I have panda dataframe with multiple columns which mixed with values and unwanted characters.

columnA        columnB    columnC        ColumnD
\x00A\X00B     NULL       \x00C\x00D        123
\x00E\X00F     NULL       NULL              456

what I'd like to do is to make this dataframe as below.

columnA  columnB  columnC   ColumnD
AB        NULL       CD        123
EF        NULL       NULL      456

With my codes below, I can remove '\x00' from columnA but columnC is tricky as it is mixed with NULL in certain row.

col_names = cols_to_clean
fixer = dict.fromkeys([0x00], u'')
for i in col_names:
if df[i].isnull().any() == False:if df[i].dtype != np.int64:df[i] = df[i].map(lambda x: x.translate(fixer))

Is there any efficient way to remove unwanted characters from columnC?

In general, to remove non-ascii characters, use str.encode with errors='ignore':

df['col'] = df['col'].str.encode('ascii', 'ignore').str.decode('ascii')

To perform this on multiple string columns, use

u = df.select_dtypes(object)
df[u.columns] = u.apply(lambda x: x.str.encode('ascii', 'ignore').str.decode('ascii'))

Although that still won't handle the null characters in your columns. For that, you replace them using regex:

df2 = df.replace(r'\W+', '', regex=True)