Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
115 lines
3.8 KiB
115 lines
3.8 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Period, Series, period_range
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays import PeriodArray
|
|
|
|
|
|
class TestSeriesPeriod:
|
|
def setup_method(self, method):
|
|
self.series = Series(period_range("2000-01-01", periods=10, freq="D"))
|
|
|
|
def test_auto_conversion(self):
|
|
series = Series(list(period_range("2000-01-01", periods=10, freq="D")))
|
|
assert series.dtype == "Period[D]"
|
|
|
|
series = pd.Series(
|
|
[pd.Period("2011-01-01", freq="D"), pd.Period("2011-02-01", freq="D")]
|
|
)
|
|
assert series.dtype == "Period[D]"
|
|
|
|
def test_isna(self):
|
|
# GH 13737
|
|
s = Series([pd.Period("2011-01", freq="M"), pd.Period("NaT", freq="M")])
|
|
tm.assert_series_equal(s.isna(), Series([False, True]))
|
|
tm.assert_series_equal(s.notna(), Series([True, False]))
|
|
|
|
def test_dropna(self):
|
|
# GH 13737
|
|
s = Series([pd.Period("2011-01", freq="M"), pd.Period("NaT", freq="M")])
|
|
tm.assert_series_equal(s.dropna(), Series([pd.Period("2011-01", freq="M")]))
|
|
|
|
# ---------------------------------------------------------------------
|
|
# NaT support
|
|
|
|
@pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
|
|
def test_NaT_scalar(self):
|
|
series = Series([0, 1000, 2000, pd._libs.iNaT], dtype="period[D]")
|
|
|
|
val = series[3]
|
|
assert pd.isna(val)
|
|
|
|
series[2] = val
|
|
assert pd.isna(series[2])
|
|
|
|
def test_NaT_cast(self):
|
|
result = Series([np.nan]).astype("period[D]")
|
|
expected = Series([pd.NaT], dtype="period[D]")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_set_none(self):
|
|
self.series[3] = None
|
|
assert self.series[3] is pd.NaT
|
|
|
|
self.series[3:5] = None
|
|
assert self.series[4] is pd.NaT
|
|
|
|
def test_set_nan(self):
|
|
# Do we want to allow this?
|
|
self.series[5] = np.nan
|
|
assert self.series[5] is pd.NaT
|
|
|
|
self.series[5:7] = np.nan
|
|
assert self.series[6] is pd.NaT
|
|
|
|
def test_intercept_astype_object(self):
|
|
expected = self.series.astype("object")
|
|
|
|
df = DataFrame({"a": self.series, "b": np.random.randn(len(self.series))})
|
|
|
|
result = df.values.squeeze()
|
|
assert (result[:, 0] == expected.values).all()
|
|
|
|
df = DataFrame({"a": self.series, "b": ["foo"] * len(self.series)})
|
|
|
|
result = df.values.squeeze()
|
|
assert (result[:, 0] == expected.values).all()
|
|
|
|
@pytest.mark.parametrize(
|
|
"input_vals",
|
|
[
|
|
[Period("2016-01", freq="M"), Period("2016-02", freq="M")],
|
|
[Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")],
|
|
[
|
|
Period("2016-01-01 00:00:00", freq="H"),
|
|
Period("2016-01-01 01:00:00", freq="H"),
|
|
],
|
|
[
|
|
Period("2016-01-01 00:00:00", freq="M"),
|
|
Period("2016-01-01 00:01:00", freq="M"),
|
|
],
|
|
[
|
|
Period("2016-01-01 00:00:00", freq="S"),
|
|
Period("2016-01-01 00:00:01", freq="S"),
|
|
],
|
|
],
|
|
)
|
|
def test_end_time_timevalues(self, input_vals):
|
|
# GH 17157
|
|
# Check that the time part of the Period is adjusted by end_time
|
|
# when using the dt accessor on a Series
|
|
input_vals = PeriodArray._from_sequence(np.asarray(input_vals))
|
|
|
|
s = Series(input_vals)
|
|
result = s.dt.end_time
|
|
expected = s.apply(lambda x: x.end_time)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("input_vals", [("2001"), ("NaT")])
|
|
def test_to_period(self, input_vals):
|
|
# GH 21205
|
|
expected = Series([input_vals], dtype="Period[D]")
|
|
result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D")
|
|
tm.assert_series_equal(result, expected)
|
|
|