update doctests

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -1649,7 +1649,7 @@ def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
         >>> idx = pd.date_range("2001-01-01 00:00", periods=3)
         >>> idx
         DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'],
-                      dtype='datetime64[ns]', freq='D')
+                      dtype='datetime64[us]', freq='D')
         >>> idx.mean()
         Timestamp('2001-01-02 00:00:00')
 
@@ -2018,7 +2018,7 @@ def freq(self):
                        '2022-02-22 06:22:22-06:00', '2022-02-22 07:22:22-06:00',
                        '2022-02-22 08:22:22-06:00', '2022-02-22 09:22:22-06:00',
                        '2022-02-22 10:22:22-06:00', '2022-02-22 11:22:22-06:00'],
-                      dtype='datetime64[ns, America/Chicago]', freq='h')
+                      dtype='datetime64[us, America/Chicago]', freq='h')
         >>> datetimeindex.freq
         <Hour>
         """

pandas/core/arrays/datetimes.py

@@ -1809,7 +1809,7 @@ def isocalendar(self) -> DataFrame:
         0   2000-01-01 00:00:00.000000000
         1   2000-01-01 00:00:00.000000001
         2   2000-01-01 00:00:00.000000002
-        dtype: datetime64[us]
+        dtype: datetime64[ns]
         >>> datetime_series.dt.nanosecond
         0       0
         1       1

pandas/core/generic.py

@@ -6446,7 +6446,7 @@ def astype(
         0   2020-01-01
         1   2020-01-02
         2   2020-01-03
-        dtype: datetime64[ns]
+        dtype: datetime64[us]
         """
         self._check_copy_deprecation(copy)
         if is_dict_like(dtype):

pandas/core/indexes/accessors.py

@@ -286,7 +286,7 @@ class DatetimeProperties(Properties):
     0   2000-01-01 00:00:00
     1   2000-01-01 00:00:01
     2   2000-01-01 00:00:02
-    dtype: datetime64[ns]
+    dtype: datetime64[us]
     >>> seconds_series.dt.second
     0    0
     1    1
@@ -298,7 +298,7 @@ class DatetimeProperties(Properties):
     0   2000-01-01 00:00:00
     1   2000-01-01 01:00:00
     2   2000-01-01 02:00:00
-    dtype: datetime64[ns]
+    dtype: datetime64[us]
     >>> hours_series.dt.hour
     0    0
     1    1
@@ -310,7 +310,7 @@ class DatetimeProperties(Properties):
     0   2000-03-31
     1   2000-06-30
     2   2000-09-30
-    dtype: datetime64[ns]
+    dtype: datetime64[us]
     >>> quarters_series.dt.quarter
     0    1
     1    2
@@ -347,7 +347,7 @@ def to_pydatetime(self) -> Series:
         >>> s
         0   2018-03-10
         1   2018-03-11
-        dtype: datetime64[ns]
+        dtype: datetime64[us]
 
         >>> s.dt.to_pydatetime()
         0    2018-03-10 00:00:00
@@ -360,7 +360,7 @@ def to_pydatetime(self) -> Series:
         >>> s
         0   2018-03-10 00:00:00.000000000
         1   2018-03-10 00:00:00.000000001
-        dtype: datetime64[ns]
+        dtype: datetime64[us]
 
         >>> s.dt.to_pydatetime()
         0    2018-03-10 00:00:00

pandas/core/indexes/datetimes.py

@@ -845,7 +845,7 @@ def indexer_between_time(
         >>> idx
         DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
                            '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
-                          dtype='datetime64[ns]', freq='h')
+                          dtype='datetime64[us]', freq='h')
         >>> idx.indexer_between_time("00:00", "2:00", include_end=False)
         array([0, 1])
         """
@@ -970,7 +970,7 @@ def date_range(
     >>> pd.date_range(start="1/1/2018", end="1/08/2018")
     DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                    '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     Specify timezone-aware `start` and `end`, with the default daily frequency.
 
@@ -982,29 +982,29 @@ def date_range(
                    '2018-01-03 00:00:00+01:00', '2018-01-04 00:00:00+01:00',
                    '2018-01-05 00:00:00+01:00', '2018-01-06 00:00:00+01:00',
                    '2018-01-07 00:00:00+01:00', '2018-01-08 00:00:00+01:00'],
-                  dtype='datetime64[ns, Europe/Berlin]', freq='D')
+                  dtype='datetime64[us, Europe/Berlin]', freq='D')
 
     Specify `start` and `periods`, the number of periods (days).
 
     >>> pd.date_range(start="1/1/2018", periods=8)
     DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                    '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     Specify `end` and `periods`, the number of periods (days).
 
     >>> pd.date_range(end="1/1/2018", periods=8)
     DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
                    '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     Specify `start`, `end`, and `periods`; the frequency is generated
     automatically (linearly spaced).
 
     >>> pd.date_range(start="2018-04-24", end="2018-04-27", periods=3)
     DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
                    '2018-04-27 00:00:00'],
-                  dtype='datetime64[ns]', freq=None)
+                  dtype='datetime64[us]', freq=None)
 
     **Other Parameters**
 
@@ -1013,49 +1013,49 @@ def date_range(
     >>> pd.date_range(start="1/1/2018", periods=5, freq="ME")
     DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
                    '2018-05-31'],
-                  dtype='datetime64[ns]', freq='ME')
+                  dtype='datetime64[us]', freq='ME')
 
     Multiples are allowed
 
     >>> pd.date_range(start="1/1/2018", periods=5, freq="3ME")
     DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                    '2019-01-31'],
-                  dtype='datetime64[ns]', freq='3ME')
+                  dtype='datetime64[us]', freq='3ME')
 
     `freq` can also be specified as an Offset object.
 
     >>> pd.date_range(start="1/1/2018", periods=5, freq=pd.offsets.MonthEnd(3))
     DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                    '2019-01-31'],
-                  dtype='datetime64[ns]', freq='3ME')
+                  dtype='datetime64[us]', freq='3ME')
 
     Specify `tz` to set the timezone.
 
     >>> pd.date_range(start="1/1/2018", periods=5, tz="Asia/Tokyo")
     DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
                    '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
                    '2018-01-05 00:00:00+09:00'],
-                  dtype='datetime64[ns, Asia/Tokyo]', freq='D')
+                  dtype='datetime64[us, Asia/Tokyo]', freq='D')
 
     `inclusive` controls whether to include `start` and `end` that are on the
     boundary. The default, "both", includes boundary points on either end.
 
     >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="both")
     DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     Use ``inclusive='left'`` to exclude `end` if it falls on the boundary.
 
     >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="left")
     DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     Use ``inclusive='right'`` to exclude `start` if it falls on the boundary, and
     similarly ``inclusive='neither'`` will exclude both `start` and `end`.
 
     >>> pd.date_range(start="2017-01-01", end="2017-01-04", inclusive="right")
     DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
-                  dtype='datetime64[ns]', freq='D')
+                  dtype='datetime64[us]', freq='D')
 
     **Specify a unit**
 
@@ -1202,7 +1202,7 @@ def bdate_range(
     >>> pd.bdate_range(start="1/1/2018", end="1/08/2018")
     DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                '2018-01-05', '2018-01-08'],
-              dtype='datetime64[ns]', freq='B')
+              dtype='datetime64[us]', freq='B')
     """
     if freq is None:
         msg = "freq must be specified for bdate_range; use date_range instead"

pandas/core/indexes/interval.py

@@ -1339,7 +1339,7 @@ def interval_range(
     IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
                    (2017-01-02 00:00:00, 2017-01-03 00:00:00],
                    (2017-01-03 00:00:00, 2017-01-04 00:00:00]],
-                  dtype='interval[datetime64[ns], right]')
+                  dtype='interval[datetime64[us], right]')
 
     The ``freq`` parameter specifies the frequency between the left and right.
     endpoints of the individual intervals within the ``IntervalIndex``.  For
@@ -1356,7 +1356,7 @@ def interval_range(
     IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],
                    (2017-02-01 00:00:00, 2017-03-01 00:00:00],
                    (2017-03-01 00:00:00, 2017-04-01 00:00:00]],
-                  dtype='interval[datetime64[ns], right]')
+                  dtype='interval[datetime64[us], right]')
 
     Specify ``start``, ``end``, and ``periods``; the frequency is generated
     automatically (linearly spaced).

pandas/core/series.py

@@ -777,7 +777,7 @@ def values(self):
         >>> pd.Series(pd.date_range("20130101", periods=3, tz="US/Eastern")).values
         array(['2013-01-01T05:00:00.000000000',
                '2013-01-02T05:00:00.000000000',
-               '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
+               '2013-01-03T05:00:00.000000000'], dtype='datetime64[us]')
         """
         return self._mgr.external_values()