Calling DataFrame.to_json() increases data frame memory usage in Python 3.6
#### Code Sample, a copy-pastable example if possible

```python
Python 3.6.0 (default, Dec 29 2016, 21:40:24) 
[GCC 4.9.4] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame({'a': [str(i) for i in range(10000)]})
>>> df.memory_usage(index=True, deep=True)
Index        80
a        608890
dtype: int64
>>> j = df.to_json()
>>> df.memory_usage(index=True, deep=True)
Index        80
a        804450
dtype: int64
```

Compared to Python 2.7.12
```python
Python 2.7.12 (default, Jul 18 2016, 15:02:52) 
[GCC 4.8.4] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame({'a': [str(i) for i in range(10000)]})
>>> df.memory_usage(index=True, deep=True)
Index        72
a        488890
dtype: int64
>>> j = df.to_json()
>>> df.memory_usage(index=True, deep=True)
Index        72
a        488890
dtype: int64
```

#### Problem description
Calling `to_json` should not have any impact on the reported memory usage of a DataFrame. Just like in Python 2. The observed increase above is 32% which is really high.

This only seems to happen with dataframes that have strings in them.

I've also tested calling `to_csv`, that does not trigger this behaviour.

Furthermore it seems like the memory usage is quite a lot higher in Python 3 compared to the equivalent data frame in Python 2 (~25% in the example above). I guess this is more related to strings in Python 2 vs Python 3 than Pandas though?

#### Expected Output
No change in reported memory usage after calling `to_json`.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-83-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.1.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.2
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None

</details>
