DOC: update the Index.shift docstring
State: closed

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [x] PR title is "DOC: update the <your-function-or-method> docstring"
- [ ] The validation script passes: `scripts/validate_docstrings.py <your-function-or-method>`
- [x] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Please include the output of the validation script below between the "```" ticks:

```
################################################################################
######################## Docstring (pandas.Index.shift) ########################
################################################################################

Shift index by desired number of time frequency increments.

This method is for shifting the values of datetime-like indexes
by a specified time increment a given number of times.

Parameters
----------
periods : int, default 1
    Number of periods (or increments) to shift by,
    can be positive or negative.
freq : pandas.DateOffset, pandas.Timedelta or string, optional
    Frequency increment to shift by.
    If None, the index is shifted by its own `freq` attribute.
    Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.

Returns
-------
pandas.Index
    shifted index

Examples
--------
Put the first 5 month starts of 2011 into an index.

>>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
>>> month_starts
DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
               '2011-05-01'],
              dtype='datetime64[ns]', freq='MS')

Shift the index by 10 days.

>>> month_starts.shift(10, freq='D')
DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
               '2011-05-11'],
              dtype='datetime64[ns]', freq=None)

The default value of `freq` is the `freq` attribute of the index,
which is 'MS' (month start) in this example.

>>> month_starts.shift(10)
DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
               '2012-03-01'],
              dtype='datetime64[ns]', freq='MS')

Notes
-----
This method is only implemented for datetime-like index classes,
i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	See Also section not found

```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

-- We haven't included a See Also section because there are no functions in pandas that are related to this.


Checklist for other PRs (remove this part if you are doing a PR for the pandas documentation sprint):

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry


Labels:
name: Docs
name: Datetime
description: Datetime data dtype
name: Visualization
description: plotting
name: Reshaping
description: Concat, Merge/Join, Stack/Unstack, Explode

Comments:
Could you add `Series.shift` as a See Also?
Thanks!