DOC: update the pandas.DataFrame.pct_change docstring
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"
- [x] 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`
- [x] The html version looks good: `python doc/make.py --single <your-function-or-method>`
- [ ] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```
################################################################################
################### Docstring (pandas.DataFrame.pct_change)  ###################
################################################################################

Percentage change between the current and previous element.

This is useful in comparing the percentage of change in a time series
of elements.

Parameters
----------
periods : int, default 1
    Periods to shift for forming percent change.
fill_method : str, default 'pad'
    How to handle NAs before computing percent changes.
limit : int, default None
    The number of consecutive NAs to fill before stopping.
freq : DateOffset, timedelta, or offset alias string, optional
    Increment to use from time series API (e.g. 'M' or BDay()).
**kwargs : mapping, optional
    Additional keyword arguments are passed into
    `DataFrame.shift` or `Series.shift`.

Returns
-------
chg : Series or DataFrame
    The same type as the calling object.

Examples
--------
See the percentage change in a Series.

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64

See the percentage change in a Series where filling NAs with last
valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

Percentage change in French franc, Deutsche Mark, and Italian lira from
1 January 1980 to 1 March 1980.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
>>> df.pct_change(axis='columns')
      2016      2015      2014
GOOG   NaN -0.151997 -0.086016
APPL   NaN  0.337604  0.012002

See Also
--------
DataFrame.diff : see the difference of two columns
Series.diff : see the difference of two columns

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

Errors found:
	Errors in parameters section
		Parameters {'kwargs'} not documented
		Unknown parameters {'**kwargs'}
```