DOC: update the pandas.DataFrame.isna and pandas.Series.isna docstring
State: closed

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.isna and pandas.Series.isna (shared docs).

```
################################################################################
###################### Docstring (pandas.DataFrame.isna)  ######################
################################################################################

Detect missing values.

Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:`numpy.NaN`, get mapped to True
values.
Everything else get mapped to False values. 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`).

Returns
-------
bool of type DataFrame
    Mask of True/False values for each element in DataFrame that
    indicates whether an element is an NA value

See Also
--------
DataFrame.isnull : alias of isna
DataFrame.notna : boolean inverse of isna
DataFrame.dropna : omit axes labels with missing values
isna : top-level isna

Examples
--------
Show which entries in a DataFrame are 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.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.DataFrame.isna" correct. :)
```

```
################################################################################
######################## Docstring (pandas.Series.isna) ########################
################################################################################

Detect missing values.

Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:`numpy.NaN`, get mapped to True
values.
Everything else get mapped to False values. 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`).

Returns
-------
bool of type Series
    Mask of True/False values for each element in Series that
    indicates whether an element is an NA value

See Also
--------
Series.isnull : alias of isna
Series.notna : boolean inverse of isna
Series.dropna : omit axes labels with missing values
isna : top-level isna

Examples
--------
Show which entries in a DataFrame are 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.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.Series.isna" correct. :)

```


Labels:
name: Docs

Comments:
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 12:47 Hours UTC
```
################################################################################
###################### Docstring (pandas.DataFrame.isna)  ######################
################################################################################

Detect missing values.

Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:`numpy.NaN`, get mapped to True
values.
Everything else get mapped to False values. 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`).

Returns
-------
bool of type DataFrame
    Mask of True/False values for each element in DataFrame that
    indicates whether an element is an NA value.

See Also
--------
DataFrame.isnull : alias of isna
DataFrame.notna : boolean inverse of isna
DataFrame.dropna : omit axes labels with missing values
isna : top-level isna

Examples
--------
Show which entries in a DataFrame are 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.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.DataFrame.isna" correct. :)
```
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@@            Coverage Diff            @@
##             master   #20138   +/-   ##
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=========================================
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=========================================
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| Flag | Coverage Δ | |
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| #single | `41.84% <2.85%> (?)` | |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/20138?src=pr&el=tree) | Coverage Δ | |
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| [pandas/core/indexes/base.py](https://codecov.io/gh/pandas-dev/pandas/pull/20138/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW5kZXhlcy9iYXNlLnB5) | `96.66% <ø> (ø)` | |
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------

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Made the changes. Now it should be consistent with the .notna docstrings (PR #20160).
Thanks @donK23 !