Find all columns of dataframe in Pandas whose type is float, or a particular type?

This is conciser:

# select the float columns
df_num = df.select_dtypes(include=[np.float])
# select non-numeric columns
df_num = df.select_dtypes(exclude=[np.number])

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.

As @RNA said, you can use pandas.DataFrame.select_dtypes. The code using your example from a question would look like this:

for col in df.select_dtypes(include=['object']).columns:
    df[col] = df[col].fillna('unknown')