Copy method does not make truly deep copies of dtype object arrays
The `copy` method of `pd.Series` and `pd.DataFrame` has a parameter `deep` which claims to [Make a deep copy, i.e. also copy data](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.copy.html). The example below seems to show that this isn't a truly deep copy (as in `from copy import deepcopy`) I can't seem to find the implementation in the source. I am wondering if this behavior below is expected, if something different should be done for `dtype=object` to make it a truly deep copy, or if we could at least add a note to the documentation that notes this behavior?

Thanks!

```
import pandas as pd
import copy
import numpy as np

"""Create a Series based on np array of objects"""
a = pd.Series(np.array([1, 'series of objects', {'first':3,'second':5}], dtype=np.object))
b = a.copy(deep=True)
c = copy.deepcopy(a)

"""The dict has length 2"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])

"""Remove one key from c (the deepcopy)"""
c.loc[2].pop('first')

"""Only changes c"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])

"""Remove one key from c (the pandas deepcopy)"""
b.loc[2].pop('first')

"""Changes a and b?"""
print len(a.loc[2]), len(b.loc[2]), len(c.loc[2])
```
