Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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123 lines
4.0 KiB
123 lines
4.0 KiB
4 years ago
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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class TestTimedeltaIndexing:
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def test_loc_setitem_bool_mask(self):
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# GH 14946
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df = pd.DataFrame({"x": range(10)})
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df.index = pd.to_timedelta(range(10), unit="s")
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conditions = [df["x"] > 3, df["x"] == 3, df["x"] < 3]
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expected_data = [
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[0, 1, 2, 3, 10, 10, 10, 10, 10, 10],
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[0, 1, 2, 10, 4, 5, 6, 7, 8, 9],
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[10, 10, 10, 3, 4, 5, 6, 7, 8, 9],
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]
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for cond, data in zip(conditions, expected_data):
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result = df.copy()
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result.loc[cond, "x"] = 10
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expected = pd.DataFrame(
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data,
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index=pd.to_timedelta(range(10), unit="s"),
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columns=["x"],
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dtype="int64",
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)
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tm.assert_frame_equal(expected, result)
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@pytest.mark.parametrize(
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"indexer, expected",
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[
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(0, [20, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
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(slice(4, 8), [0, 1, 2, 3, 20, 20, 20, 20, 8, 9]),
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([3, 5], [0, 1, 2, 20, 4, 20, 6, 7, 8, 9]),
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],
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)
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def test_list_like_indexing(self, indexer, expected):
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# GH 16637
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df = pd.DataFrame({"x": range(10)}, dtype="int64")
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df.index = pd.to_timedelta(range(10), unit="s")
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df.loc[df.index[indexer], "x"] = 20
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expected = pd.DataFrame(
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expected,
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index=pd.to_timedelta(range(10), unit="s"),
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columns=["x"],
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dtype="int64",
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)
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tm.assert_frame_equal(expected, df)
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def test_string_indexing(self):
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# GH 16896
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df = pd.DataFrame({"x": range(3)}, index=pd.to_timedelta(range(3), unit="days"))
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expected = df.iloc[0]
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sliced = df.loc["0 days"]
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tm.assert_series_equal(sliced, expected)
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@pytest.mark.parametrize("value", [None, pd.NaT, np.nan])
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def test_setitem_mask_na_value_td64(self, value):
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# issue (#18586)
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series = pd.Series([0, 1, 2], dtype="timedelta64[ns]")
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series[series == series[0]] = value
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expected = pd.Series([pd.NaT, 1, 2], dtype="timedelta64[ns]")
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tm.assert_series_equal(series, expected)
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@pytest.mark.parametrize("value", [None, pd.NaT, np.nan])
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def test_listlike_setitem(self, value):
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# issue (#18586)
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series = pd.Series([0, 1, 2], dtype="timedelta64[ns]")
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series.iloc[0] = value
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expected = pd.Series([pd.NaT, 1, 2], dtype="timedelta64[ns]")
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tm.assert_series_equal(series, expected)
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@pytest.mark.parametrize(
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"start,stop, expected_slice",
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[
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[np.timedelta64(0, "ns"), None, slice(0, 11)],
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[np.timedelta64(1, "D"), np.timedelta64(6, "D"), slice(1, 7)],
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[None, np.timedelta64(4, "D"), slice(0, 5)],
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],
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)
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def test_numpy_timedelta_scalar_indexing(self, start, stop, expected_slice):
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# GH 20393
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s = pd.Series(range(11), pd.timedelta_range("0 days", "10 days"))
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result = s.loc[slice(start, stop)]
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expected = s.iloc[expected_slice]
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tm.assert_series_equal(result, expected)
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def test_roundtrip_thru_setitem(self):
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# PR 23462
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dt1 = pd.Timedelta(0)
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dt2 = pd.Timedelta(28767471428571405)
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df = pd.DataFrame({"dt": pd.Series([dt1, dt2])})
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df_copy = df.copy()
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s = pd.Series([dt1])
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expected = df["dt"].iloc[1].value
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df.loc[[True, False]] = s
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result = df["dt"].iloc[1].value
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assert expected == result
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tm.assert_frame_equal(df, df_copy)
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def test_loc_str_slicing(self):
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ix = pd.timedelta_range(start="1 day", end="2 days", freq="1H")
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ser = ix.to_series()
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result = ser.loc[:"1 days"]
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expected = ser.iloc[:-1]
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tm.assert_series_equal(result, expected)
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def test_loc_slicing(self):
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ix = pd.timedelta_range(start="1 day", end="2 days", freq="1H")
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ser = ix.to_series()
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result = ser.loc[: ix[-2]]
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expected = ser.iloc[:-1]
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tm.assert_series_equal(result, expected)
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