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update doctests
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‎pandas/core/arrays/datetimelike.py‎

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -1900,21 +1900,21 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19001900
>>> rng
19011901
DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
19021902
'2018-01-01 12:01:00'],
1903-
dtype='datetime64[ns]', freq='min')
1903+
dtype='datetime64[us]', freq='min')
19041904
"""
19051905

19061906
_round_example = """>>> rng.round('h')
19071907
DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
19081908
'2018-01-01 12:00:00'],
1909-
dtype='datetime64[ns]', freq=None)
1909+
dtype='datetime64[us]', freq=None)
19101910
19111911
**Series**
19121912
19131913
>>> pd.Series(rng).dt.round("h")
19141914
0 2018-01-01 12:00:00
19151915
1 2018-01-01 12:00:00
19161916
2 2018-01-01 12:00:00
1917-
dtype: datetime64[ns]
1917+
dtype: datetime64[us]
19181918
19191919
When rounding near a daylight savings time transition, use ``ambiguous`` or
19201920
``nonexistent`` to control how the timestamp should be re-localized.
@@ -1933,15 +1933,15 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19331933
_floor_example = """>>> rng.floor('h')
19341934
DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
19351935
'2018-01-01 12:00:00'],
1936-
dtype='datetime64[ns]', freq=None)
1936+
dtype='datetime64[us]', freq=None)
19371937
19381938
**Series**
19391939
19401940
>>> pd.Series(rng).dt.floor("h")
19411941
0 2018-01-01 11:00:00
19421942
1 2018-01-01 12:00:00
19431943
2 2018-01-01 12:00:00
1944-
dtype: datetime64[ns]
1944+
dtype: datetime64[us]
19451945
19461946
When rounding near a daylight savings time transition, use ``ambiguous`` or
19471947
``nonexistent`` to control how the timestamp should be re-localized.
@@ -1960,15 +1960,15 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19601960
_ceil_example = """>>> rng.ceil('h')
19611961
DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
19621962
'2018-01-01 13:00:00'],
1963-
dtype='datetime64[ns]', freq=None)
1963+
dtype='datetime64[us]', freq=None)
19641964
19651965
**Series**
19661966
19671967
>>> pd.Series(rng).dt.ceil("h")
19681968
0 2018-01-01 12:00:00
19691969
1 2018-01-01 12:00:00
19701970
2 2018-01-01 13:00:00
1971-
dtype: datetime64[ns]
1971+
dtype: datetime64[us]
19721972
19731973
When rounding near a daylight savings time transition, use ``ambiguous`` or
19741974
``nonexistent`` to control how the timestamp should be re-localized.

‎pandas/core/arrays/datetimes.py‎

Lines changed: 33 additions & 33 deletions
Original file line numberDiff line numberDiff line change
@@ -912,13 +912,13 @@ def tz_convert(self, tz) -> Self:
912912
DatetimeIndex(['2014-08-01 09:00:00+02:00',
913913
'2014-08-01 10:00:00+02:00',
914914
'2014-08-01 11:00:00+02:00'],
915-
dtype='datetime64[ns, Europe/Berlin]', freq='h')
915+
dtype='datetime64[us, Europe/Berlin]', freq='h')
916916
917917
>>> dti.tz_convert(None)
918918
DatetimeIndex(['2014-08-01 07:00:00',
919919
'2014-08-01 08:00:00',
920920
'2014-08-01 09:00:00'],
921-
dtype='datetime64[ns]', freq='h')
921+
dtype='datetime64[us]', freq='h')
922922
""" # noqa: E501
923923
tz = timezones.maybe_get_tz(tz)
924924

@@ -1009,7 +1009,7 @@ def tz_localize(
10091009
>>> tz_naive
10101010
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
10111011
'2018-03-03 09:00:00'],
1012-
dtype='datetime64[ns]', freq='D')
1012+
dtype='datetime64[us]', freq='D')
10131013
10141014
Localize DatetimeIndex in US/Eastern time zone:
10151015
@@ -1018,15 +1018,15 @@ def tz_localize(
10181018
DatetimeIndex(['2018-03-01 09:00:00-05:00',
10191019
'2018-03-02 09:00:00-05:00',
10201020
'2018-03-03 09:00:00-05:00'],
1021-
dtype='datetime64[ns, US/Eastern]', freq=None)
1021+
dtype='datetime64[us, US/Eastern]', freq=None)
10221022
10231023
With the ``tz=None``, we can remove the time zone information
10241024
while keeping the local time (not converted to UTC):
10251025
10261026
>>> tz_aware.tz_localize(None)
10271027
DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',
10281028
'2018-03-03 09:00:00'],
1029-
dtype='datetime64[ns]', freq=None)
1029+
dtype='datetime64[us]', freq=None)
10301030
10311031
Be careful with DST changes. When there is sequential data, pandas can
10321032
infer the DST time:
@@ -1179,12 +1179,12 @@ def normalize(self) -> Self:
11791179
DatetimeIndex(['2014-08-01 10:00:00+05:30',
11801180
'2014-08-01 11:00:00+05:30',
11811181
'2014-08-01 12:00:00+05:30'],
1182-
dtype='datetime64[ns, Asia/Calcutta]', freq='h')
1182+
dtype='datetime64[us, Asia/Calcutta]', freq='h')
11831183
>>> idx.normalize()
11841184
DatetimeIndex(['2014-08-01 00:00:00+05:30',
11851185
'2014-08-01 00:00:00+05:30',
11861186
'2014-08-01 00:00:00+05:30'],
1187-
dtype='datetime64[ns, Asia/Calcutta]', freq=None)
1187+
dtype='datetime64[us, Asia/Calcutta]', freq=None)
11881188
"""
11891189
new_values = normalize_i8_timestamps(self.asi8, self.tz, reso=self._creso)
11901190
dt64_values = new_values.view(self._ndarray.dtype)
@@ -1308,7 +1308,7 @@ def month_name(self, locale=None) -> npt.NDArray[np.object_]:
13081308
0 2018-01-31
13091309
1 2018-02-28
13101310
2 2018-03-31
1311-
dtype: datetime64[ns]
1311+
dtype: datetime64[us]
13121312
>>> s.dt.month_name()
13131313
0 January
13141314
1 February
@@ -1318,7 +1318,7 @@ def month_name(self, locale=None) -> npt.NDArray[np.object_]:
13181318
>>> idx = pd.date_range(start="2018-01", freq="ME", periods=3)
13191319
>>> idx
13201320
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
1321-
dtype='datetime64[ns]', freq='ME')
1321+
dtype='datetime64[us]', freq='ME')
13221322
>>> idx.month_name()
13231323
Index(['January', 'February', 'March'], dtype='str')
13241324
@@ -1329,7 +1329,7 @@ def month_name(self, locale=None) -> npt.NDArray[np.object_]:
13291329
>>> idx = pd.date_range(start="2018-01", freq="ME", periods=3)
13301330
>>> idx
13311331
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],
1332-
dtype='datetime64[ns]', freq='ME')
1332+
dtype='datetime64[us]', freq='ME')
13331333
>>> idx.month_name(locale="pt_BR.utf8") # doctest: +SKIP
13341334
Index(['Janeiro', 'Fevereiro', 'Março'], dtype='str')
13351335
"""
@@ -1376,7 +1376,7 @@ def day_name(self, locale=None) -> npt.NDArray[np.object_]:
13761376
0 2018-01-01
13771377
1 2018-01-02
13781378
2 2018-01-03
1379-
dtype: datetime64[ns]
1379+
dtype: datetime64[us]
13801380
>>> s.dt.day_name()
13811381
0 Monday
13821382
1 Tuesday
@@ -1386,7 +1386,7 @@ def day_name(self, locale=None) -> npt.NDArray[np.object_]:
13861386
>>> idx = pd.date_range(start="2018-01-01", freq="D", periods=3)
13871387
>>> idx
13881388
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
1389-
dtype='datetime64[ns]', freq='D')
1389+
dtype='datetime64[us]', freq='D')
13901390
>>> idx.day_name()
13911391
Index(['Monday', 'Tuesday', 'Wednesday'], dtype='str')
13921392
@@ -1397,7 +1397,7 @@ def day_name(self, locale=None) -> npt.NDArray[np.object_]:
13971397
>>> idx = pd.date_range(start="2018-01-01", freq="D", periods=3)
13981398
>>> idx
13991399
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],
1400-
dtype='datetime64[ns]', freq='D')
1400+
dtype='datetime64[us]', freq='D')
14011401
>>> idx.day_name(locale="pt_BR.utf8") # doctest: +SKIP
14021402
Index(['Segunda', 'Terça', 'Quarta'], dtype='str')
14031403
"""
@@ -1610,7 +1610,7 @@ def isocalendar(self) -> DataFrame:
16101610
0 2000-12-31
16111611
1 2001-12-31
16121612
2 2002-12-31
1613-
dtype: datetime64[ns]
1613+
dtype: datetime64[us]
16141614
>>> datetime_series.dt.year
16151615
0 2000
16161616
1 2001
@@ -1638,7 +1638,7 @@ def isocalendar(self) -> DataFrame:
16381638
0 2000-01-31
16391639
1 2000-02-29
16401640
2 2000-03-31
1641-
dtype: datetime64[ns]
1641+
dtype: datetime64[us]
16421642
>>> datetime_series.dt.month
16431643
0 1
16441644
1 2
@@ -1667,7 +1667,7 @@ def isocalendar(self) -> DataFrame:
16671667
0 2000-01-01
16681668
1 2000-01-02
16691669
2 2000-01-03
1670-
dtype: datetime64[ns]
1670+
dtype: datetime64[us]
16711671
>>> datetime_series.dt.day
16721672
0 1
16731673
1 2
@@ -1696,7 +1696,7 @@ def isocalendar(self) -> DataFrame:
16961696
0 2000-01-01 00:00:00
16971697
1 2000-01-01 01:00:00
16981698
2 2000-01-01 02:00:00
1699-
dtype: datetime64[ns]
1699+
dtype: datetime64[us]
17001700
>>> datetime_series.dt.hour
17011701
0 0
17021702
1 1
@@ -1724,7 +1724,7 @@ def isocalendar(self) -> DataFrame:
17241724
0 2000-01-01 00:00:00
17251725
1 2000-01-01 00:01:00
17261726
2 2000-01-01 00:02:00
1727-
dtype: datetime64[ns]
1727+
dtype: datetime64[us]
17281728
>>> datetime_series.dt.minute
17291729
0 0
17301730
1 1
@@ -1753,7 +1753,7 @@ def isocalendar(self) -> DataFrame:
17531753
0 2000-01-01 00:00:00
17541754
1 2000-01-01 00:00:01
17551755
2 2000-01-01 00:00:02
1756-
dtype: datetime64[ns]
1756+
dtype: datetime64[us]
17571757
>>> datetime_series.dt.second
17581758
0 0
17591759
1 1
@@ -1781,7 +1781,7 @@ def isocalendar(self) -> DataFrame:
17811781
0 2000-01-01 00:00:00.000000
17821782
1 2000-01-01 00:00:00.000001
17831783
2 2000-01-01 00:00:00.000002
1784-
dtype: datetime64[ns]
1784+
dtype: datetime64[us]
17851785
>>> datetime_series.dt.microsecond
17861786
0 0
17871787
1 1
@@ -1809,7 +1809,7 @@ def isocalendar(self) -> DataFrame:
18091809
0 2000-01-01 00:00:00.000000000
18101810
1 2000-01-01 00:00:00.000000001
18111811
2 2000-01-01 00:00:00.000000002
1812-
dtype: datetime64[ns]
1812+
dtype: datetime64[us]
18131813
>>> datetime_series.dt.nanosecond
18141814
0 0
18151815
1 1
@@ -1981,7 +1981,7 @@ def isocalendar(self) -> DataFrame:
19811981
0 2018-02-27
19821982
1 2018-02-28
19831983
2 2018-03-01
1984-
dtype: datetime64[ns]
1984+
dtype: datetime64[us]
19851985
>>> s.dt.is_month_start
19861986
0 False
19871987
1 False
@@ -2043,7 +2043,7 @@ def isocalendar(self) -> DataFrame:
20432043
>>> idx = pd.date_range('2017-03-30', periods=4)
20442044
>>> idx
20452045
DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
2046-
dtype='datetime64[ns]', freq='D')
2046+
dtype='datetime64[us]', freq='D')
20472047
20482048
>>> idx.is_quarter_start
20492049
array([False, False, True, False])
@@ -2085,7 +2085,7 @@ def isocalendar(self) -> DataFrame:
20852085
>>> idx = pd.date_range('2017-03-30', periods=4)
20862086
>>> idx
20872087
DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],
2088-
dtype='datetime64[ns]', freq='D')
2088+
dtype='datetime64[us]', freq='D')
20892089
20902090
>>> idx.is_quarter_end
20912091
array([False, True, False, False])
@@ -2118,7 +2118,7 @@ def isocalendar(self) -> DataFrame:
21182118
0 2017-12-30
21192119
1 2017-12-31
21202120
2 2018-01-01
2121-
dtype: datetime64[ns]
2121+
dtype: datetime64[us]
21222122
21232123
>>> dates.dt.is_year_start
21242124
0 False
@@ -2129,7 +2129,7 @@ def isocalendar(self) -> DataFrame:
21292129
>>> idx = pd.date_range("2017-12-30", periods=3)
21302130
>>> idx
21312131
DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
2132-
dtype='datetime64[ns]', freq='D')
2132+
dtype='datetime64[us]', freq='D')
21332133
21342134
>>> idx.is_year_start
21352135
array([False, False, True])
@@ -2143,7 +2143,7 @@ def isocalendar(self) -> DataFrame:
21432143
1 2022-01-03
21442144
2 2023-01-02
21452145
3 2024-01-01
2146-
dtype: datetime64[ns]
2146+
dtype: datetime64[us]
21472147
21482148
>>> dates.dt.is_year_start
21492149
0 True
@@ -2155,7 +2155,7 @@ def isocalendar(self) -> DataFrame:
21552155
>>> idx = pd.date_range("2020-10-30", periods=4, freq="BYS")
21562156
>>> idx
21572157
DatetimeIndex(['2021-01-01', '2022-01-03', '2023-01-02', '2024-01-01'],
2158-
dtype='datetime64[ns]', freq='BYS-JAN')
2158+
dtype='datetime64[us]', freq='BYS-JAN')
21592159
21602160
>>> idx.is_year_start
21612161
array([ True, True, True, True])
@@ -2188,7 +2188,7 @@ def isocalendar(self) -> DataFrame:
21882188
0 2017-12-30
21892189
1 2017-12-31
21902190
2 2018-01-01
2191-
dtype: datetime64[ns]
2191+
dtype: datetime64[us]
21922192
21932193
>>> dates.dt.is_year_end
21942194
0 False
@@ -2199,7 +2199,7 @@ def isocalendar(self) -> DataFrame:
21992199
>>> idx = pd.date_range("2017-12-30", periods=3)
22002200
>>> idx
22012201
DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],
2202-
dtype='datetime64[ns]', freq='D')
2202+
dtype='datetime64[us]', freq='D')
22032203
22042204
>>> idx.is_year_end
22052205
array([False, True, False])
@@ -2236,7 +2236,7 @@ def isocalendar(self) -> DataFrame:
22362236
>>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="YE")
22372237
>>> idx
22382238
DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
2239-
dtype='datetime64[ns]', freq='YE-DEC')
2239+
dtype='datetime64[us]', freq='YE-DEC')
22402240
>>> idx.is_leap_year
22412241
array([ True, False, False])
22422242
@@ -2245,7 +2245,7 @@ def isocalendar(self) -> DataFrame:
22452245
0 2012-12-31
22462246
1 2013-12-31
22472247
2 2014-12-31
2248-
dtype: datetime64[ns]
2248+
dtype: datetime64[us]
22492249
>>> dates_series.dt.is_leap_year
22502250
0 True
22512251
1 False
@@ -2379,7 +2379,7 @@ def std(
23792379
>>> idx = pd.date_range("2001-01-01 00:00", periods=3)
23802380
>>> idx
23812381
DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
2382-
dtype='datetime64[ns]', freq='D')
2382+
dtype='datetime64[us]', freq='D')
23832383
>>> idx.std()
23842384
Timedelta('1 days 00:00:00')
23852385
"""

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