本文整理匯總了Python中pandas.NaT方法的典型用法代碼示例。如果您正苦於以下問題:Python pandas.NaT方法的具體用法?Python pandas.NaT怎麽用?Python pandas.NaT使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas
的用法示例。
在下文中一共展示了pandas.NaT方法的15個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_min_max
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
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
示例2: test_min_max
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_min_max(self):
arr = period_array([
'2000-01-03',
'2000-01-03',
'NaT',
'2000-01-02',
'2000-01-05',
'2000-01-04',
], freq='D')
result = arr.min()
expected = pd.Period('2000-01-02', freq='D')
assert result == expected
result = arr.max()
expected = pd.Period('2000-01-05', freq='D')
assert result == expected
result = arr.min(skipna=False)
assert result is pd.NaT
result = arr.max(skipna=False)
assert result is pd.NaT
示例3: test_fillna_preserves_tz
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_fillna_preserves_tz(self, method):
dti = pd.date_range('2000-01-01', periods=5, freq='D', tz='US/Central')
arr = DatetimeArray(dti, copy=True)
arr[2] = pd.NaT
fill_val = dti[1] if method == 'pad' else dti[3]
expected = DatetimeArray._from_sequence(
[dti[0], dti[1], fill_val, dti[3], dti[4]],
freq=None, tz='US/Central'
)
result = arr.fillna(method=method)
tm.assert_extension_array_equal(result, expected)
# assert that arr and dti were not modified in-place
assert arr[2] is pd.NaT
assert dti[2] == pd.Timestamp('2000-01-03', tz='US/Central')
示例4: test_min_max
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_min_max(self, tz):
arr = DatetimeArray._from_sequence([
'2000-01-03',
'2000-01-03',
'NaT',
'2000-01-02',
'2000-01-05',
'2000-01-04',
], tz=tz)
result = arr.min()
expected = pd.Timestamp('2000-01-02', tz=tz)
assert result == expected
result = arr.max()
expected = pd.Timestamp('2000-01-05', tz=tz)
assert result == expected
result = arr.min(skipna=False)
assert result is pd.NaT
result = arr.max(skipna=False)
assert result is pd.NaT
示例5: test_searchsorted
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_searchsorted(self):
data = np.arange(10, dtype='i8') * 24 * 3600 * 10**9
arr = self.array_cls(data, freq='D')
# scalar
result = arr.searchsorted(arr[1])
assert result == 1
result = arr.searchsorted(arr[2], side="right")
assert result == 3
# own-type
result = arr.searchsorted(arr[1:3])
expected = np.array([1, 2], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
result = arr.searchsorted(arr[1:3], side="right")
expected = np.array([2, 3], dtype=np.intp)
tm.assert_numpy_array_equal(result, expected)
# Following numpy convention, NaT goes at the beginning
# (unlike NaN which goes at the end)
result = arr.searchsorted(pd.NaT)
assert result == 0
示例6: test_float64_ns_rounded
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_float64_ns_rounded(self):
# GH#23539 without specifying a unit, floats are regarded as nanos,
# and fractional portions are truncated
tdi = TimedeltaIndex([2.3, 9.7])
expected = TimedeltaIndex([2, 9])
tm.assert_index_equal(tdi, expected)
# integral floats are non-lossy
tdi = TimedeltaIndex([2.0, 9.0])
expected = TimedeltaIndex([2, 9])
tm.assert_index_equal(tdi, expected)
# NaNs get converted to NaT
tdi = TimedeltaIndex([2.0, np.nan])
expected = TimedeltaIndex([pd.Timedelta(nanoseconds=2), pd.NaT])
tm.assert_index_equal(tdi, expected)
示例7: test_to_timedelta_on_missing_values
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_to_timedelta_on_missing_values(self):
# GH5438
timedelta_NaT = np.timedelta64('NaT')
actual = pd.to_timedelta(Series(['00:00:01', np.nan]))
expected = Series([np.timedelta64(1000000000, 'ns'),
timedelta_NaT], dtype='<m8[ns]')
assert_series_equal(actual, expected)
actual = pd.to_timedelta(Series(['00:00:01', pd.NaT]))
assert_series_equal(actual, expected)
actual = pd.to_timedelta(np.nan)
assert actual.value == timedelta_NaT.astype('int64')
actual = pd.to_timedelta(pd.NaT)
assert actual.value == timedelta_NaT.astype('int64')
示例8: test_nat
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_nat(self):
assert pd.TimedeltaIndex._na_value is pd.NaT
assert pd.TimedeltaIndex([])._na_value is pd.NaT
idx = pd.TimedeltaIndex(['1 days', '2 days'])
assert idx._can_hold_na
tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
assert idx.hasnans is False
tm.assert_numpy_array_equal(idx._nan_idxs,
np.array([], dtype=np.intp))
idx = pd.TimedeltaIndex(['1 days', 'NaT'])
assert idx._can_hold_na
tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
assert idx.hasnans is True
tm.assert_numpy_array_equal(idx._nan_idxs,
np.array([1], dtype=np.intp))
示例9: test_map_dictlike
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_map_dictlike(self, mapper):
expected = self.index + self.index.freq
# don't compare the freqs
if isinstance(expected, pd.DatetimeIndex):
expected.freq = None
result = self.index.map(mapper(expected, self.index))
tm.assert_index_equal(result, expected)
expected = pd.Index([pd.NaT] + self.index[1:].tolist())
result = self.index.map(mapper(expected, self.index))
tm.assert_index_equal(result, expected)
# empty map; these map to np.nan because we cannot know
# to re-infer things
expected = pd.Index([np.nan] * len(self.index))
result = self.index.map(mapper([], []))
tm.assert_index_equal(result, expected)
示例10: test_index_groupby
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_index_groupby(self):
int_idx = Index(range(6))
float_idx = Index(np.arange(0, 0.6, 0.1))
obj_idx = Index('A B C D E F'.split())
dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)
for idx in [int_idx, float_idx, obj_idx, dt_idx]:
to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
tm.assert_dict_equal(idx.groupby(to_groupby),
{1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})
to_groupby = Index([datetime(2011, 11, 1),
datetime(2011, 12, 1),
pd.NaT,
pd.NaT,
datetime(2011, 12, 1),
datetime(2011, 11, 1)],
tz='UTC').values
ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
expected = {ex_keys[0]: idx[[0, 5]],
ex_keys[1]: idx[[1, 4]]}
tm.assert_dict_equal(idx.groupby(to_groupby), expected)
示例11: test_where_other
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_where_other(self):
# other is ndarray or Index
i = pd.date_range('20130101', periods=3, tz='US/Eastern')
for arr in [np.nan, pd.NaT]:
result = i.where(notna(i), other=np.nan)
expected = i
tm.assert_index_equal(result, expected)
i2 = i.copy()
i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
result = i.where(notna(i2), i2)
tm.assert_index_equal(result, i2)
i2 = i.copy()
i2 = Index([pd.NaT, pd.NaT] + i[2:].tolist())
result = i.where(notna(i2), i2.values)
tm.assert_index_equal(result, i2)
示例12: test_categorical_preserves_tz
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_categorical_preserves_tz(self):
# GH#18664 retain tz when going DTI-->Categorical-->DTI
# TODO: parametrize over DatetimeIndex/DatetimeArray
# once CategoricalIndex(DTA) works
dti = pd.DatetimeIndex(
[pd.NaT, '2015-01-01', '1999-04-06 15:14:13', '2015-01-01'],
tz='US/Eastern')
ci = pd.CategoricalIndex(dti)
carr = pd.Categorical(dti)
cser = pd.Series(ci)
for obj in [ci, carr, cser]:
result = pd.DatetimeIndex(obj)
tm.assert_index_equal(result, dti)
示例13: test_dti_tz_localize_nonexistent_raise_coerce
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_dti_tz_localize_nonexistent_raise_coerce(self):
# GH#13057
times = ['2015-03-08 01:00', '2015-03-08 02:00', '2015-03-08 03:00']
index = DatetimeIndex(times)
tz = 'US/Eastern'
with pytest.raises(pytz.NonExistentTimeError):
index.tz_localize(tz=tz)
with pytest.raises(pytz.NonExistentTimeError):
with tm.assert_produces_warning(FutureWarning):
index.tz_localize(tz=tz, errors='raise')
with tm.assert_produces_warning(FutureWarning,
clear=FutureWarning,
check_stacklevel=False):
result = index.tz_localize(tz=tz, errors='coerce')
test_times = ['2015-03-08 01:00-05:00', 'NaT',
'2015-03-08 03:00-04:00']
dti = to_datetime(test_times, utc=True)
expected = dti.tz_convert('US/Eastern')
tm.assert_index_equal(result, expected)
示例14: test_datetime_outofbounds_scalar
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_datetime_outofbounds_scalar(self, value, format, infer):
# GH24763
res = pd.to_datetime(value, errors='ignore', format=format,
infer_datetime_format=infer)
assert res == value
res = pd.to_datetime(value, errors='coerce', format=format,
infer_datetime_format=infer)
assert res is pd.NaT
if format is not None:
with pytest.raises(ValueError):
pd.to_datetime(value, errors='raise', format=format,
infer_datetime_format=infer)
else:
with pytest.raises(OutOfBoundsDatetime):
pd.to_datetime(value, errors='raise', format=format,
infer_datetime_format=infer)
示例15: test_iso_8601_strings_with_different_offsets
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import NaT [as 別名]
def test_iso_8601_strings_with_different_offsets(self):
# GH 17697, 11736
ts_strings = ["2015-11-18 15:30:00+05:30",
"2015-11-18 16:30:00+06:30",
NaT]
result = to_datetime(ts_strings)
expected = np.array([datetime(2015, 11, 18, 15, 30,
tzinfo=tzoffset(None, 19800)),
datetime(2015, 11, 18, 16, 30,
tzinfo=tzoffset(None, 23400)),
NaT],
dtype=object)
# GH 21864
expected = Index(expected)
tm.assert_index_equal(result, expected)
result = to_datetime(ts_strings, utc=True)
expected = DatetimeIndex([Timestamp(2015, 11, 18, 10),
Timestamp(2015, 11, 18, 10),
NaT], tz='UTC')
tm.assert_index_equal(result, expected)