DOC: update the pandas.Series.shift docstring
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- [x] PR title is "DOC: update the <your-function-or-method> docstring"
- [x] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
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Please include the output of the validation script below between the "```" ticks:

```
################################################################################
####################### Docstring (pandas.Series.shift)  #######################
################################################################################

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realign the data.
If freq is passed (in this case, the index must be date or datetime),
the index will be increased using the periods and the freq.

Parameters
----------
periods : int
    Number of periods to move, 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 : int or str
    Shift direction. {0, 'index'}.

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.

Returns
-------
shifted : Series

Examples
--------
>>> df = pd.DataFrame({'Col1': [10, 20, 30, 20, 15, 30, 45],
...                    'Col2': [13, 23, 33, 23, 18, 33, 48],
...                    'Col3': [17, 27, 37, 27, 22, 37, 52]})
>>> print(df)
   Col1  Col2  Col3
0    10    13    17
1    20    23    27
2    30    33    37
3    20    23    27
4    15    18    22
5    30    33    37
6    45    48    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
5  30.0  33.0  37.0
6  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  30.0  33.0
3   NaN  20.0  23.0
4   NaN  15.0  18.0
5   NaN  30.0  33.0
6   NaN  45.0  48.0

>>> import datetime
>>> names = ['João', 'Maria', 'Emanuel', 'Jussara', 'José']
>>> dates = [datetime.datetime(2018, 3, 1, 11, 15),
...          datetime.datetime(2018, 3, 5, 11, 15),
...          datetime.datetime(2018, 3, 10, 11, 15),
...          datetime.datetime(2018, 3, 15, 11, 15),
...          datetime.datetime(2018, 3, 20, 11, 15)]
>>> df = pd.DataFrame(data={'names': names}, index=dates)
>>> print(df)
                       names
2018-03-01 11:15:00     João
2018-03-05 11:15:00    Maria
2018-03-10 11:15:00  Emanuel
2018-03-15 11:15:00  Jussara
2018-03-20 11:15:00     José

>>> df.shift(periods=2, freq='D')
                       names
2018-03-03 11:15:00     João
2018-03-07 11:15:00    Maria
2018-03-12 11:15:00  Emanuel
2018-03-17 11:15:00  Jussara
2018-03-22 11:15:00     José

>>> df.shift(periods=75, freq='min')
                       names
2018-03-01 12:30:00     João
2018-03-05 12:30:00    Maria
2018-03-10 12:30:00  Emanuel
2018-03-15 12:30:00  Jussara
2018-03-20 12:30:00     José

See Also
--------
slice_shift: Equivalent to shift without copying data.
tshift: Shift the time index, using the index’s frequency if available.

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.Series.shift" correct. :)
```