Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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246 lines
8.7 KiB
246 lines
8.7 KiB
4 years ago
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from datetime import datetime
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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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from pandas import NaT, Series, Timestamp
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import pandas._testing as tm
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from pandas.core.internals.blocks import IntBlock
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class TestSeriesInternals:
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# GH 10265
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def test_convert(self):
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# Tests: All to nans, coerce, true
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# Test coercion returns correct type
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s = Series(["a", "b", "c"])
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results = s._convert(datetime=True, coerce=True)
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expected = Series([NaT] * 3)
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tm.assert_series_equal(results, expected)
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results = s._convert(numeric=True, coerce=True)
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expected = Series([np.nan] * 3)
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tm.assert_series_equal(results, expected)
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expected = Series([NaT] * 3, dtype=np.dtype("m8[ns]"))
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results = s._convert(timedelta=True, coerce=True)
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tm.assert_series_equal(results, expected)
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dt = datetime(2001, 1, 1, 0, 0)
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td = dt - datetime(2000, 1, 1, 0, 0)
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# Test coercion with mixed types
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s = Series(["a", "3.1415", dt, td])
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results = s._convert(datetime=True, coerce=True)
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expected = Series([NaT, NaT, dt, NaT])
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tm.assert_series_equal(results, expected)
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results = s._convert(numeric=True, coerce=True)
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expected = Series([np.nan, 3.1415, np.nan, np.nan])
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tm.assert_series_equal(results, expected)
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results = s._convert(timedelta=True, coerce=True)
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expected = Series([NaT, NaT, NaT, td], dtype=np.dtype("m8[ns]"))
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tm.assert_series_equal(results, expected)
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# Test standard conversion returns original
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results = s._convert(datetime=True)
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tm.assert_series_equal(results, s)
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results = s._convert(numeric=True)
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expected = Series([np.nan, 3.1415, np.nan, np.nan])
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tm.assert_series_equal(results, expected)
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results = s._convert(timedelta=True)
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tm.assert_series_equal(results, s)
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# test pass-through and non-conversion when other types selected
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s = Series(["1.0", "2.0", "3.0"])
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results = s._convert(datetime=True, numeric=True, timedelta=True)
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expected = Series([1.0, 2.0, 3.0])
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tm.assert_series_equal(results, expected)
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results = s._convert(True, False, True)
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tm.assert_series_equal(results, s)
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s = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 1, 0, 0)], dtype="O")
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results = s._convert(datetime=True, numeric=True, timedelta=True)
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expected = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 1, 0, 0)])
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tm.assert_series_equal(results, expected)
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results = s._convert(datetime=False, numeric=True, timedelta=True)
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tm.assert_series_equal(results, s)
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td = datetime(2001, 1, 1, 0, 0) - datetime(2000, 1, 1, 0, 0)
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s = Series([td, td], dtype="O")
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results = s._convert(datetime=True, numeric=True, timedelta=True)
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expected = Series([td, td])
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tm.assert_series_equal(results, expected)
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results = s._convert(True, True, False)
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tm.assert_series_equal(results, s)
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s = Series([1.0, 2, 3], index=["a", "b", "c"])
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result = s._convert(numeric=True)
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tm.assert_series_equal(result, s)
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# force numeric conversion
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r = s.copy().astype("O")
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r["a"] = "1"
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result = r._convert(numeric=True)
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tm.assert_series_equal(result, s)
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r = s.copy().astype("O")
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r["a"] = "1."
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result = r._convert(numeric=True)
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tm.assert_series_equal(result, s)
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r = s.copy().astype("O")
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r["a"] = "garbled"
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result = r._convert(numeric=True)
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expected = s.copy()
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expected["a"] = np.nan
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tm.assert_series_equal(result, expected)
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# GH 4119, not converting a mixed type (e.g.floats and object)
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s = Series([1, "na", 3, 4])
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result = s._convert(datetime=True, numeric=True)
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expected = Series([1, np.nan, 3, 4])
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tm.assert_series_equal(result, expected)
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s = Series([1, "", 3, 4])
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result = s._convert(datetime=True, numeric=True)
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tm.assert_series_equal(result, expected)
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# dates
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s = Series(
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[
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datetime(2001, 1, 1, 0, 0),
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datetime(2001, 1, 2, 0, 0),
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datetime(2001, 1, 3, 0, 0),
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]
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)
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s2 = Series(
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[
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datetime(2001, 1, 1, 0, 0),
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datetime(2001, 1, 2, 0, 0),
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datetime(2001, 1, 3, 0, 0),
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"foo",
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1.0,
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1,
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Timestamp("20010104"),
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"20010105",
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],
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dtype="O",
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)
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result = s._convert(datetime=True)
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expected = Series(
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[Timestamp("20010101"), Timestamp("20010102"), Timestamp("20010103")],
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dtype="M8[ns]",
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)
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tm.assert_series_equal(result, expected)
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result = s._convert(datetime=True, coerce=True)
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tm.assert_series_equal(result, expected)
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expected = Series(
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[
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Timestamp("20010101"),
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Timestamp("20010102"),
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Timestamp("20010103"),
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NaT,
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NaT,
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NaT,
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Timestamp("20010104"),
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Timestamp("20010105"),
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],
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dtype="M8[ns]",
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)
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result = s2._convert(datetime=True, numeric=False, timedelta=False, coerce=True)
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tm.assert_series_equal(result, expected)
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result = s2._convert(datetime=True, coerce=True)
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tm.assert_series_equal(result, expected)
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s = Series(["foo", "bar", 1, 1.0], dtype="O")
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result = s._convert(datetime=True, coerce=True)
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expected = Series([NaT] * 2 + [Timestamp(1)] * 2)
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tm.assert_series_equal(result, expected)
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# preserver if non-object
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s = Series([1], dtype="float32")
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result = s._convert(datetime=True, coerce=True)
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tm.assert_series_equal(result, s)
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# FIXME: dont leave commented-out
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# r = s.copy()
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# r[0] = np.nan
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# result = r._convert(convert_dates=True,convert_numeric=False)
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# assert result.dtype == 'M8[ns]'
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# dateutil parses some single letters into today's value as a date
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expected = Series([NaT])
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for x in "abcdefghijklmnopqrstuvwxyz":
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s = Series([x])
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result = s._convert(datetime=True, coerce=True)
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tm.assert_series_equal(result, expected)
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s = Series([x.upper()])
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result = s._convert(datetime=True, coerce=True)
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tm.assert_series_equal(result, expected)
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def test_convert_no_arg_error(self):
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s = Series(["1.0", "2"])
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msg = r"At least one of datetime, numeric or timedelta must be True\."
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with pytest.raises(ValueError, match=msg):
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s._convert()
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def test_convert_preserve_bool(self):
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s = Series([1, True, 3, 5], dtype=object)
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r = s._convert(datetime=True, numeric=True)
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e = Series([1, 1, 3, 5], dtype="i8")
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tm.assert_series_equal(r, e)
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def test_convert_preserve_all_bool(self):
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s = Series([False, True, False, False], dtype=object)
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r = s._convert(datetime=True, numeric=True)
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e = Series([False, True, False, False], dtype=bool)
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tm.assert_series_equal(r, e)
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def test_constructor_no_pandas_array(self):
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ser = pd.Series([1, 2, 3])
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result = pd.Series(ser.array)
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tm.assert_series_equal(ser, result)
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assert isinstance(result._mgr.blocks[0], IntBlock)
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def test_astype_no_pandas_dtype(self):
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# https://github.com/pandas-dev/pandas/pull/24866
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ser = pd.Series([1, 2], dtype="int64")
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# Don't have PandasDtype in the public API, so we use `.array.dtype`,
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# which is a PandasDtype.
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result = ser.astype(ser.array.dtype)
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tm.assert_series_equal(result, ser)
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def test_from_array(self):
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result = pd.Series(pd.array(["1H", "2H"], dtype="timedelta64[ns]"))
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assert result._mgr.blocks[0].is_extension is False
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result = pd.Series(pd.array(["2015"], dtype="datetime64[ns]"))
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assert result._mgr.blocks[0].is_extension is False
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def test_from_list_dtype(self):
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result = pd.Series(["1H", "2H"], dtype="timedelta64[ns]")
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assert result._mgr.blocks[0].is_extension is False
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result = pd.Series(["2015"], dtype="datetime64[ns]")
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assert result._mgr.blocks[0].is_extension is False
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def test_hasnans_uncached_for_series():
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# GH#19700
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idx = pd.Index([0, 1])
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assert idx.hasnans is False
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assert "hasnans" in idx._cache
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ser = idx.to_series()
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assert ser.hasnans is False
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assert not hasattr(ser, "_cache")
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ser.iloc[-1] = np.nan
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assert ser.hasnans is True
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assert Series.hasnans.__doc__ == pd.Index.hasnans.__doc__
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