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. :)
```

Hello @donK23! Thanks for updating the PR.



Cheers ! There are no PEP8 issues in this Pull Request. :beers: 

##### Comment last updated on March 13, 2018 at 13:49 Hours UTC # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20160?src=pr&el=h1) Report
> :exclamation: No coverage uploaded for pull request base (`master@e7e0ea8`). [Click here to learn what that means](https://docs.codecov.io/docs/error-reference#section-missing-base-commit).
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/20160/graphs/tree.svg?token=eZ4WkYLtcO&src=pr&height=150&width=650)](https://codecov.io/gh/pandas-dev/pandas/pull/20160?src=pr&el=tree)

```diff
@@            Coverage Diff            @@
##             master   #20160   +/-   ##
=========================================
  Coverage          ?   91.76%           
=========================================
  Files             ?      150           
  Lines             ?    49146           
  Branches          ?        0           
=========================================
  Hits              ?    45097           
  Misses            ?     4049           
  Partials          ?        0
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.14% <100%> (?)` | |
| #single | `41.9% <0%> (?)` | |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/20160?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/pull/20160/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `95.85% <100%> (ø)` | |

------

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> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20160?src=pr&el=footer). Last update [e7e0ea8...744dc61](https://codecov.io/gh/pandas-dev/pandas/pull/20160?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 @donK23 Thanks a lot!

If possible, welcome to check the online docs in a few hours to see if everything is looking OK: http://pandas-docs.github.io/pandas-docs-travis/generated/pandas.DataFrame.notna.html#pandas.DataFrame.notna