Seems your roundtripping IS causing some unicode. Not sure why that is, but easy to fix. You cannot store unicode in a HDFStore Table in python 2, (this works correctly in python 3 however). You could do it as a Fixed format if you want though (it would be pickled). See here.

In [33]: df = pd.read_json(s)

In [25]: df
Out[25]: 
  args                date            host kwargs     operation  status   thingy      time
0   [] 2013-12-02 00:33:59  yy38.segm1.org     {}       x_gbinf    -101  a13yy38  0.000801
1   [] 2013-12-02 00:33:59  kyy1.segm1.org     {}     x_initobj       1  a19kyy1  0.003244
2   [] 2013-12-02 00:34:00  yy10.segm1.org     {}  x_gobjParams    -101  a14yy10  0.002247
3   [] 2013-12-02 00:34:00  yy24.segm1.org     {}        gtfull    -101  a14yy24  0.002787
4   [] 2013-12-02 00:34:00  yy24.segm1.org     {}       x_gbinf    -101  a14yy24  0.001067
5   [] 2013-12-02 00:34:00  yy34.segm1.org     {}       gxyzinf    -101  a12yy34  0.002652
6   [] 2013-12-02 00:34:00  yy15.segm1.org     {}     deletemfg       1  a15yy15  0.004371
7   [] 2013-12-02 00:34:00  yy15.segm1.org     {}       gxyzinf    -101  a15yy15  0.000602

[8 rows x 8 columns]

In [26]: df.dtypes
Out[26]: 
args                 object
date         datetime64[ns]
host                 object
kwargs               object
operation            object
status                int64
thingy               object
time                float64
dtype: object

This is inferring the actual type of the object dtyped Series. They will come out as unicode only if at least 1 string is unicode (otherwise they would be inferred as string)

In [27]: df.apply(lambda x: pd.lib.infer_dtype(x.values))
Out[27]: 
args            unicode
date         datetime64
host            unicode
kwargs          unicode
operation       unicode
status          integer
thingy          unicode
time           floating
dtype: object

Here's how to 'fix' it

In [28]: types = df.apply(lambda x: pd.lib.infer_dtype(x.values))

In [29]: types[types=='unicode']
Out[29]: 
args         unicode
host         unicode
kwargs       unicode
operation    unicode
thingy       unicode
dtype: object

In [30]: for col in types[types=='unicode'].index:
   ....:     df[col] = df[col].astype(str)
   ....:     

Looks the same

In [31]: df
Out[31]: 
  args                date            host kwargs     operation  status   thingy      time
0   [] 2013-12-02 00:33:59  yy38.segm1.org     {}       x_gbinf    -101  a13yy38  0.000801
1   [] 2013-12-02 00:33:59  kyy1.segm1.org     {}     x_initobj       1  a19kyy1  0.003244
2   [] 2013-12-02 00:34:00  yy10.segm1.org     {}  x_gobjParams    -101  a14yy10  0.002247
3   [] 2013-12-02 00:34:00  yy24.segm1.org     {}        gtfull    -101  a14yy24  0.002787
4   [] 2013-12-02 00:34:00  yy24.segm1.org     {}       x_gbinf    -101  a14yy24  0.001067
5   [] 2013-12-02 00:34:00  yy34.segm1.org     {}       gxyzinf    -101  a12yy34  0.002652
6   [] 2013-12-02 00:34:00  yy15.segm1.org     {}     deletemfg       1  a15yy15  0.004371
7   [] 2013-12-02 00:34:00  yy15.segm1.org     {}       gxyzinf    -101  a15yy15  0.000602

[8 rows x 8 columns]

But now infers correctly.

In [32]: df.apply(lambda x: pd.lib.infer_dtype(x.values))
Out[32]: 
args             string
date         datetime64
host             string
kwargs           string
operation        string
status          integer
thingy           string
time           floating
dtype: object

As the other poster mentioned, you might try:

df = pd.read_csv('1459966468_324.csv', encoding='utf8')

However this could still leave you looking at 'object' when you print the dtypes. To confirm they are utf8, try this line after reading the CSV:

df.apply(lambda x: pd.lib.infer_dtype(x.values))

Example output:

args            unicode
date         datetime64
host            unicode
kwargs          unicode
operation       unicode

You have unicode values in your DataFrame. Files store bytes, which means all unicode have to be encoded into bytes before they can be stored in a file. You have to specify an encoding, such as utf-8. For example,

df.to_csv('path', header=True, index=False, encoding='utf-8')

If you don't specify an encoding, then the encoding used by df.to_csv defaults to ascii in Python2, or utf-8 in Python3.

There is a very useful function u in pandas.compat, to make your values in unicode.:

In [26]:
import pandas as pd
from pandas.compat import u
no_unicode = pd.Series(['Steve', 'Jason', 'Jake'])
#yes_unicode = pd.Series(['tea', 'caf\xe9', 'beer'])
yes_unicode = pd.Series(map(u,['tea', 'caf\xe9', 'beer']))
var_names = dict(no_unicode = no_unicode, yes_unicode = yes_unicode)
df = pd.DataFrame(var_names)
print(df)

  no_unicode yes_unicode
0      Steve         tea
1      Jason        café
2       Jake        beer

[3 rows x 2 columns]