update docstrings

Author: jorisvandenbossche

pandas/core/arrays/datetimelike.py

@@ -927,7 +927,7 @@ def inferred_freq(self) -> str | None:
         >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
         >>> tdelta_idx
         TimedeltaIndex(['0 days', '10 days', '20 days'],
-                       dtype='timedelta64[ns]', freq=None)
+                       dtype='timedelta64[us]', freq=None)
         >>> tdelta_idx.inferred_freq
         '10D'
         """

pandas/core/arrays/timedeltas.py

@@ -150,7 +150,7 @@ class TimedeltaArray(dtl.TimelikeOps):
     >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(["1h", "2h"]))
     <TimedeltaArray>
     ['0 days 01:00:00', '0 days 02:00:00']
-    Length: 2, dtype: timedelta64[ns]
+    Length: 2, dtype: timedelta64[us]
     """
 
     _typ = "timedeltaarray"
@@ -813,7 +813,7 @@ def total_seconds(self) -> npt.NDArray[np.float64]:
         >>> idx = pd.to_timedelta(np.arange(5), unit="D")
         >>> idx
         TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
-                       dtype='timedelta64[ns]', freq=None)
+                       dtype='timedelta64[us]', freq=None)
 
         >>> idx.total_seconds()
         Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64')
@@ -892,7 +892,7 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
     >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
     >>> tdelta_idx
     TimedeltaIndex(['0 days', '10 days', '20 days'],
-                    dtype='timedelta64[ns]', freq=None)
+                    dtype='timedelta64[us]', freq=None)
     >>> tdelta_idx.days
     Index([0, 10, 20], dtype='int64')"""
     )

pandas/core/generic.py

@@ -1560,7 +1560,7 @@ def abs(self) -> Self:
         >>> s = pd.Series([pd.Timedelta("1 days")])
         >>> s.abs()
         0   1 days
-        dtype: timedelta64[ns]
+        dtype: timedelta64[us]
 
         Select rows with data closest to certain value using argsort (from
         `StackOverflow <https://stackoverflow.com/a/17758115>`__).

pandas/core/indexes/accessors.py

@@ -453,7 +453,7 @@ class TimedeltaProperties(Properties):
     0   0 days 00:00:01
     1   0 days 00:00:02
     2   0 days 00:00:03
-    dtype: timedelta64[ns]
+    dtype: timedelta64[us]
     >>> seconds_series.dt.seconds
     0    1
     1    2

pandas/core/indexes/datetimelike.py

@@ -691,7 +691,7 @@ def inferred_freq(self) -> str | None:
         >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
         >>> tdelta_idx
         TimedeltaIndex(['0 days', '10 days', '20 days'],
-                       dtype='timedelta64[ns]', freq=None)
+                       dtype='timedelta64[us]', freq=None)
         >>> tdelta_idx.inferred_freq
         '10D'
         """

pandas/core/indexes/timedeltas.py

@@ -122,7 +122,7 @@ class TimedeltaIndex(DatetimeTimedeltaMixin):
     --------
     >>> pd.TimedeltaIndex(["0 days", "1 days", "2 days", "3 days", "4 days"])
     TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
-                   dtype='timedelta64[ns]', freq=None)
+                   dtype='timedelta64[us]', freq=None)
 
     We can also let pandas infer the frequency when possible.
 
@@ -319,14 +319,14 @@ def timedelta_range(
     --------
     >>> pd.timedelta_range(start="1 day", periods=4)
     TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'],
-                   dtype='timedelta64[ns]', freq='D')
+                   dtype='timedelta64[us]', freq='D')
 
     The ``closed`` parameter specifies which endpoint is included.  The default
     behavior is to include both endpoints.
 
     >>> pd.timedelta_range(start="1 day", periods=4, closed="right")
     TimedeltaIndex(['2 days', '3 days', '4 days'],
-                   dtype='timedelta64[ns]', freq='D')
+                   dtype='timedelta64[us]', freq='D')
 
     The ``freq`` parameter specifies the frequency of the TimedeltaIndex.
     Only fixed frequencies can be passed, non-fixed frequencies such as
@@ -335,15 +335,15 @@ def timedelta_range(
     >>> pd.timedelta_range(start="1 day", end="2 days", freq="6h")
     TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
                     '1 days 18:00:00', '2 days 00:00:00'],
-                   dtype='timedelta64[ns]', freq='6h')
+                   dtype='timedelta64[us]', freq='6h')
 
     Specify ``start``, ``end``, and ``periods``; the frequency is generated
     automatically (linearly spaced).
 
     >>> pd.timedelta_range(start="1 day", end="5 days", periods=4)
     TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00',
                     '5 days 00:00:00'],
-                   dtype='timedelta64[ns]', freq=None)
+                   dtype='timedelta64[us]', freq=None)
 
     **Specify a unit**
 

pandas/core/tools/timedeltas.py

@@ -174,7 +174,7 @@ def to_timedelta(
                    dtype='timedelta64[ns]', freq=None)
     >>> pd.to_timedelta(np.arange(5), unit="D")
     TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
-                   dtype='timedelta64[ns]', freq=None)
+                   dtype='timedelta64[us]', freq=None)
     """
     if unit is not None:
         unit = parse_timedelta_unit(unit)