Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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689 lines
24 KiB
689 lines
24 KiB
from datetime import timedelta
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import operator
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import numpy as np
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import pytest
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import pytz
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from pandas._libs.tslibs import IncompatibleFrequency
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import pandas as pd
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from pandas import Categorical, Index, Series, bdate_range, date_range, isna
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import pandas._testing as tm
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from pandas.core import nanops, ops
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def _permute(obj):
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return obj.take(np.random.permutation(len(obj)))
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class TestSeriesFlexArithmetic:
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@pytest.mark.parametrize(
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"ts",
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[
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(lambda x: x, lambda x: x * 2, False),
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(lambda x: x, lambda x: x[::2], False),
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(lambda x: x, lambda x: 5, True),
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(lambda x: tm.makeFloatSeries(), lambda x: tm.makeFloatSeries(), True),
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],
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)
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@pytest.mark.parametrize(
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"opname", ["add", "sub", "mul", "floordiv", "truediv", "pow"]
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)
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def test_flex_method_equivalence(self, opname, ts):
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# check that Series.{opname} behaves like Series.__{opname}__,
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tser = tm.makeTimeSeries().rename("ts")
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series = ts[0](tser)
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other = ts[1](tser)
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check_reverse = ts[2]
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op = getattr(Series, opname)
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alt = getattr(operator, opname)
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result = op(series, other)
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expected = alt(series, other)
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tm.assert_almost_equal(result, expected)
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if check_reverse:
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rop = getattr(Series, "r" + opname)
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result = rop(series, other)
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expected = alt(other, series)
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tm.assert_almost_equal(result, expected)
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def test_flex_method_subclass_metadata_preservation(self, all_arithmetic_operators):
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# GH 13208
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class MySeries(Series):
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_metadata = ["x"]
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@property
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def _constructor(self):
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return MySeries
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opname = all_arithmetic_operators
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op = getattr(Series, opname)
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m = MySeries([1, 2, 3], name="test")
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m.x = 42
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result = op(m, 1)
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assert result.x == 42
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def test_flex_add_scalar_fill_value(self):
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# GH12723
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s = Series([0, 1, np.nan, 3, 4, 5])
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exp = s.fillna(0).add(2)
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res = s.add(2, fill_value=0)
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tm.assert_series_equal(res, exp)
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pairings = [(Series.div, operator.truediv, 1), (Series.rdiv, ops.rtruediv, 1)]
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for op in ["add", "sub", "mul", "pow", "truediv", "floordiv"]:
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fv = 0
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lop = getattr(Series, op)
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lequiv = getattr(operator, op)
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rop = getattr(Series, "r" + op)
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# bind op at definition time...
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requiv = lambda x, y, op=op: getattr(operator, op)(y, x)
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pairings.append((lop, lequiv, fv))
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pairings.append((rop, requiv, fv))
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@pytest.mark.parametrize("op, equiv_op, fv", pairings)
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def test_operators_combine(self, op, equiv_op, fv):
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def _check_fill(meth, op, a, b, fill_value=0):
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exp_index = a.index.union(b.index)
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a = a.reindex(exp_index)
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b = b.reindex(exp_index)
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amask = isna(a)
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bmask = isna(b)
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exp_values = []
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for i in range(len(exp_index)):
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with np.errstate(all="ignore"):
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if amask[i]:
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if bmask[i]:
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exp_values.append(np.nan)
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continue
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exp_values.append(op(fill_value, b[i]))
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elif bmask[i]:
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if amask[i]:
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exp_values.append(np.nan)
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continue
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exp_values.append(op(a[i], fill_value))
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else:
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exp_values.append(op(a[i], b[i]))
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result = meth(a, b, fill_value=fill_value)
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expected = Series(exp_values, exp_index)
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tm.assert_series_equal(result, expected)
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a = Series([np.nan, 1.0, 2.0, 3.0, np.nan], index=np.arange(5))
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b = Series([np.nan, 1, np.nan, 3, np.nan, 4.0], index=np.arange(6))
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result = op(a, b)
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exp = equiv_op(a, b)
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tm.assert_series_equal(result, exp)
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_check_fill(op, equiv_op, a, b, fill_value=fv)
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# should accept axis=0 or axis='rows'
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op(a, b, axis=0)
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class TestSeriesArithmetic:
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# Some of these may end up in tests/arithmetic, but are not yet sorted
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def test_add_series_with_period_index(self):
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rng = pd.period_range("1/1/2000", "1/1/2010", freq="A")
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ts = Series(np.random.randn(len(rng)), index=rng)
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result = ts + ts[::2]
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expected = ts + ts
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expected.iloc[1::2] = np.nan
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tm.assert_series_equal(result, expected)
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result = ts + _permute(ts[::2])
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tm.assert_series_equal(result, expected)
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msg = "Input has different freq=D from PeriodIndex\\(freq=A-DEC\\)"
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with pytest.raises(IncompatibleFrequency, match=msg):
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ts + ts.asfreq("D", how="end")
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@pytest.mark.parametrize(
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"target_add,input_value,expected_value",
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[
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("!", ["hello", "world"], ["hello!", "world!"]),
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("m", ["hello", "world"], ["hellom", "worldm"]),
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],
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)
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def test_string_addition(self, target_add, input_value, expected_value):
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# GH28658 - ensure adding 'm' does not raise an error
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a = Series(input_value)
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result = a + target_add
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expected = Series(expected_value)
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tm.assert_series_equal(result, expected)
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def test_divmod(self):
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# GH#25557
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a = Series([1, 1, 1, np.nan], index=["a", "b", "c", "d"])
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b = Series([2, np.nan, 1, np.nan], index=["a", "b", "d", "e"])
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result = a.divmod(b)
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expected = divmod(a, b)
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tm.assert_series_equal(result[0], expected[0])
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tm.assert_series_equal(result[1], expected[1])
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result = a.rdivmod(b)
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expected = divmod(b, a)
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tm.assert_series_equal(result[0], expected[0])
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tm.assert_series_equal(result[1], expected[1])
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@pytest.mark.parametrize("index", [None, range(9)])
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def test_series_integer_mod(self, index):
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# GH#24396
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s1 = Series(range(1, 10))
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s2 = Series("foo", index=index)
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msg = "not all arguments converted during string formatting"
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with pytest.raises(TypeError, match=msg):
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s2 % s1
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def test_add_with_duplicate_index(self):
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# GH14227
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s1 = Series([1, 2], index=[1, 1])
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s2 = Series([10, 10], index=[1, 2])
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result = s1 + s2
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expected = pd.Series([11, 12, np.nan], index=[1, 1, 2])
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tm.assert_series_equal(result, expected)
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def test_add_na_handling(self):
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from datetime import date
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from decimal import Decimal
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s = Series(
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[Decimal("1.3"), Decimal("2.3")], index=[date(2012, 1, 1), date(2012, 1, 2)]
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)
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result = s + s.shift(1)
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result2 = s.shift(1) + s
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assert isna(result[0])
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assert isna(result2[0])
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def test_add_corner_cases(self, datetime_series):
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empty = Series([], index=Index([]), dtype=np.float64)
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result = datetime_series + empty
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assert np.isnan(result).all()
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result = empty + empty.copy()
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assert len(result) == 0
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# FIXME: dont leave commented-out
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# TODO: this returned NotImplemented earlier, what to do?
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# deltas = Series([timedelta(1)] * 5, index=np.arange(5))
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# sub_deltas = deltas[::2]
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# deltas5 = deltas * 5
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# deltas = deltas + sub_deltas
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# float + int
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int_ts = datetime_series.astype(int)[:-5]
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added = datetime_series + int_ts
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expected = Series(
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datetime_series.values[:-5] + int_ts.values,
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index=datetime_series.index[:-5],
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name="ts",
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)
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tm.assert_series_equal(added[:-5], expected)
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def test_mul_empty_int_corner_case(self):
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s1 = Series([], [], dtype=np.int32)
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s2 = Series({"x": 0.0})
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tm.assert_series_equal(s1 * s2, Series([np.nan], index=["x"]))
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def test_sub_datetimelike_align(self):
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# GH#7500
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# datetimelike ops need to align
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dt = Series(date_range("2012-1-1", periods=3, freq="D"))
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dt.iloc[2] = np.nan
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dt2 = dt[::-1]
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expected = Series([timedelta(0), timedelta(0), pd.NaT])
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# name is reset
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result = dt2 - dt
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tm.assert_series_equal(result, expected)
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expected = Series(expected, name=0)
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result = (dt2.to_frame() - dt.to_frame())[0]
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tm.assert_series_equal(result, expected)
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# ------------------------------------------------------------------
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# Comparisons
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class TestSeriesFlexComparison:
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def test_comparison_flex_basic(self):
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left = pd.Series(np.random.randn(10))
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right = pd.Series(np.random.randn(10))
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tm.assert_series_equal(left.eq(right), left == right)
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tm.assert_series_equal(left.ne(right), left != right)
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tm.assert_series_equal(left.le(right), left < right)
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tm.assert_series_equal(left.lt(right), left <= right)
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tm.assert_series_equal(left.gt(right), left > right)
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tm.assert_series_equal(left.ge(right), left >= right)
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# axis
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for axis in [0, None, "index"]:
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tm.assert_series_equal(left.eq(right, axis=axis), left == right)
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tm.assert_series_equal(left.ne(right, axis=axis), left != right)
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tm.assert_series_equal(left.le(right, axis=axis), left < right)
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tm.assert_series_equal(left.lt(right, axis=axis), left <= right)
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tm.assert_series_equal(left.gt(right, axis=axis), left > right)
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tm.assert_series_equal(left.ge(right, axis=axis), left >= right)
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#
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msg = "No axis named 1 for object type"
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for op in ["eq", "ne", "le", "le", "gt", "ge"]:
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with pytest.raises(ValueError, match=msg):
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getattr(left, op)(right, axis=1)
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def test_comparison_flex_alignment(self):
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left = Series([1, 3, 2], index=list("abc"))
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right = Series([2, 2, 2], index=list("bcd"))
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exp = pd.Series([False, False, True, False], index=list("abcd"))
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tm.assert_series_equal(left.eq(right), exp)
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exp = pd.Series([True, True, False, True], index=list("abcd"))
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tm.assert_series_equal(left.ne(right), exp)
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exp = pd.Series([False, False, True, False], index=list("abcd"))
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tm.assert_series_equal(left.le(right), exp)
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exp = pd.Series([False, False, False, False], index=list("abcd"))
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tm.assert_series_equal(left.lt(right), exp)
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exp = pd.Series([False, True, True, False], index=list("abcd"))
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tm.assert_series_equal(left.ge(right), exp)
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exp = pd.Series([False, True, False, False], index=list("abcd"))
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tm.assert_series_equal(left.gt(right), exp)
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def test_comparison_flex_alignment_fill(self):
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left = Series([1, 3, 2], index=list("abc"))
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right = Series([2, 2, 2], index=list("bcd"))
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exp = pd.Series([False, False, True, True], index=list("abcd"))
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tm.assert_series_equal(left.eq(right, fill_value=2), exp)
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exp = pd.Series([True, True, False, False], index=list("abcd"))
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tm.assert_series_equal(left.ne(right, fill_value=2), exp)
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exp = pd.Series([False, False, True, True], index=list("abcd"))
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tm.assert_series_equal(left.le(right, fill_value=0), exp)
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exp = pd.Series([False, False, False, True], index=list("abcd"))
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tm.assert_series_equal(left.lt(right, fill_value=0), exp)
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exp = pd.Series([True, True, True, False], index=list("abcd"))
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tm.assert_series_equal(left.ge(right, fill_value=0), exp)
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exp = pd.Series([True, True, False, False], index=list("abcd"))
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tm.assert_series_equal(left.gt(right, fill_value=0), exp)
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class TestSeriesComparison:
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def test_comparison_different_length(self):
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a = Series(["a", "b", "c"])
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b = Series(["b", "a"])
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msg = "only compare identically-labeled Series"
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with pytest.raises(ValueError, match=msg):
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a < b
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a = Series([1, 2])
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b = Series([2, 3, 4])
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with pytest.raises(ValueError, match=msg):
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a == b
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@pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
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def test_ser_flex_cmp_return_dtypes(self, opname):
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# GH#15115
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ser = Series([1, 3, 2], index=range(3))
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const = 2
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result = getattr(ser, opname)(const).dtypes
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expected = np.dtype("bool")
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assert result == expected
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@pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
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def test_ser_flex_cmp_return_dtypes_empty(self, opname):
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# GH#15115 empty Series case
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ser = Series([1, 3, 2], index=range(3))
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empty = ser.iloc[:0]
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const = 2
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result = getattr(empty, opname)(const).dtypes
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expected = np.dtype("bool")
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assert result == expected
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@pytest.mark.parametrize(
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"op",
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[operator.eq, operator.ne, operator.le, operator.lt, operator.ge, operator.gt],
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)
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@pytest.mark.parametrize(
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"names", [(None, None, None), ("foo", "bar", None), ("baz", "baz", "baz")]
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)
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def test_ser_cmp_result_names(self, names, op):
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# datetime64 dtype
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dti = pd.date_range("1949-06-07 03:00:00", freq="H", periods=5, name=names[0])
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ser = Series(dti).rename(names[1])
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result = op(ser, dti)
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assert result.name == names[2]
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# datetime64tz dtype
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dti = dti.tz_localize("US/Central")
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dti = pd.DatetimeIndex(dti, freq="infer") # freq not preserved by tz_localize
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ser = Series(dti).rename(names[1])
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result = op(ser, dti)
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assert result.name == names[2]
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# timedelta64 dtype
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tdi = dti - dti.shift(1)
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ser = Series(tdi).rename(names[1])
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result = op(ser, tdi)
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assert result.name == names[2]
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# interval dtype
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if op in [operator.eq, operator.ne]:
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# interval dtype comparisons not yet implemented
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ii = pd.interval_range(start=0, periods=5, name=names[0])
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ser = Series(ii).rename(names[1])
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result = op(ser, ii)
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assert result.name == names[2]
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# categorical
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if op in [operator.eq, operator.ne]:
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# categorical dtype comparisons raise for inequalities
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cidx = tdi.astype("category")
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ser = Series(cidx).rename(names[1])
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result = op(ser, cidx)
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assert result.name == names[2]
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def test_comparisons(self):
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left = np.random.randn(10)
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right = np.random.randn(10)
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left[:3] = np.nan
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result = nanops.nangt(left, right)
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with np.errstate(invalid="ignore"):
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expected = (left > right).astype("O")
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expected[:3] = np.nan
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tm.assert_almost_equal(result, expected)
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s = Series(["a", "b", "c"])
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s2 = Series([False, True, False])
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# it works!
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exp = Series([False, False, False])
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tm.assert_series_equal(s == s2, exp)
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tm.assert_series_equal(s2 == s, exp)
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# -----------------------------------------------------------------
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# Categorical Dtype Comparisons
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def test_categorical_comparisons(self):
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# GH#8938
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# allow equality comparisons
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a = Series(list("abc"), dtype="category")
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b = Series(list("abc"), dtype="object")
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c = Series(["a", "b", "cc"], dtype="object")
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d = Series(list("acb"), dtype="object")
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e = Categorical(list("abc"))
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f = Categorical(list("acb"))
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# vs scalar
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assert not (a == "a").all()
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assert ((a != "a") == ~(a == "a")).all()
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assert not ("a" == a).all()
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assert (a == "a")[0]
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assert ("a" == a)[0]
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assert not ("a" != a)[0]
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# vs list-like
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assert (a == a).all()
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assert not (a != a).all()
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assert (a == list(a)).all()
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assert (a == b).all()
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assert (b == a).all()
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assert ((~(a == b)) == (a != b)).all()
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assert ((~(b == a)) == (b != a)).all()
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assert not (a == c).all()
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assert not (c == a).all()
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assert not (a == d).all()
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assert not (d == a).all()
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# vs a cat-like
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assert (a == e).all()
|
|
assert (e == a).all()
|
|
assert not (a == f).all()
|
|
assert not (f == a).all()
|
|
|
|
assert (~(a == e) == (a != e)).all()
|
|
assert (~(e == a) == (e != a)).all()
|
|
assert (~(a == f) == (a != f)).all()
|
|
assert (~(f == a) == (f != a)).all()
|
|
|
|
# non-equality is not comparable
|
|
msg = "can only compare equality or not"
|
|
with pytest.raises(TypeError, match=msg):
|
|
a < b
|
|
with pytest.raises(TypeError, match=msg):
|
|
b < a
|
|
with pytest.raises(TypeError, match=msg):
|
|
a > b
|
|
with pytest.raises(TypeError, match=msg):
|
|
b > a
|
|
|
|
def test_unequal_categorical_comparison_raises_type_error(self):
|
|
# unequal comparison should raise for unordered cats
|
|
cat = Series(Categorical(list("abc")))
|
|
msg = "can only compare equality or not"
|
|
with pytest.raises(TypeError, match=msg):
|
|
cat > "b"
|
|
|
|
cat = Series(Categorical(list("abc"), ordered=False))
|
|
with pytest.raises(TypeError, match=msg):
|
|
cat > "b"
|
|
|
|
# https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
|
|
# and following comparisons with scalars not in categories should raise
|
|
# for unequal comps, but not for equal/not equal
|
|
cat = Series(Categorical(list("abc"), ordered=True))
|
|
|
|
msg = "Cannot compare a Categorical for op.+with a scalar"
|
|
with pytest.raises(TypeError, match=msg):
|
|
cat < "d"
|
|
with pytest.raises(TypeError, match=msg):
|
|
cat > "d"
|
|
with pytest.raises(TypeError, match=msg):
|
|
"d" < cat
|
|
with pytest.raises(TypeError, match=msg):
|
|
"d" > cat
|
|
|
|
tm.assert_series_equal(cat == "d", Series([False, False, False]))
|
|
tm.assert_series_equal(cat != "d", Series([True, True, True]))
|
|
|
|
# -----------------------------------------------------------------
|
|
|
|
def test_comparison_tuples(self):
|
|
# GH#11339
|
|
# comparisons vs tuple
|
|
s = Series([(1, 1), (1, 2)])
|
|
|
|
result = s == (1, 2)
|
|
expected = Series([False, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s != (1, 2)
|
|
expected = Series([True, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s == (0, 0)
|
|
expected = Series([False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s != (0, 0)
|
|
expected = Series([True, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s = Series([(1, 1), (1, 1)])
|
|
|
|
result = s == (1, 1)
|
|
expected = Series([True, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s != (1, 1)
|
|
expected = Series([False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s = Series([frozenset([1]), frozenset([1, 2])])
|
|
|
|
result = s == frozenset([1])
|
|
expected = Series([True, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_comparison_operators_with_nas(self):
|
|
ser = Series(bdate_range("1/1/2000", periods=10), dtype=object)
|
|
ser[::2] = np.nan
|
|
|
|
# test that comparisons work
|
|
ops = ["lt", "le", "gt", "ge", "eq", "ne"]
|
|
for op in ops:
|
|
val = ser[5]
|
|
|
|
f = getattr(operator, op)
|
|
result = f(ser, val)
|
|
|
|
expected = f(ser.dropna(), val).reindex(ser.index)
|
|
|
|
if op == "ne":
|
|
expected = expected.fillna(True).astype(bool)
|
|
else:
|
|
expected = expected.fillna(False).astype(bool)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# FIXME: dont leave commented-out
|
|
# fffffffuuuuuuuuuuuu
|
|
# result = f(val, s)
|
|
# expected = f(val, s.dropna()).reindex(s.index)
|
|
# tm.assert_series_equal(result, expected)
|
|
|
|
def test_ne(self):
|
|
ts = Series([3, 4, 5, 6, 7], [3, 4, 5, 6, 7], dtype=float)
|
|
expected = [True, True, False, True, True]
|
|
assert tm.equalContents(ts.index != 5, expected)
|
|
assert tm.equalContents(~(ts.index == 5), expected)
|
|
|
|
def test_comp_ops_df_compat(self):
|
|
# GH 1134
|
|
s1 = pd.Series([1, 2, 3], index=list("ABC"), name="x")
|
|
s2 = pd.Series([2, 2, 2], index=list("ABD"), name="x")
|
|
|
|
s3 = pd.Series([1, 2, 3], index=list("ABC"), name="x")
|
|
s4 = pd.Series([2, 2, 2, 2], index=list("ABCD"), name="x")
|
|
|
|
for left, right in [(s1, s2), (s2, s1), (s3, s4), (s4, s3)]:
|
|
|
|
msg = "Can only compare identically-labeled Series objects"
|
|
with pytest.raises(ValueError, match=msg):
|
|
left == right
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
left != right
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
left < right
|
|
|
|
msg = "Can only compare identically-labeled DataFrame objects"
|
|
with pytest.raises(ValueError, match=msg):
|
|
left.to_frame() == right.to_frame()
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
left.to_frame() != right.to_frame()
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
left.to_frame() < right.to_frame()
|
|
|
|
def test_compare_series_interval_keyword(self):
|
|
# GH#25338
|
|
s = Series(["IntervalA", "IntervalB", "IntervalC"])
|
|
result = s == "IntervalA"
|
|
expected = Series([True, False, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
# ------------------------------------------------------------------
|
|
# Unsorted
|
|
# These arithmetic tests were previously in other files, eventually
|
|
# should be parametrized and put into tests.arithmetic
|
|
|
|
|
|
class TestTimeSeriesArithmetic:
|
|
# TODO: De-duplicate with test below
|
|
def test_series_add_tz_mismatch_converts_to_utc_duplicate(self):
|
|
rng = date_range("1/1/2011", periods=10, freq="H", tz="US/Eastern")
|
|
ser = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
ts_moscow = ser.tz_convert("Europe/Moscow")
|
|
|
|
result = ser + ts_moscow
|
|
assert result.index.tz is pytz.utc
|
|
|
|
result = ts_moscow + ser
|
|
assert result.index.tz is pytz.utc
|
|
|
|
def test_series_add_tz_mismatch_converts_to_utc(self):
|
|
rng = date_range("1/1/2011", periods=100, freq="H", tz="utc")
|
|
|
|
perm = np.random.permutation(100)[:90]
|
|
ser1 = Series(
|
|
np.random.randn(90), index=rng.take(perm).tz_convert("US/Eastern")
|
|
)
|
|
|
|
perm = np.random.permutation(100)[:90]
|
|
ser2 = Series(
|
|
np.random.randn(90), index=rng.take(perm).tz_convert("Europe/Berlin")
|
|
)
|
|
|
|
result = ser1 + ser2
|
|
|
|
uts1 = ser1.tz_convert("utc")
|
|
uts2 = ser2.tz_convert("utc")
|
|
expected = uts1 + uts2
|
|
|
|
assert result.index.tz == pytz.UTC
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_series_add_aware_naive_raises(self):
|
|
rng = date_range("1/1/2011", periods=10, freq="H")
|
|
ser = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
ser_utc = ser.tz_localize("utc")
|
|
|
|
msg = "Cannot join tz-naive with tz-aware DatetimeIndex"
|
|
with pytest.raises(Exception, match=msg):
|
|
ser + ser_utc
|
|
|
|
with pytest.raises(Exception, match=msg):
|
|
ser_utc + ser
|
|
|
|
def test_datetime_understood(self):
|
|
# Ensures it doesn't fail to create the right series
|
|
# reported in issue#16726
|
|
series = pd.Series(pd.date_range("2012-01-01", periods=3))
|
|
offset = pd.offsets.DateOffset(days=6)
|
|
result = series - offset
|
|
expected = pd.Series(pd.to_datetime(["2011-12-26", "2011-12-27", "2011-12-28"]))
|
|
tm.assert_series_equal(result, expected)
|
|
|