从 Pandas 列中删除重复的字母只存在于彼此，Python

Question

from this question : Python: Best Way to remove duplicate character from string answer:

''.join(ch for ch, _ in itertools.groupby(string_to_remove)

I know how to remove duplicated letters exists only next to each other, how to apply this solution to column in pandas?

df:

df=pd.DataFrame({'A':['ODOODY','LLHHEELLO'],'B':['NNMminee','DDasdss']})

expected result:

A,B
ODODY,NMine
LHELO,Dasds

tried: df['A'] = df['A'].apply(lambda x: ''.join(ch for ch, _ in itertools.groupby(x['A']))) thanks !

Answer 1

Use DataFrame.applymap , if necessary filter columns for remove duplicates:

import itertools
cols = ['A','B']
df[cols] = df[cols].applymap(lambda x: ''.join(ch for ch, _ in itertools.groupby(x)))
#for all columns
#df = df.applymap(lambda x: ''.join(ch for ch, _ in itertools.groupby(x)))
print (df)
       A       B
0  ODODY  NMmine
1  LHELO   Dasds

Solution with DataFrame.apply is possible, but need process each value separately, so aded list comprehension:

df[cols] = df[cols].apply(lambda x: [''.join(ch for ch, _ in itertools.groupby(y)) for y in x])
print (df)
       A       B
0  ODODY  NMmine
1  LHELO   Dasds

Or use Series.apply :

f = lambda x: ''.join(ch for ch, _ in itertools.groupby(x))
df['A'] = df['A'].apply(f)
df['B'] = df['B'].apply(f)