import pandas as pd

df = pd.DataFrame([['  a  ', 10], ['  c  ', 5]])

df.replace('^s+', '', regex=True, inplace=True) #front
df.replace('s+$', '', regex=True, inplace=True) #end

df.values

df_obj = df.select_dtypes(['object'])
print (df_obj)
0    a
1    c

df[df_obj.columns] = df_obj.apply(lambda x: x.str.strip())
print (df)

   0   1
0  a  10
1  c   5

df[0] = df[0].str.strip()

>>> df = pd.DataFrame([['  a  ', 10], ['  c  ', 5]])
>>> df[0][0]
'  a  '
>>> df[0] = df[0].apply(lambda x: x.strip())
>>> df[0][0]
'a'

>>> df = pd.DataFrame([['  a  ', 10], ['  c  ', 5]])
>>> df.apply(lambda x: x.apply(lambda y: y.strip() if type(y) == type('') else y), axis=0)

   0   1
0  a  10
1  c   5

>>> df.replace('(^s+|s+$)', '', regex=True, inplace=True)
>>> df
   0   1
0  a  10
1  c   5

>>> df[0] = df[0].str.strip()

df[0] = df[0].str.strip()

non_numeric_columns = list(set(df.columns)-set(df._get_numeric_data().columns))
df[non_numeric_columns] = df[non_numeric_columns].apply(lambda x : str(x).strip())

df.applymap(lambda x: x.strip() if type(x) is str else x)

import pandas as pd


def trimAllColumns(df):
    """
    Trim whitespace from ends of each value across all series in dataframe
    """
    trimStrings = lambda x: x.strip() if type(x) is str else x
    return df.applymap(trimStrings)


# simple example of trimming whitespace from data elements
df = pd.DataFrame([['  a  ', 10], ['  c  ', 5]])
df = trimAllColumns(df)
print(df)


>>>
   0   1
0  a  10
1  c   5