def test_NaT_methods():
# GH 9513
raise_methods = ['astimezone', 'combine', 'ctime', 'dst',
'fromordinal', 'fromtimestamp', 'isocalendar',
'strftime', 'strptime', 'time', 'timestamp',
'timetuple', 'timetz', 'toordinal', 'tzname',
'utcfromtimestamp', 'utcnow', 'utcoffset',
'utctimetuple']
nat_methods = ['date', 'now', 'replace', 'to_datetime', 'today']
nan_methods = ['weekday', 'isoweekday']
for method in raise_methods:
if hasattr(NaT, method):
with pytest.raises(ValueError):
getattr(NaT, method)()
for method in nan_methods:
if hasattr(NaT, method):
assert np.isnan(getattr(NaT, method)())
for method in nat_methods:
if hasattr(NaT, method):
# see gh-8254
exp_warning = None
if method == 'to_datetime':
exp_warning = FutureWarning
with tm.assert_produces_warning(
exp_warning, check_stacklevel=False):
assert getattr(NaT, method)() is NaT
# GH 12300
assert NaT.isoformat() == 'NaT'
def setup(self, dtype):
N = 10000
self.mismatched = [NaT.to_datetime64()] * 2
if dtype in ["boolean", "bool"]:
self.series = Series(np.random.randint(0, 2, N)).astype(dtype)
self.values = [True, False]
elif dtype == "datetime64[ns]":
# Note: values here is much larger than non-dt64ns cases
# dti has length=115777
dti = date_range(start="2015-10-26", end="2016-01-01", freq="50s")
self.series = Series(dti)
self.values = self.series._values[::3]
self.mismatched = [1, 2]
elif dtype in ["category[object]", "category[int]"]:
# Note: sizes are different in this case than others
np.random.seed(1234)
n = 5 * 10**5
sample_size = 100
arr = list(np.random.randint(0, n // 10, size=n))
if dtype == "category[object]":
arr = [f"s{i:04d}" for i in arr]
self.values = np.random.choice(arr, sample_size)
self.series = Series(arr).astype("category")
elif dtype in ["str", "string", "arrow_string"]:
from pandas.core.arrays.string_arrow import ArrowStringDtype # noqa: F401
try:
self.series = Series(tm.makeStringIndex(N), dtype=dtype)
except ImportError:
raise NotImplementedError
self.values = list(self.series[:2])
else:
self.series = Series(np.random.randint(1, 10, N)).astype(dtype)
self.values = [1, 2]
self.cat_values = Categorical(self.values)
def setup(self, dtype):
N = 10000
self.mismatched = [NaT.to_datetime64()] * 2
if dtype in ["boolean", "bool"]:
self.series = Series(np.random.randint(0, 2, N)).astype(dtype)
self.values = [True, False]
elif dtype == "datetime64[ns]":
# Note: values here is much larger than non-dt64ns cases
# dti has length=115777
dti = date_range(start="2015-10-26", end="2016-01-01", freq="50s")
self.series = Series(dti)
self.values = self.series._values[::3]
self.mismatched = [1, 2]
elif dtype in ["category[object]", "category[int]"]:
# Note: sizes are different in this case than others
np.random.seed(1234)
n = 5 * 10**5
sample_size = 100
arr = list(np.random.randint(0, n // 10, size=n))
if dtype == "category[object]":
arr = [f"s{i:04d}" for i in arr]
self.values = np.random.choice(arr, sample_size)
self.series = Series(arr).astype("category")
else:
self.series = Series(np.random.randint(1, 10, N)).astype(dtype)
self.values = [1, 2]
self.cat_values = Categorical(self.values)
def test_to_numpy_alias():
# GH 24653: alias .to_numpy() for scalars
expected = NaT.to_datetime64()
result = NaT.to_numpy()
assert isna(expected) and isna(result)
# GH#44460
result = NaT.to_numpy("M8[s]")
assert isinstance(result, np.datetime64)
assert result.dtype == "M8[s]"
result = NaT.to_numpy("m8[ns]")
assert isinstance(result, np.timedelta64)
assert result.dtype == "m8[ns]"
result = NaT.to_numpy("m8[s]")
assert isinstance(result, np.timedelta64)
assert result.dtype == "m8[s]"
with pytest.raises(ValueError, match="NaT.to_numpy dtype must be a "):
NaT.to_numpy(np.int64)
def test_to_numpy_alias():
# GH 24653: alias .to_numpy() for scalars
expected = NaT.to_datetime64()
result = NaT.to_numpy()
assert isna(expected) and isna(result)
def test_to_numpy_alias():
# GH 24653: alias .to_numpy() for scalars
expected = NaT.to_datetime64()
result = NaT.to_numpy()
assert isna(expected) and isna(result)
