You can see what the dtype is for all the columns using the dtypes attribute:
In [11]: df = pd.DataFrame([[1, 'a', 2.]])
In [12]: df
Out[12]:
0 1 2
0 1 a 2
In [13]: df.dtypes
Out[13]:
0 int64
1 object
2 float64
dtype: object
In [14]: df.dtypes == object
Out[14]:
0 False
1 True
2 False
dtype: bool
To access the object columns:
In [15]: df.loc[:, df.dtypes == object]
Out[15]:
1
0 a
I think it's most explicit to use (I'm not sure that inplace would work here):
In [16]: df.loc[:, df.dtypes == object] = df.loc[:, df.dtypes == object].fillna('')
Saying that, I recommend you use NaN for missing data.