DOC: update the DataFrame.count 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.count) ######################
################################################################################

Count non-NA cells for each column or row.

Return Series with number of non-NA observations over requested
axis. Works with non-floating point data as well (detects `NaN` and
`None`)

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    If equal 0 or 'index' counts are generated for each column.
    If equal 1 or 'columns' counts are generated for each row.
level : int or str, optional
    If the axis is a `MultiIndex` (hierarchical), count along a
    particular level, collapsing into a `DataFrame`.
    A `str` specifies the level name.
numeric_only : boolean, default False
    Include only `float`, `int` or `boolean` data.

Returns
-------
Series or DataFrame
    For each column/row the number of non-NA/null entries.
    If level is specified returns a `DataFrame`.

See Also
--------
Series.count: number of non-NA elements in a Series
DataFrame.shape: number of DataFrame rows and columns (including NA
    elements)
DataFrame.isnull: boolean same-sized DataFrame showing places of NA
    elements

Examples
--------
>>> df=pd.DataFrame({ "Person":["John","Myla",None],
...                   "Age":[24.,np.nan,21.],
...                   "Single":[False,True,True]     })
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2    None  21.0    True
>>> df.count()
Person    2
Age       2
Single    3
dtype: int64
>>> df.count(axis=1)
0    3
1    2
2    2
dtype: int64

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

Docstring for "pandas.DataFrame.count" correct. :)
```

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.
