DOC: update the pandas.DataFrame.notna and pandas.Series.notna 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

Two Validations for pandas.DataFrame.notna and pandas.Series.notna (shared docs).
```
################################################################################
###################### Docstring (pandas.DataFrame.notna) ######################
################################################################################

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings `''` or :attr:`numpy.inf` are not considered NA values
(unless you set :attr:`pandas.options.mode.use_inf_as_na` `= True`).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.

Returns
-------
bool of type DataFrame
    Mask of True/False values for each element in DataFrame that
    indicates whether an element is not an NA value

See Also
--------
DataFrame.notnull : alias of notna
DataFrame.isna : boolean inverse of notna
DataFrame.dropna : omit axes labels with missing values
notna : top-level notna

Examples
--------
Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

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

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

```
################################################################################
####################### Docstring (pandas.Series.notna)  #######################
################################################################################

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings `''` or :attr:`numpy.inf` are not considered NA values
(unless you set :attr:`pandas.options.mode.use_inf_as_na` `= True`).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.

Returns
-------
bool of type Series
    Mask of True/False values for each element in Series that
    indicates whether an element is not an NA value

See Also
--------
Series.notnull : alias of notna
Series.isna : boolean inverse of notna
Series.dropna : omit axes labels with missing values
notna : top-level notna

Examples
--------
Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

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

Docstring for "pandas.Series.notna" correct. :)
```
