DOC: update the pandas.Series.shift docstring

Author: ZackStone

pandas/core/generic.py

@@ -8012,26 +8012,93 @@ def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None,
                           errors=errors)
 
     _shared_docs['shift'] = ("""
-        Shift index by desired number of periods with an optional time freq
+        Shift index by desired number of periods with an optional time `freq`.
+
+        When `freq` is not passed, shift the index without realigning the data.
+        If `freq` is passed (in this case, the index must be date or datetime,
+        or it will raises a `NotImplementedError`), the index will be
+        increased using the periods and the `freq`.
 
         Parameters
         ----------
         periods : int
-            Number of periods to move, can be positive or negative
+            Number of periods to shift; can be positive or negative.
         freq : DateOffset, timedelta, or time rule string, optional
             Increment to use from the tseries module or time rule (e.g. 'EOM').
-            See Notes.
-        axis : %(axes_single_arg)s
-
-        Notes
-        -----
-        If freq is specified then the index values are shifted but the data
-        is not realigned. That is, use freq if you would like to extend the
-        index when shifting and preserve the original data.
+            If `freq` is specified then the index values are shifted but the
+            data is not realigned. That is, use `freq` if you would like to
+            extend the index when shifting and preserve the original data.
+        axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
+            Shift direction.
 
         Returns
         -------
-        shifted : %(klass)s
+        %(klass)s
+            Copy of input object, shifted.
+
+        See Also
+        --------
+        tshift : Shift the time index, using the index’s frequency if
+            available.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'Col1': [10, 20, 15, 30, 45],
+        ...                    'Col2': [13, 23, 18, 33, 48],
+        ...                    'Col3': [17, 27, 22, 37, 52]})
+
+        >>> df.shift(periods=3)
+           Col1  Col2  Col3
+        0   NaN   NaN   NaN
+        1   NaN   NaN   NaN
+        2   NaN   NaN   NaN
+        3  10.0  13.0  17.0
+        4  20.0  23.0  27.0
+
+        >>> df.shift(periods=1, axis=1)
+           Col1  Col2  Col3
+        0   NaN  10.0  13.0
+        1   NaN  20.0  23.0
+        2   NaN  15.0  18.0
+        3   NaN  30.0  33.0
+        4   NaN  45.0  48.0
+
+        **freq parameter**
+
+        When using the freq parameter with a :class:pandas.DateTimeIndex
+        the index values are shifted but the data is not realigned.
+
+        >>> names = ['Joaquim', 'Maria', 'Emanuel', 'Jussara', 'Geraldo']
+        >>> dates = pd.date_range(start='2018-03-05 11:15:00',
+        ...                       freq='5D', periods=5)
+        >>> df = pd.DataFrame(data={'names': names}, index=dates)
+        >>> df
+                               names
+        2018-03-05 11:15:00  Joaquim
+        2018-03-10 11:15:00    Maria
+        2018-03-15 11:15:00  Emanuel
+        2018-03-20 11:15:00  Jussara
+        2018-03-25 11:15:00  Geraldo
+
+        Shift the index by 2 days (offset-alias 'D').
+
+        >>> df.shift(periods=2, freq='D')
+                               names
+        2018-03-07 11:15:00  Joaquim
+        2018-03-12 11:15:00    Maria
+        2018-03-17 11:15:00  Emanuel
+        2018-03-22 11:15:00  Jussara
+        2018-03-27 11:15:00  Geraldo
+
+        Shift the index by 75 minutes (offset-alias 'min').
+
+        >>> df.shift(periods=75, freq='min')
+                               names
+        2018-03-05 12:30:00  Joaquim
+        2018-03-10 12:30:00    Maria
+        2018-03-15 12:30:00  Emanuel
+        2018-03-20 12:30:00  Jussara
+        2018-03-25 12:30:00  Geraldo
     """)
 
     @Appender(_shared_docs['shift'] % _shared_doc_kwargs)