how to remove white space from strings of data frame column?

I am trying to loop through a column in a pandas data frame to remove unnecessary white space in the beginning and end of the strings within the column. My data frame looks like this:

df={'c1': [' ab', 'fg', 'ac ', 'hj-jk ', ' ac', 'df, gh', 'gh', 'ab', 'ad', 'jk-pl', 'ae', 'kl-kl '], 'b2': ['ba', 'bc', 'bd', 'be', 'be', 'be', 'ba'] }


    c1  b2
0   ab, fg
1   ac, hj-jk   
2   ac, df,gh   
3   gh, be
4   ab, be
5   ad, jk-pl
6   ae, kl-kl   

I tried the this answer here, but did not work either. The reason I need to remove the white space from the strings in this column is that I want to one hot encode this column using get.dummies() function. My idea was to use the strip() function to remove the white space from each value and then I use .str.get_dummies(‘,’):

#function to remove white space from strings
def strip_string(dataframe, column_name):
  for id, item in dataframe[column_name].items():
    a=item.strip()

#removing the white space from the values of the column
strip_string(df, 'c1')

#creating one hot-encoded columns from the values using split(",")

df1=df['c1'].str.get_dummies(',')

but my code returns duplicate columns and I don’t want this…I suppose the function to remove the white space is not working well? Can anyone help? My current output is:

   ab   ac  df  fg  gh  hj-jk   jk-pl   kl-kl   ab  ac  ad  ae  gh
0   1   0   0   1   0   0   0   0   0   0   0   0   0
1   0   0   0   0   0   1   0   0   0   1   0   0   0
2   0   1   1   0   1   0   0   0   0   0   0   0   0
3   0   0   0   0   0   0   0   0   0   0   0   0   1
4   0   0   0   0   0   0   0   0   1   0   0   0   0
5   0   0   0   0   0   0   1   0   0   0   1   0   0
6   0   0   0   0   0   0   0   1   0   0   0   1   0

columns ‘ac’ and ‘ab’ are duplicated. I want to remove the duplicated columns

Answer

I would stack, strip, get_dummies, and groupby.max:

If the separator is ', ':

df.stack().str.strip().str.get_dummies(sep=', ').groupby(level=0).max()

else:

df.stack().str.replace(r's', '', regex=True).str.get_dummies(sep=',').groupby(level=0).max()

output:

   ab  ac  ba  bc  bd  be  df  fg  gh  hj-jk
0   1   0   1   0   0   0   0   0   0      0
1   0   0   0   1   0   0   0   1   0      0
2   0   1   0   0   1   0   0   0   0      0
3   0   0   0   0   0   1   0   0   0      1
4   0   1   0   0   0   1   0   0   0      0
5   0   0   0   0   0   1   1   0   1      0
6   0   0   1   0   0   0   0   0   1      0