DOC: update the DataFrame.update() 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>`
- [x] 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.update)  #####################
################################################################################

Modify DataFrame in place using non-NA values from passed
DataFrame.

Aligns on indices.

Parameters
----------
other : DataFrame, or object coercible into a DataFrame
    Index should be similar to one of the columns in this one. If a
    Series is passed, its name attribute must be set, and that will be
    used as the column name in the resulting joined DataFrame.
join : {'left'}, default 'left'
    Indicates which column values overwrite.
overwrite : boolean, default True
    If True then overwrite values for common keys in the calling frame.
filter_func : callable(1d-array) -> 1d-array<boolean>, default None
    Can choose to replace values other than NA. Return True for values
    that should be updated.
raise_conflict : boolean
    If True, will raise an error if the DataFrame and other both
    contain data in the same place.

Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If ``other`` contains NaNs the corresponding values are not updated
in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

See also
--------
DataFrame.merge : For column(s)-on-columns(s) operations

Returns
-------
updated : DataFrame

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

```

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.

