DOC: update the pd.DataFrame.memory_usage/empty docstring(Seoul)
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:

```
# paste output of "scripts/validate_docstrings.py <your-function-or-method>" here
# between the "```" (remove this comment, but keep the "```")

################################################################################
###################### Docstring (pandas.DataFrame.empty) ######################
################################################################################

True if DataFrame is empty.

True if DataFrame is entirely empty [no items], meaning any of the
axes are of length 0.

Returns
-------
empty : boolean
    if DataFrame is empty, return true, if not return false.

Notes
-----
If DataFrame contains only NaNs, it is still not considered empty. See
the example below.

Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:

>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True

If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:

>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
    A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True

See also
--------
pandas.Series.dropna
pandas.DataFrame.dropna

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

Errors found:
	Missing description for See Also "pandas.Series.dropna" reference
	Missing description for See Also "pandas.DataFrame.dropna" reference


################################################################################
################## Docstring (pandas.DataFrame.memory_usage)  ##################
################################################################################

Memory usage of DataFrame columns.

Memory usage of DataFrame is accessing pandas.DataFrame.info method.
A configuration option, `display.memory_usage` (see Parameters)

Parameters
----------
index : bool
    Specifies whether to include memory usage of DataFrame's
    index in returned Series. If `index=True` (default is False)
    the first index of the Series is `Index`.
deep : bool
    Introspect the data deeply, interrogate
    `object` dtypes for system-level memory consumption.

Returns
-------
sizes : Series
    A series with column names as index and memory usage of
    columns with units of bytes.

Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False

See Also
--------
numpy.ndarray.nbytes

Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.random.randint(100, size=5000).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.memory_usage()
Index            80
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=True)
Index            80
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=True).sum()
205080

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

Errors found:
	Missing description for See Also "numpy.ndarray.nbytes" reference

```
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.

Lastly, I left errors already occurred in the previous version without changes.