If you need to convert ALL columns to strings, you can simply use:

df = df.astype(str)

This is useful if you need everything except a few columns to be strings/objects, then go back and convert the other ones to whatever you need (integer in this case):

 df[["D", "E"]] = df[["D", "E"]].astype(int) 

df = pd.DataFrame({
    'a': [23.0, 51.0, np.nan, 24.0],
    'b': ["a42", "3", np.nan, "a1"],
    'c': [142.0, 12.0, np.nan, np.nan]})

for col in df:
    df[col] = [np.nan if (not isinstance(val, str) and np.isnan(val)) else 
               (val if isinstance(val, str) else str(int(val))) 
               for val in df[col].tolist()]

>>> df
     a    b    c
0   23  a42  142
1   51    3   12
2  NaN  NaN  NaN
3   24   a1  NaN

>>> df.values
array([['23', 'a42', '142'],
       ['51', '3', '12'],
       [nan, nan, nan],
       ['24', 'a1', nan]], dtype=object)

I know this is an old question, but I was looking for a way to turn all columns with an object dtype to strings as a workaround for a bug I discovered in rpy2. I'm working with large dataframes, so didn't want to list each column explicitly. This seemed to work well for me so I thought I'd share in case it helps someone else.

stringcols = df.select_dtypes(include='object').columns
df[stringcols] = df[stringcols].fillna('').astype(str)

The "fillna('')" prevents NaN entries from getting converted to the string 'nan' by replacing with an empty string instead.