239 lines
8.5 KiB
Python
239 lines
8.5 KiB
Python
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import re
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import numpy as np
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import pytest
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from pandas._libs import algos as libalgos, index as libindex
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import pandas as pd
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import pandas._testing as tm
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@pytest.fixture(
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params=[
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(libindex.Int64Engine, np.int64),
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(libindex.Int32Engine, np.int32),
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(libindex.Int16Engine, np.int16),
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(libindex.Int8Engine, np.int8),
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(libindex.UInt64Engine, np.uint64),
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(libindex.UInt32Engine, np.uint32),
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(libindex.UInt16Engine, np.uint16),
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(libindex.UInt8Engine, np.uint8),
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(libindex.Float64Engine, np.float64),
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(libindex.Float32Engine, np.float32),
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],
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ids=lambda x: x[0].__name__,
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)
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def numeric_indexing_engine_type_and_dtype(request):
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return request.param
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class TestDatetimeEngine:
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@pytest.mark.parametrize(
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"scalar",
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[
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pd.Timedelta(pd.Timestamp("2016-01-01").asm8.view("m8[ns]")),
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pd.Timestamp("2016-01-01").value,
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pd.Timestamp("2016-01-01").to_pydatetime(),
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pd.Timestamp("2016-01-01").to_datetime64(),
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],
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)
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def test_not_contains_requires_timestamp(self, scalar):
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dti1 = pd.date_range("2016-01-01", periods=3)
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dti2 = dti1.insert(1, pd.NaT) # non-monotonic
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dti3 = dti1.insert(3, dti1[0]) # non-unique
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dti4 = pd.date_range("2016-01-01", freq="ns", periods=2_000_000)
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dti5 = dti4.insert(0, dti4[0]) # over size threshold, not unique
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msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
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for dti in [dti1, dti2, dti3, dti4, dti5]:
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with pytest.raises(TypeError, match=msg):
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scalar in dti._engine
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with pytest.raises(KeyError, match=msg):
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dti._engine.get_loc(scalar)
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class TestTimedeltaEngine:
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@pytest.mark.parametrize(
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"scalar",
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[
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pd.Timestamp(pd.Timedelta(days=42).asm8.view("datetime64[ns]")),
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pd.Timedelta(days=42).value,
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pd.Timedelta(days=42).to_pytimedelta(),
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pd.Timedelta(days=42).to_timedelta64(),
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],
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)
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def test_not_contains_requires_timestamp(self, scalar):
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tdi1 = pd.timedelta_range("42 days", freq="9h", periods=1234)
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tdi2 = tdi1.insert(1, pd.NaT) # non-monotonic
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tdi3 = tdi1.insert(3, tdi1[0]) # non-unique
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tdi4 = pd.timedelta_range("42 days", freq="ns", periods=2_000_000)
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tdi5 = tdi4.insert(0, tdi4[0]) # over size threshold, not unique
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msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
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for tdi in [tdi1, tdi2, tdi3, tdi4, tdi5]:
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with pytest.raises(TypeError, match=msg):
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scalar in tdi._engine
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with pytest.raises(KeyError, match=msg):
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tdi._engine.get_loc(scalar)
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class TestNumericEngine:
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def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
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engine_type, dtype = numeric_indexing_engine_type_and_dtype
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num = 1000
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arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
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# monotonic increasing
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engine = engine_type(lambda: arr, len(arr))
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assert engine.is_monotonic_increasing is True
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assert engine.is_monotonic_decreasing is False
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# monotonic decreasing
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engine = engine_type(lambda: arr[::-1], len(arr))
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assert engine.is_monotonic_increasing is False
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assert engine.is_monotonic_decreasing is True
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# neither monotonic increasing or decreasing
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arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
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engine = engine_type(lambda: arr[::-1], len(arr))
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assert engine.is_monotonic_increasing is False
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assert engine.is_monotonic_decreasing is False
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def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
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engine_type, dtype = numeric_indexing_engine_type_and_dtype
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# unique
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arr = np.array([1, 3, 2], dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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assert engine.is_unique is True
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# not unique
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arr = np.array([1, 2, 1], dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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assert engine.is_unique is False
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def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
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engine_type, dtype = numeric_indexing_engine_type_and_dtype
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# unique
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arr = np.array([1, 2, 3], dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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assert engine.get_loc(2) == 1
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# monotonic
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num = 1000
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arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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assert engine.get_loc(2) == slice(1000, 2000)
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# not monotonic
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arr = np.array([1, 2, 3] * num, dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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expected = np.array([False, True, False] * num, dtype=bool)
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result = engine.get_loc(2)
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assert (result == expected).all()
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def test_get_backfill_indexer(self, numeric_indexing_engine_type_and_dtype):
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engine_type, dtype = numeric_indexing_engine_type_and_dtype
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arr = np.array([1, 5, 10], dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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new = np.arange(12, dtype=dtype)
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result = engine.get_backfill_indexer(new)
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expected = libalgos.backfill(arr, new)
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tm.assert_numpy_array_equal(result, expected)
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def test_get_pad_indexer(self, numeric_indexing_engine_type_and_dtype):
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engine_type, dtype = numeric_indexing_engine_type_and_dtype
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arr = np.array([1, 5, 10], dtype=dtype)
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engine = engine_type(lambda: arr, len(arr))
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new = np.arange(12, dtype=dtype)
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result = engine.get_pad_indexer(new)
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expected = libalgos.pad(arr, new)
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tm.assert_numpy_array_equal(result, expected)
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class TestObjectEngine:
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engine_type = libindex.ObjectEngine
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dtype = np.object_
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values = list("abc")
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def test_is_monotonic(self):
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num = 1000
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arr = np.array(["a"] * num + ["a"] * num + ["c"] * num, dtype=self.dtype)
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# monotonic increasing
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engine = self.engine_type(lambda: arr, len(arr))
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assert engine.is_monotonic_increasing is True
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assert engine.is_monotonic_decreasing is False
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# monotonic decreasing
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engine = self.engine_type(lambda: arr[::-1], len(arr))
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assert engine.is_monotonic_increasing is False
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assert engine.is_monotonic_decreasing is True
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# neither monotonic increasing or decreasing
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arr = np.array(["a"] * num + ["b"] * num + ["a"] * num, dtype=self.dtype)
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engine = self.engine_type(lambda: arr[::-1], len(arr))
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assert engine.is_monotonic_increasing is False
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assert engine.is_monotonic_decreasing is False
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def test_is_unique(self):
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# unique
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arr = np.array(self.values, dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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assert engine.is_unique is True
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# not unique
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arr = np.array(["a", "b", "a"], dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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assert engine.is_unique is False
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def test_get_loc(self):
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# unique
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arr = np.array(self.values, dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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assert engine.get_loc("b") == 1
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# monotonic
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num = 1000
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arr = np.array(["a"] * num + ["b"] * num + ["c"] * num, dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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assert engine.get_loc("b") == slice(1000, 2000)
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# not monotonic
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arr = np.array(self.values * num, dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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expected = np.array([False, True, False] * num, dtype=bool)
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result = engine.get_loc("b")
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assert (result == expected).all()
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def test_get_backfill_indexer(self):
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arr = np.array(["a", "e", "j"], dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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new = np.array(list("abcdefghij"), dtype=self.dtype)
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result = engine.get_backfill_indexer(new)
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expected = libalgos.backfill["object"](arr, new)
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tm.assert_numpy_array_equal(result, expected)
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def test_get_pad_indexer(self):
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arr = np.array(["a", "e", "j"], dtype=self.dtype)
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engine = self.engine_type(lambda: arr, len(arr))
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new = np.array(list("abcdefghij"), dtype=self.dtype)
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result = engine.get_pad_indexer(new)
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expected = libalgos.pad["object"](arr, new)
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tm.assert_numpy_array_equal(result, expected)
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