| Current File : //usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/test_timedeltas.py |
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