) == 1
|
|
|
|
# monotonic
|
|
num = 1000
|
|
arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
|
|
engine = engine_type(arr)
|
|
assert engine.get_loc(2) == slice(1000, 2000)
|
|
|
|
# not monotonic
|
|
arr = np.array([1, 2, 3] * num, dtype=dtype)
|
|
engine = engine_type(arr)
|
|
expected = np.array([False, True, False] * num, dtype=bool)
|
|
result = engine.get_loc(2)
|
|
assert (result == expected).all()
|
|
|
|
|
|
class TestObjectEngine:
|
|
engine_type = libindex.ObjectEngine
|
|
dtype = np.object_
|
|
values = list("abc")
|
|
|
|
def test_is_monotonic(self):
|
|
num = 1000
|
|
arr = np.array(["a"] * num + ["a"] * num + ["c"] * num, dtype=self.dtype)
|
|
|
|
# monotonic increasing
|
|
engine = self.engine_type(arr)
|
|
assert engine.is_monotonic_increasing is True
|
|
assert engine.is_monotonic_decreasing is False
|
|
|
|
# monotonic decreasing
|
|
engine = self.engine_type(arr[::-1])
|
|
assert engine.is_monotonic_increasing is False
|
|
assert engine.is_monotonic_decreasing is True
|
|
|
|
# neither monotonic increasing or decreasing
|
|
arr = np.array(["a"] * num + ["b"] * num + ["a"] * num, dtype=self.dtype)
|
|
engine = self.engine_type(arr[::-1])
|
|
assert engine.is_monotonic_increasing is False
|
|
assert engine.is_monotonic_decreasing is False
|
|
|
|
def test_is_unique(self):
|
|
# unique
|
|
arr = np.array(self.values, dtype=self.dtype)
|
|
engine = self.engine_type(arr)
|
|
assert engine.is_unique is True
|
|
|
|
# not unique
|
|
arr = np.array(["a", "b", "a"], dtype=self.dtype)
|
|
engine = self.engine_type(arr)
|
|
assert engine.is_unique is False
|
|
|
|
def test_get_loc(self):
|
|
# unique
|
|
arr = np.array(self.values, dtype=self.dtype)
|
|
engine = self.engine_type(arr)
|
|
assert engine.get_loc("b") == 1
|
|
|
|
# monotonic
|
|
num = 1000
|
|
arr = np.array(["a"] * num + ["b"] * num + ["c"] * num, dtype=self.dtype)
|
|
engine = self.engine_type(arr)
|
|
assert engine.get_loc("b") == slice(1000, 2000)
|
|
|
|
# not monotonic
|
|
arr = np.array(self.values * num, dtype=self.dtype)
|
|
engine = self.engine_type(arr)
|
|
expected = np.array([False, True, False] * num, dtype=bool)
|
|
result = engine.get_loc("b")
|
|
assert (result == expected).all()
|