DOC: update the docstring for several functions and properties (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:

```
`pd.DataFrame.get_dtype_counts`

################################################################################
################ Docstring (pandas.DataFrame.get_dtype_counts)  ################
################################################################################

Return counts of unique dtypes in this object.

Returns
-------
dtype   Number of dtype

See Also
--------
dtypes : Return the dtypes in this object.

Examples
--------
>>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
>>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
>>> df['int'].astype(int)
>>> df['float'].astype(float)
>>> df.get_dtype_counts()
float64    1
int64      1
object     1
dtype: int64

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

Errors found:
	No extended summary found
	Examples do not pass tests

################################################################################
################################### Doctests ###################################
################################################################################

**********************************************************************
Line 16, in pandas.DataFrame.get_dtype_counts
Failed example:
    df['int'].astype(int)
Expected nothing
Got:
    0    1
    1    2
    2    3
    Name: int, dtype: int64
**********************************************************************
Line 17, in pandas.DataFrame.get_dtype_counts
Failed example:
    df['float'].astype(float)
Expected nothing
Got:
    0    1.0
    1    2.0
    2    3.0
    Name: float, dtype: float64

`pd.DataFrame.get_ftype_counts`

################################################################################
################ Docstring (pandas.DataFrame.get_ftype_counts)  ################
################################################################################

Return counts of unique ftypes in this object.

Returns
-------
dtype   Number of dtype:dense|sparse

See Also
--------
ftypes : Return
         ftypes (indication of sparse/dense and dtype) in this object.

Examples
--------
>>> a = [['a', 1, 1.0], ['b', 2, 2.0], ['c', 3, 3.0]]
>>> df = pd.DataFrame(a, columns=['str', 'int', 'float'])
>>> df['int'].astype(int)
>>> df['float'].astype(float)
>>> df.get_dtype_counts()
float64:dense    1
int64:dense      1
object:dense     1
dtype: int64

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

Errors found:
	No extended summary found
	Examples do not pass tests

################################################################################
################################### Doctests ###################################
################################################################################

**********************************************************************
Line 17, in pandas.DataFrame.get_ftype_counts
Failed example:
    df['int'].astype(int)
Expected nothing
Got:
    0    1
    1    2
    2    3
    Name: int, dtype: int64
**********************************************************************
Line 18, in pandas.DataFrame.get_ftype_counts
Failed example:
    df['float'].astype(float)
Expected nothing
Got:
    0    1.0
    1    2.0
    2    3.0
    Name: float, dtype: float64
**********************************************************************
Line 19, in pandas.DataFrame.get_ftype_counts
Failed example:
    df.get_dtype_counts()
Expected:
    float64:dense    1
    int64:dense      1
    object:dense     1
    dtype: int64
Got:
    float64    1
    int64      1
    object     1
    dtype: int64

`pd.DataFrame.select_dtypes`

################################################################################
################## Docstring (pandas.DataFrame.select_dtypes) ##################
################################################################################

Return a subset of a DataFrame including/excluding columns based on
their ``dtype``.

Parameters
----------
include, exclude : scalar or list-like
    A selection of dtypes or strings to be included/excluded. At least
    one of these parameters must be supplied.

Raises
------
ValueError
    * If both of ``include`` and ``exclude`` are empty
    * If ``include`` and ``exclude`` have overlapping elements
    * If any kind of string dtype is passed in.

Returns
-------
subset : DataFrame
    The subset of the frame including the dtypes in ``include`` and
    excluding the dtypes in ``exclude``.

Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
  this will return *all* object dtype columns
* See the `numpy dtype hierarchy
  <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
  ``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
  ``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
  0.20.0) or ``'datetime64[ns, tz]'``

Examples
--------
>>> df = pd.DataFrame({'a': np.random.randn(6).astype('f4'),
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0  0.3962   True  1.0
1  0.1459  False  2.0
2  0.2623   True  1.0
3  0.0764  False  2.0
4 -0.9703   True  1.0
5 -1.2094  False  2.0
>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['floating'])
       b
0   True
1  False
2   True
3  False
4   True
5  False

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

Errors found:
	No summary found (a short summary in a single line should be present at the beginning of the docstring)
	Errors in parameters section
		Parameters {'exclude', 'include'} not documented
		Unknown parameters {'include, exclude'}
	See Also section not found
	Examples do not pass tests

################################################################################
################################### Doctests ###################################
################################################################################

**********************************************************************
Line 44, in pandas.DataFrame.select_dtypes
Failed example:
    df
Expected:
            a      b  c
    0  0.3962   True  1.0
    1  0.1459  False  2.0
    2  0.2623   True  1.0
    3  0.0764  False  2.0
    4 -0.9703   True  1.0
    5 -1.2094  False  2.0
Got:
              a      b    c
    0  1.941085   True  1.0
    1  1.050210  False  2.0
    2  1.936395   True  1.0
    3 -1.503260  False  2.0
    4 -0.155825   True  1.0
    5  0.852338  False  2.0

`pd.DataFrame.values`

################################################################################
##################### Docstring (pandas.DataFrame.values)  #####################
################################################################################

Return NDFrame as ndarray or ndarray-like depending on the dtype.

Notes
-----
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32.  If dtypes are int32 and uint8, dtype will be upcast to
int32. By numpy.find_common_type convention, mixing int64 and uint64
will result in a flot64 dtype.

Examples
--------
>>> df = pd.DataFrame({'a': np.random.randn(2).astype('f4'),
...                    'b': [True, False], 'c': [1.0, 2.0]})
>>> type(df.values)
<class 'numpy.ndarray'>
>>> df.values
array([[0.25209328532218933, True, 1.0],
[0.35383567214012146, False, 2.0]], dtype=object)
################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	No extended summary found
	No returns section found
	Private classes (['NDFrame']) should not be mentioned in public docstring.
	See Also section not found
	Examples do not pass tests

################################################################################
################################### Doctests ###################################
################################################################################

**********************************************************************
Line 22, in pandas.DataFrame.values
Failed example:
    df.values
Expected:
    array([[0.25209328532218933, True, 1.0],
    [0.35383567214012146, False, 2.0]], dtype=object)
Got:
    array([[-0.8504104018211365, True, 1.0],
           [-0.9855750203132629, False, 2.0]], dtype=object)

`pd.DataFrame.get_values`

################################################################################
################### Docstring (pandas.DataFrame.get_values)  ###################
################################################################################

Same as values (but handles sparseness conversions).

Returns
-------
numpy.ndaray
    Numpy representation of NDFrame

Examples
--------
>>> df = pd.DataFrame({'a': np.random.randn(2).astype('f4'),
...                    'b': [True, False], 'c': [1.0, 2.0]})
>>> df.get_values()
array([[0.25209328532218933, True, 1.0],
[0.35383567214012146, False, 2.0]], dtype=object)
################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	No extended summary found
	Private classes (['NDFrame']) should not be mentioned in public docstring.
	See Also section not found
	Examples do not pass tests

################################################################################
################################### Doctests ###################################
################################################################################

**********************************************************************
Line 13, in pandas.DataFrame.get_values
Failed example:
    df.get_values()
Expected:
    array([[0.25209328532218933, True, 1.0],
    [0.35383567214012146, False, 2.0]], dtype=object)
Got:
    array([[-1.3661248683929443, True, 1.0],
           [-0.5633015632629395, False, 2.0]], dtype=object)
```

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.

-> Most of them occur because of missing extended summaries. Functions and properties I added docstrings are fairly well explained without extended summaries I think. 

Some of the examples are failed because I used random functions for several examples. This makes different results in each execution. Also, I omitted some outputs because of its simplicity and clearness.

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

