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jbrockmendeljbrockmendel
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update doctests
1 parent a16175c commit c49c186

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+29
-29
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10 files changed

+29
-29
lines changed

‎pandas/core/algorithms.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -370,7 +370,7 @@ def unique(values):
370370
array([2, 1])
371371
372372
>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
373-
array(['2016-01-01T00:00:00'], dtype='datetime64[s]')
373+
array(['2016-01-01T00:00:00.000000'], dtype='datetime64[us]')
374374
375375
>>> pd.unique(
376376
... pd.Series(

‎pandas/core/arrays/datetimelike.py‎

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1912,11 +1912,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19121912
19131913
>>> rng_tz.floor("2h", ambiguous=False)
19141914
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
1915-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1915+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19161916
19171917
>>> rng_tz.floor("2h", ambiguous=True)
19181918
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
1919-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1919+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19201920
"""
19211921

19221922
_floor_example = """>>> rng.floor('h')
@@ -1939,11 +1939,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19391939
19401940
>>> rng_tz.floor("2h", ambiguous=False)
19411941
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
1942-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1942+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19431943
19441944
>>> rng_tz.floor("2h", ambiguous=True)
19451945
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
1946-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1946+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19471947
"""
19481948

19491949
_ceil_example = """>>> rng.ceil('h')
@@ -1966,11 +1966,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
19661966
19671967
>>> rng_tz.ceil("h", ambiguous=False)
19681968
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
1969-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1969+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19701970
19711971
>>> rng_tz.ceil("h", ambiguous=True)
19721972
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
1973-
dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1973+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
19741974
"""
19751975

19761976

‎pandas/core/arrays/datetimes.py‎

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -220,7 +220,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps):
220220
... )
221221
<DatetimeArray>
222222
['2023-01-01 00:00:00', '2023-01-02 00:00:00']
223-
Length: 2, dtype: datetime64[s]
223+
Length: 2, dtype: datetime64[us]
224224
"""
225225

226226
__module__ = "pandas.arrays"
@@ -612,7 +612,7 @@ def tz(self) -> tzinfo | None:
612612
>>> s
613613
0 2020-01-01 10:00:00+00:00
614614
1 2020-02-01 11:00:00+00:00
615-
dtype: datetime64[s, UTC]
615+
dtype: datetime64[us, UTC]
616616
>>> s.dt.tz
617617
datetime.timezone.utc
618618
@@ -1441,7 +1441,7 @@ def time(self) -> npt.NDArray[np.object_]:
14411441
>>> s
14421442
0 2020-01-01 10:00:00+00:00
14431443
1 2020-02-01 11:00:00+00:00
1444-
dtype: datetime64[s, UTC]
1444+
dtype: datetime64[us, UTC]
14451445
>>> s.dt.time
14461446
0 10:00:00
14471447
1 11:00:00
@@ -1484,7 +1484,7 @@ def timetz(self) -> npt.NDArray[np.object_]:
14841484
>>> s
14851485
0 2020-01-01 10:00:00+00:00
14861486
1 2020-02-01 11:00:00+00:00
1487-
dtype: datetime64[s, UTC]
1487+
dtype: datetime64[us, UTC]
14881488
>>> s.dt.timetz
14891489
0 10:00:00+00:00
14901490
1 11:00:00+00:00
@@ -1526,7 +1526,7 @@ def date(self) -> npt.NDArray[np.object_]:
15261526
>>> s
15271527
0 2020-01-01 10:00:00+00:00
15281528
1 2020-02-01 11:00:00+00:00
1529-
dtype: datetime64[s, UTC]
1529+
dtype: datetime64[us, UTC]
15301530
>>> s.dt.date
15311531
0 2020-01-01
15321532
1 2020-02-01
@@ -1875,7 +1875,7 @@ def isocalendar(self) -> DataFrame:
18751875
>>> s
18761876
0 2020-01-01 10:00:00+00:00
18771877
1 2020-02-01 11:00:00+00:00
1878-
dtype: datetime64[s, UTC]
1878+
dtype: datetime64[us, UTC]
18791879
>>> s.dt.dayofyear
18801880
0 1
18811881
1 32
@@ -1911,7 +1911,7 @@ def isocalendar(self) -> DataFrame:
19111911
>>> s
19121912
0 2020-01-01 10:00:00+00:00
19131913
1 2020-04-01 11:00:00+00:00
1914-
dtype: datetime64[s, UTC]
1914+
dtype: datetime64[us, UTC]
19151915
>>> s.dt.quarter
19161916
0 1
19171917
1 2
@@ -1947,7 +1947,7 @@ def isocalendar(self) -> DataFrame:
19471947
>>> s
19481948
0 2020-01-01 10:00:00+00:00
19491949
1 2020-02-01 11:00:00+00:00
1950-
dtype: datetime64[s, UTC]
1950+
dtype: datetime64[us, UTC]
19511951
>>> s.dt.daysinmonth
19521952
0 31
19531953
1 29

‎pandas/core/base.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1380,7 +1380,7 @@ def factorize(
13801380
0 2000-03-11
13811381
1 2000-03-12
13821382
2 2000-03-13
1383-
dtype: datetime64[s]
1383+
dtype: datetime64[us]
13841384
13851385
>>> ser.searchsorted('3/14/2000')
13861386
np.int64(3)

‎pandas/core/dtypes/missing.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -150,7 +150,7 @@ def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
150150
>>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None, "2017-07-08"])
151151
>>> index
152152
DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
153-
dtype='datetime64[s]', freq=None)
153+
dtype='datetime64[us]', freq=None)
154154
>>> pd.isna(index)
155155
array([False, False, True, False])
156156
@@ -365,7 +365,7 @@ def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
365365
>>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None, "2017-07-08"])
366366
>>> index
367367
DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
368-
dtype='datetime64[s]', freq=None)
368+
dtype='datetime64[us]', freq=None)
369369
>>> pd.notna(index)
370370
array([ True, True, False, True])
371371

‎pandas/core/generic.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6319,7 +6319,7 @@ def dtypes(self):
63196319
>>> df.dtypes
63206320
float float64
63216321
int int64
6322-
datetime datetime64[s]
6322+
datetime datetime64[us]
63236323
string str
63246324
dtype: object
63256325
"""

‎pandas/core/groupby/generic.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1444,7 +1444,7 @@ def idxmin(self, skipna: bool = True) -> Series:
14441444
>>> ser.groupby(["a", "a", "b", "b"]).idxmin()
14451445
a 2023-01-01
14461446
b 2023-02-01
1447-
dtype: datetime64[s]
1447+
dtype: datetime64[us]
14481448
"""
14491449
return self._idxmax_idxmin("idxmin", skipna=skipna)
14501450

@@ -1505,7 +1505,7 @@ def idxmax(self, skipna: bool = True) -> Series:
15051505
>>> ser.groupby(["a", "a", "b", "b"]).idxmax()
15061506
a 2023-01-15
15071507
b 2023-02-15
1508-
dtype: datetime64[s]
1508+
dtype: datetime64[us]
15091509
"""
15101510
return self._idxmax_idxmin("idxmax", skipna=skipna)
15111511

‎pandas/core/indexes/datetimes.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -252,7 +252,7 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
252252
>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
253253
>>> idx
254254
DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
255-
dtype='datetime64[s, UTC]', freq=None)
255+
dtype='datetime64[us, UTC]', freq=None)
256256
"""
257257

258258
_typ = "datetimeindex"

‎pandas/core/series.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -2134,14 +2134,14 @@ def unique(self) -> ArrayLike:
21342134
>>> pd.Series([pd.Timestamp("2016-01-01") for _ in range(3)]).unique()
21352135
<DatetimeArray>
21362136
['2016-01-01 00:00:00']
2137-
Length: 1, dtype: datetime64[s]
2137+
Length: 1, dtype: datetime64[us]
21382138
21392139
>>> pd.Series(
21402140
... [pd.Timestamp("2016-01-01", tz="US/Eastern") for _ in range(3)]
21412141
... ).unique()
21422142
<DatetimeArray>
21432143
['2016-01-01 00:00:00-05:00']
2144-
Length: 1, dtype: datetime64[s, US/Eastern]
2144+
Length: 1, dtype: datetime64[us, US/Eastern]
21452145
21462146
An Categorical will return categories in the order of
21472147
appearance and with the same dtype.

‎pandas/core/tools/datetimes.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -881,7 +881,7 @@ def to_datetime(
881881
>>> pd.to_datetime(df)
882882
0 2015-02-04
883883
1 2016-03-05
884-
dtype: datetime64[s]
884+
dtype: datetime64[us]
885885
886886
Using a unix epoch time
887887
@@ -924,14 +924,14 @@ def to_datetime(
924924
925925
>>> pd.to_datetime(["2018-10-26 12:00:00", "2018-10-26 13:00:15"])
926926
DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
927-
dtype='datetime64[s]', freq=None)
927+
dtype='datetime64[us]', freq=None)
928928
929929
- Timezone-aware inputs *with constant time offset* are converted to
930930
timezone-aware :class:`DatetimeIndex`:
931931
932932
>>> pd.to_datetime(["2018-10-26 12:00 -0500", "2018-10-26 13:00 -0500"])
933933
DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
934-
dtype='datetime64[s, UTC-05:00]', freq=None)
934+
dtype='datetime64[us, UTC-05:00]', freq=None)
935935
936936
- However, timezone-aware inputs *with mixed time offsets* (for example
937937
issued from a timezone with daylight savings, such as Europe/Paris)
@@ -973,14 +973,14 @@ def to_datetime(
973973
974974
>>> pd.to_datetime(["2018-10-26 12:00", "2018-10-26 13:00"], utc=True)
975975
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
976-
dtype='datetime64[s, UTC]', freq=None)
976+
dtype='datetime64[us, UTC]', freq=None)
977977
978978
- Timezone-aware inputs are *converted* to UTC (the output represents the
979979
exact same datetime, but viewed from the UTC time offset `+00:00`).
980980
981981
>>> pd.to_datetime(["2018-10-26 12:00 -0530", "2018-10-26 12:00 -0500"], utc=True)
982982
DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
983-
dtype='datetime64[s, UTC]', freq=None)
983+
dtype='datetime64[us, UTC]', freq=None)
984984
985985
- Inputs can contain both string or datetime, the above
986986
rules still apply

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