mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-23 15:38:14 +01:00
287 lines
9.1 KiB
Python
287 lines
9.1 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays import TimedeltaArray
|
|
|
|
|
|
class TestTimedeltaArrayConstructor:
|
|
def test_only_1dim_accepted(self):
|
|
# GH#25282
|
|
arr = np.array([0, 1, 2, 3], dtype="m8[h]").astype("m8[ns]")
|
|
|
|
with pytest.raises(ValueError, match="Only 1-dimensional"):
|
|
# 3-dim, we allow 2D to sneak in for ops purposes GH#29853
|
|
TimedeltaArray(arr.reshape(2, 2, 1))
|
|
|
|
with pytest.raises(ValueError, match="Only 1-dimensional"):
|
|
# 0-dim
|
|
TimedeltaArray(arr[[0]].squeeze())
|
|
|
|
def test_freq_validation(self):
|
|
# ensure that the public constructor cannot create an invalid instance
|
|
arr = np.array([0, 0, 1], dtype=np.int64) * 3600 * 10 ** 9
|
|
|
|
msg = (
|
|
"Inferred frequency None from passed values does not "
|
|
"conform to passed frequency D"
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
TimedeltaArray(arr.view("timedelta64[ns]"), freq="D")
|
|
|
|
def test_non_array_raises(self):
|
|
with pytest.raises(ValueError, match="list"):
|
|
TimedeltaArray([1, 2, 3])
|
|
|
|
def test_other_type_raises(self):
|
|
with pytest.raises(ValueError, match="dtype bool cannot be converted"):
|
|
TimedeltaArray(np.array([1, 2, 3], dtype="bool"))
|
|
|
|
def test_incorrect_dtype_raises(self):
|
|
# TODO: why TypeError for 'category' but ValueError for i8?
|
|
with pytest.raises(
|
|
ValueError, match=r"category cannot be converted to timedelta64\[ns\]"
|
|
):
|
|
TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
|
|
|
|
with pytest.raises(
|
|
ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]",
|
|
):
|
|
TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64"))
|
|
|
|
def test_copy(self):
|
|
data = np.array([1, 2, 3], dtype="m8[ns]")
|
|
arr = TimedeltaArray(data, copy=False)
|
|
assert arr._data is data
|
|
|
|
arr = TimedeltaArray(data, copy=True)
|
|
assert arr._data is not data
|
|
assert arr._data.base is not data
|
|
|
|
|
|
class TestTimedeltaArray:
|
|
def test_np_sum(self):
|
|
# GH#25282
|
|
vals = np.arange(5, dtype=np.int64).view("m8[h]").astype("m8[ns]")
|
|
arr = TimedeltaArray(vals)
|
|
result = np.sum(arr)
|
|
assert result == vals.sum()
|
|
|
|
result = np.sum(pd.TimedeltaIndex(arr))
|
|
assert result == vals.sum()
|
|
|
|
def test_from_sequence_dtype(self):
|
|
msg = "dtype .*object.* cannot be converted to timedelta64"
|
|
with pytest.raises(ValueError, match=msg):
|
|
TimedeltaArray._from_sequence([], dtype=object)
|
|
|
|
def test_abs(self):
|
|
vals = np.array([-3600 * 10 ** 9, "NaT", 7200 * 10 ** 9], dtype="m8[ns]")
|
|
arr = TimedeltaArray(vals)
|
|
|
|
evals = np.array([3600 * 10 ** 9, "NaT", 7200 * 10 ** 9], dtype="m8[ns]")
|
|
expected = TimedeltaArray(evals)
|
|
|
|
result = abs(arr)
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
def test_neg(self):
|
|
vals = np.array([-3600 * 10 ** 9, "NaT", 7200 * 10 ** 9], dtype="m8[ns]")
|
|
arr = TimedeltaArray(vals)
|
|
|
|
evals = np.array([3600 * 10 ** 9, "NaT", -7200 * 10 ** 9], dtype="m8[ns]")
|
|
expected = TimedeltaArray(evals)
|
|
|
|
result = -arr
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
def test_neg_freq(self):
|
|
tdi = pd.timedelta_range("2 Days", periods=4, freq="H")
|
|
arr = TimedeltaArray(tdi, freq=tdi.freq)
|
|
|
|
expected = TimedeltaArray(-tdi._data, freq=-tdi.freq)
|
|
|
|
result = -arr
|
|
tm.assert_timedelta_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
|
|
def test_astype_int(self, dtype):
|
|
arr = TimedeltaArray._from_sequence([pd.Timedelta("1H"), pd.Timedelta("2H")])
|
|
result = arr.astype(dtype)
|
|
|
|
if np.dtype(dtype).kind == "u":
|
|
expected_dtype = np.dtype("uint64")
|
|
else:
|
|
expected_dtype = np.dtype("int64")
|
|
expected = arr.astype(expected_dtype)
|
|
|
|
assert result.dtype == expected_dtype
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_setitem_clears_freq(self):
|
|
a = TimedeltaArray(pd.timedelta_range("1H", periods=2, freq="H"))
|
|
a[0] = pd.Timedelta("1H")
|
|
assert a.freq is None
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.Timedelta(seconds=1),
|
|
pd.Timedelta(seconds=1).to_timedelta64(),
|
|
pd.Timedelta(seconds=1).to_pytimedelta(),
|
|
],
|
|
)
|
|
def test_setitem_objects(self, obj):
|
|
# make sure we accept timedelta64 and timedelta in addition to Timedelta
|
|
tdi = pd.timedelta_range("2 Days", periods=4, freq="H")
|
|
arr = TimedeltaArray(tdi, freq=tdi.freq)
|
|
|
|
arr[0] = obj
|
|
assert arr[0] == pd.Timedelta(seconds=1)
|
|
|
|
@pytest.mark.parametrize(
|
|
"other",
|
|
[
|
|
1,
|
|
np.int64(1),
|
|
1.0,
|
|
np.datetime64("NaT"),
|
|
pd.Timestamp.now(),
|
|
"invalid",
|
|
np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9,
|
|
(np.arange(10) * 24 * 3600 * 10 ** 9).view("datetime64[ns]"),
|
|
pd.Timestamp.now().to_period("D"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("index", [True, False])
|
|
def test_searchsorted_invalid_types(self, other, index):
|
|
data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
|
|
arr = TimedeltaArray(data, freq="D")
|
|
if index:
|
|
arr = pd.Index(arr)
|
|
|
|
msg = "|".join(
|
|
[
|
|
"searchsorted requires compatible dtype or scalar",
|
|
"Unexpected type for 'value'",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr.searchsorted(other)
|
|
|
|
|
|
class TestReductions:
|
|
@pytest.mark.parametrize("name", ["sum", "std", "min", "max", "median"])
|
|
@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
|
|
|
|
def test_min_max(self):
|
|
arr = TimedeltaArray._from_sequence(["3H", "3H", "NaT", "2H", "5H", "4H"])
|
|
|
|
result = arr.min()
|
|
expected = pd.Timedelta("2H")
|
|
assert result == expected
|
|
|
|
result = arr.max()
|
|
expected = pd.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 = pd.Timedelta(hours=17)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.sum(skipna=True)
|
|
assert isinstance(result, pd.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, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.sum(min_count=1)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
def test_npsum(self):
|
|
# 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 = pd.Timedelta(hours=17)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = np.sum(arr)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
def test_std(self):
|
|
tdi = pd.TimedeltaIndex(["0H", "4H", "NaT", "4H", "0H", "2H"])
|
|
arr = tdi.array
|
|
|
|
result = arr.std(skipna=True)
|
|
expected = pd.Timedelta(hours=2)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.std(skipna=True)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = arr.std(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.std(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 = pd.Timedelta(hours=2)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = tdi.median(skipna=True)
|
|
assert isinstance(result, pd.Timedelta)
|
|
assert result == expected
|
|
|
|
result = arr.std(skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = tdi.std(skipna=False)
|
|
assert result is pd.NaT
|