Doc: Updated DataFrame.dot 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.dot) #######################
################################################################################

Find the dot product of two matrices.

Using two DataFrame, matrix, or Series objects -
this method finds the standard inner product (dot product)
of two conformable matrices. Matrices A and B are said
to be conformable for multiplication A(B) whenever the
number of columns in A = the number of rows in B.
(i.e. -  A is m × p and B is p × n)

Parameters
----------
other : DataFrame, matrix, or Series
    Different combinations shown in examples below.

Returns
-------
dot_product : DataFrame, matrix, or Series
    Return type depends on input, see examples below.

Raises
------
ValueError
    If the first dimension of A is not the same size as the
    secondi dimension of B.

See Also
--------
numpy.dot : dot product in numpy

Examples
--------
Two DataFrame objects:

>>> df_A = pd.DataFrame([[1,2],[3,4]])
>>> df_A
   0  1
0  1  2
1  3  4

>>> df_B = pd.DataFrame([[3,4],[1,2]])
>>> df_B
   0  1
0  3  4
1  1  2

>>> df_A.dot(df_B)
      0   1
0     5   8
1    13   20

A DataFrame and a matrix object (returns DataFrame):

>>> df_A = pd.DataFrame([[1,2],[3,4]])
>>> df_A
   0  1
0  1  2
1  3  4

>>> mat_B = np.matrix('3 4 ; 1 2')
>>> mat_B
matrix([[3, 4],
       [1, 2]])

>>> df_A.dot(mat_B)
      0   1
0     5   8
1    13   20

A matrix object and a DataFrame (returns matrix):

>>> mat_A = np.matrix('1 2 ; 3 4')
>>> mat_A
matrix([[1, 2],
        [3, 4]])

>>> df_B = pd.DataFrame([[3,4],[1,2]])
>>> df_B
   0  1
0  3  4
1  1  2

>>> mat_A.dot(df_B)
matrix([[ 5,  8],
        [13, 20]])

There is similar functionality using just matrices or Series.

Two matrix objects:

>>> mat_A = np.matrix('1 2 ; 3 4')
>>> mat_A
matrix([[1, 2],
        [3, 4]])

>>> mat_B = np.matrix('3 4 ; 1 2')
>>> mat_B
matrix([[3, 4],
       [1, 2]])

>>> mat_A.dot(mat_B)
matrix([[ 5,  8],
        [13, 20]])

Two Series objects:

>>> ser_A = pd.Series(data=[1,2,3,4])
>>> ser_A
0    1
1    2
2    3
3    4
dtype: int64

>>> ser_B = pd.Series(data=[3,4,1,2])
>>> ser_B
0    3
1    4
2    1
3    2
dtype: int64

>>> ser_A.dot(ser_B)
22

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

Docstring for "pandas.DataFrame.dot" correct. :)

```
