PERF: json_normalize
I haven't looked much at the implementation, but guessing simpler cases like this could be optimized.

```python
In [63]: data = [
    ...:     {'name': 'Name',
    ...:      'value': 1.0,
    ...:      'value2': 2.0,
    ...:      'nested': {'a': 'aa', 'b': 'bb'}}] * 1000000

In [64]: %timeit pd.DataFrame(data)
1 loop, best of 3: 847 ms per loop

In [65]: %timeit pd.io.json.json_normalize(data)
1 loop, best of 3: 20 s per loop
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: 1.5.3
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
</details>
