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.

```
################################################################################
###################### 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 `None`,
`NaN` and `NaT`).

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    If 0 or 'index' counts are generated for each column.
    If 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.isna: boolean same-sized DataFrame showing places of NA
    elements

Examples
--------
Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", None, "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2    None  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    4
Age       4
Single    5
dtype: int64

Counts for each **row**:

>>> df.count(axis='columns')
0    3
1    2
2    2
3    3
4    3
dtype: int64

Counts for one level of a `MultiIndex`:

>>> df.set_index(["Person", "Single"]).count(level="Person")
        Age
Person
John      2
Myla      1

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

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

Count non-NA cells for each column or row.

The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
on `pandas.options.mode.use_inf_as_na`) are considered NA.

Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
    If 0 or 'index' counts are generated for each column.
    If 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.isna: boolean same-sized DataFrame showing places of NA
    elements

Examples
--------
Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", None, "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2    None  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    4
Age       4
Single    5
dtype: int64

Counts for each **row**:

>>> df.count(axis='columns')
0    3
1    2
2    2
3    3
4    3
dtype: int64

Counts for one level of a `MultiIndex`:

>>> df.set_index(["Person", "Single"]).count(level="Person")
        Age
Person
John      2
Myla      1

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

Docstring for "pandas.DataFrame.count" correct. :)
``` Thanks @joders! thanks for providing pandas