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jorisvandenbosschejorisvandenbossche
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update docstrings
1 parent 991c986 commit e3d7b64

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‎pandas/core/algorithms.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -355,7 +355,7 @@ def unique(values):
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array([2, 1])
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>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
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array(['2016-01-01T00:00:00'], dtype='datetime64[s]')
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array(['2016-01-01T00:00:00.000000'], dtype='datetime64[us]')
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>>> pd.unique(
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... pd.Series(

‎pandas/core/arrays/datetimelike.py‎

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1906,11 +1906,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
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>>> rng_tz.floor("2h", ambiguous=False)
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DatetimeIndex(['2021-10-31 02:00:00+01:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
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dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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>>> rng_tz.floor("2h", ambiguous=True)
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DatetimeIndex(['2021-10-31 02:00:00+02:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
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dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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"""
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_floor_example = """>>> rng.floor('h')
@@ -1933,11 +1933,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
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>>> rng_tz.floor("2h", ambiguous=False)
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DatetimeIndex(['2021-10-31 02:00:00+01:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
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dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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>>> rng_tz.floor("2h", ambiguous=True)
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DatetimeIndex(['2021-10-31 02:00:00+02:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
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dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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"""
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_ceil_example = """>>> rng.ceil('h')
@@ -1960,11 +1960,11 @@ def strftime(self, date_format: str) -> npt.NDArray[np.object_]:
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>>> rng_tz.ceil("h", ambiguous=False)
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DatetimeIndex(['2021-10-31 02:00:00+01:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
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dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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>>> rng_tz.ceil("h", ambiguous=True)
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DatetimeIndex(['2021-10-31 02:00:00+02:00'],
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dtype='datetime64[s, Europe/Amsterdam]', freq=None)
1967+
dtype='datetime64[us, Europe/Amsterdam]', freq=None)
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"""
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‎pandas/core/arrays/datetimes.py‎

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -220,7 +220,7 @@ class DatetimeArray(dtl.TimelikeOps, dtl.DatelikeOps): # type: ignore[misc]
220220
... )
221221
<DatetimeArray>
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['2023-01-01 00:00:00', '2023-01-02 00:00:00']
223-
Length: 2, dtype: datetime64[s]
223+
Length: 2, dtype: datetime64[us]
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"""
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_typ = "datetimearray"
@@ -614,7 +614,7 @@ def tz(self) -> tzinfo | None:
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>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.tz
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datetime.timezone.utc
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@@ -1044,7 +1044,7 @@ def tz_localize(
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4 2018-10-28 02:30:00+01:00
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5 2018-10-28 03:00:00+01:00
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6 2018-10-28 03:30:00+01:00
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dtype: datetime64[s, CET]
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dtype: datetime64[us, CET]
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In some cases, inferring the DST is impossible. In such cases, you can
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pass an ndarray to the ambiguous parameter to set the DST explicitly
@@ -1056,7 +1056,7 @@ def tz_localize(
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0 2018-10-28 01:20:00+02:00
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1 2018-10-28 02:36:00+02:00
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2 2018-10-28 03:46:00+01:00
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dtype: datetime64[s, CET]
1059+
dtype: datetime64[us, CET]
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If the DST transition causes nonexistent times, you can shift these
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dates forward or backwards with a timedelta object or `'shift_forward'`
@@ -1439,7 +1439,7 @@ def time(self) -> npt.NDArray[np.object_]:
14391439
>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.time
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0 10:00:00
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1 11:00:00
@@ -1482,7 +1482,7 @@ def timetz(self) -> npt.NDArray[np.object_]:
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>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
1485-
dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.timetz
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0 10:00:00+00:00
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1 11:00:00+00:00
@@ -1524,7 +1524,7 @@ def date(self) -> npt.NDArray[np.object_]:
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>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.date
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0 2020-01-01
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1 2020-02-01
@@ -1873,7 +1873,7 @@ def isocalendar(self) -> DataFrame:
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>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.dayofyear
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0 1
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1 32
@@ -1909,7 +1909,7 @@ def isocalendar(self) -> DataFrame:
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>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-04-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.quarter
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0 1
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1 2
@@ -1945,7 +1945,7 @@ def isocalendar(self) -> DataFrame:
19451945
>>> s
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0 2020-01-01 10:00:00+00:00
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1 2020-02-01 11:00:00+00:00
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dtype: datetime64[s, UTC]
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dtype: datetime64[us, UTC]
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>>> s.dt.daysinmonth
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0 31
19511951
1 29

‎pandas/core/base.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1376,7 +1376,7 @@ def factorize(
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0 2000-03-11
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1 2000-03-12
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2 2000-03-13
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dtype: datetime64[s]
1379+
dtype: datetime64[us]
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>>> ser.searchsorted('3/14/2000')
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np.int64(3)

‎pandas/core/dtypes/missing.py‎

Lines changed: 1 addition & 1 deletion
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

‎pandas/core/generic.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -6299,8 +6299,8 @@ def dtypes(self):
62996299
>>> df.dtypes
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float float64
63016301
int int64
6302-
datetime datetime64[s]
6303-
string str
6302+
datetime datetime64[us]
6303+
string str
63046304
dtype: object
63056305
"""
63066306
data = self._mgr.get_dtypes()

‎pandas/core/indexes/datetimes.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -246,7 +246,7 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
246246
>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
247247
>>> idx
248248
DatetimeIndex(['2020-01-01 10:00:00+00:00', '2020-02-01 11:00:00+00:00'],
249-
dtype='datetime64[s, UTC]', freq=None)
249+
dtype='datetime64[us, UTC]', freq=None)
250250
"""
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252252
_typ = "datetimeindex"

‎pandas/core/tools/datetimes.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -878,7 +878,7 @@ def to_datetime(
878878
>>> pd.to_datetime(df)
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0 2015-02-04
880880
1 2016-03-05
881-
dtype: datetime64[s]
881+
dtype: datetime64[us]
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883883
Using a unix epoch time
884884
@@ -921,14 +921,14 @@ def to_datetime(
921921
922922
>>> pd.to_datetime(["2018-10-26 12:00:00", "2018-10-26 13:00:15"])
923923
DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
924-
dtype='datetime64[s]', freq=None)
924+
dtype='datetime64[us]', freq=None)
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926926
- Timezone-aware inputs *with constant time offset* are converted to
927927
timezone-aware :class:`DatetimeIndex`:
928928
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>>> pd.to_datetime(["2018-10-26 12:00 -0500", "2018-10-26 13:00 -0500"])
930930
DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
931-
dtype='datetime64[s, UTC-05:00]', freq=None)
931+
dtype='datetime64[us, UTC-05:00]', freq=None)
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933933
- However, timezone-aware inputs *with mixed time offsets* (for example
934934
issued from a timezone with daylight savings, such as Europe/Paris)
@@ -970,14 +970,14 @@ def to_datetime(
970970
971971
>>> pd.to_datetime(["2018-10-26 12:00", "2018-10-26 13:00"], utc=True)
972972
DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
973-
dtype='datetime64[s, UTC]', freq=None)
973+
dtype='datetime64[us, UTC]', freq=None)
974974
975975
- Timezone-aware inputs are *converted* to UTC (the output represents the
976976
exact same datetime, but viewed from the UTC time offset `+00:00`).
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978978
>>> pd.to_datetime(["2018-10-26 12:00 -0530", "2018-10-26 12:00 -0500"], utc=True)
979979
DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
980-
dtype='datetime64[s, UTC]', freq=None)
980+
dtype='datetime64[us, UTC]', freq=None)
981981
982982
- Inputs can contain both string or datetime, the above
983983
rules still apply

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