Did you try assigning it back to the column?

df['column'] = df['column'].astype('str') 

Referring to this question, the pandas dataframe stores the pointers to the strings and hence it is of type 'object'. As per the docs ,You could try:

df['column_new'] = df['column'].str.split(',') 

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.

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

Copydf = 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):

Copy 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)