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.
214 lines
6.2 KiB
214 lines
6.2 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import Timedelta
|
|
import pandas._testing as tm
|
|
from pandas.core import nanops
|
|
from pandas.core.arrays import TimedeltaArray
|
|
|
|
|
|
class TestReductions:
|
|
@pytest.mark.parametrize("name", ["std", "min", "max", "median", "mean"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_reductions_empty(self, name, skipna):
|
|
tdi = pd.TimedeltaIndex([])
|
|
arr = tdi.array
|
|
|
|
result = getattr(tdi, name)(skipna=skipna)
|
|
assert result is pd.NaT
|
|
|
|
result = getattr(arr, name)(skipna=skipna)
|
|
assert result is pd.NaT
|
|
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_sum_empty(self, skipna):
|
|
tdi = pd.TimedeltaIndex([])
|
|
arr = tdi.array
|
|
|
|
result = tdi.sum(skipna=skipna)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == Timedelta(0)
|
|
|
|
result = arr.sum(skipna=skipna)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == Timedelta(0)
|
|
|
|
def test_min_max(self):
|
|
arr = TimedeltaArray._from_sequence(["3H", "3H", "NaT", "2H", "5H", "4H"])
|
|
|
|
result = arr.min()
|
|
expected = Timedelta("2H")
|
|
assert result == expected
|
|
|
|
result = arr.max()
|
|
expected = Timedelta("5H")
|
|
assert result == expected
|
|
|
|
result = arr.min(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.max(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
def test_sum(self):
|
|
tdi = pd.TimedeltaIndex(["3H", "3H", "NaT", "2H", "5H", "4H"])
|
|
arr = tdi.array
|
|
|
|
result = arr.sum(skipna=True)
|
|
expected = Timedelta(hours=17)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.sum(skipna=True)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = arr.sum(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.sum(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.sum(min_count=9)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.sum(min_count=9)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.sum(min_count=1)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.sum(min_count=1)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
def test_npsum(self):
|
|
# GH#25282, GH#25335 np.sum should return a Timedelta, not timedelta64
|
|
tdi = pd.TimedeltaIndex(["3H", "3H", "2H", "5H", "4H"])
|
|
arr = tdi.array
|
|
|
|
result = np.sum(tdi)
|
|
expected = Timedelta(hours=17)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = np.sum(arr)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
def test_sum_2d_skipna_false(self):
|
|
arr = np.arange(8).astype(np.int64).view("m8[s]").astype("m8[ns]").reshape(4, 2)
|
|
arr[-1, -1] = "Nat"
|
|
|
|
tda = TimedeltaArray(arr)
|
|
|
|
result = tda.sum(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tda.sum(axis=0, skipna=False)
|
|
expected = pd.TimedeltaIndex([Timedelta(seconds=12), pd.NaT])._values
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result = tda.sum(axis=1, skipna=False)
|
|
expected = pd.TimedeltaIndex(
|
|
[
|
|
Timedelta(seconds=1),
|
|
Timedelta(seconds=5),
|
|
Timedelta(seconds=9),
|
|
pd.NaT,
|
|
]
|
|
)._values
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
# Adding a Timestamp makes this a test for DatetimeArray.std
|
|
@pytest.mark.parametrize(
|
|
"add",
|
|
[
|
|
Timedelta(0),
|
|
pd.Timestamp("2021-01-01"),
|
|
pd.Timestamp("2021-01-01", tz="UTC"),
|
|
pd.Timestamp("2021-01-01", tz="Asia/Tokyo"),
|
|
],
|
|
)
|
|
def test_std(self, add):
|
|
tdi = pd.TimedeltaIndex(["0H", "4H", "NaT", "4H", "0H", "2H"]) + add
|
|
arr = tdi.array
|
|
|
|
result = arr.std(skipna=True)
|
|
expected = Timedelta(hours=2)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.std(skipna=True)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
if getattr(arr, "tz", None) is None:
|
|
result = nanops.nanstd(np.asarray(arr), skipna=True)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = arr.std(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.std(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
if getattr(arr, "tz", None) is None:
|
|
result = nanops.nanstd(np.asarray(arr), skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
def test_median(self):
|
|
tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"])
|
|
arr = tdi.array
|
|
|
|
result = arr.median(skipna=True)
|
|
expected = Timedelta(hours=2)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.median(skipna=True)
|
|
assert isinstance(result, Timedelta)
|
|
assert result == expected
|
|
|
|
result = arr.median(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.median(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
def test_mean(self):
|
|
tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"])
|
|
arr = tdi._data
|
|
|
|
# manually verified result
|
|
expected = Timedelta(arr.dropna()._ndarray.mean())
|
|
|
|
result = arr.mean()
|
|
assert result == expected
|
|
result = arr.mean(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = arr.dropna().mean(skipna=False)
|
|
assert result == expected
|
|
|
|
result = arr.mean(axis=0)
|
|
assert result == expected
|
|
|
|
def test_mean_2d(self):
|
|
tdi = pd.timedelta_range("14 days", periods=6)
|
|
tda = tdi._data.reshape(3, 2)
|
|
|
|
result = tda.mean(axis=0)
|
|
expected = tda[1]
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result = tda.mean(axis=1)
|
|
expected = tda[:, 0] + Timedelta(hours=12)
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
result = tda.mean(axis=None)
|
|
expected = tdi.mean()
|
|
assert result == expected
|
|
|