DOC: Additional examples for `json_normalize`
When handling JSON data, a common use case is to start with a list of hierarchically nested records with an unknown, or possibly inconsistent, layout, and transform them into a flat tabular structure. Pandas' existing `json_normalize` function handles this use case, but the examples in the function's documentation don't make this clear. It could be useful to provide some additional explanation and examples in these functions.

#### Code Sample

```python
data = [
    ...: {'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
    ...: {'name': {'given': 'Mose', 'family': 'Regner'}},
    ...: {'id': 2, 'name': 'Faye Raker'},
    ...: ]

json_normalize(data)

    id        name name.family name.first name.given name.last
0  1.0         NaN         NaN     Coleen        NaN      Volk
1  NaN         NaN      Regner        NaN       Mose       NaN
2  2.0  Faye Raker         NaN        NaN        NaN       NaN
```

#### Problem description

Direct conversion to a data frame doesn't provide information about the nested structure. `pandas.read_json` is also designed to work with data that's already flat.

The existing [documentation for `json_normalize`](http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.io.json.json_normalize.html) only includes an example of using it for a somewhat more complicated process. The [tutorial sections on JSON parsing](http://pandas.pydata.org/pandas-docs/version/0.20/io.html#normalization) use the same example. These items could be updated to include additional examples that would help others understand when and how to apply `json_normalize`

#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.1
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 6.0.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
