map: on Series only; on one element at a time; takes dicts, series, or callable;

applymap: on DataFrame only; on one element at a time; takes callables only;  

apply: Both Series & DataFrame; on entire rows or columns at a time; takes callables only; 

df['A'].map({1:'a', 2:'b', 3:'c'})

df[['A','B','C']].applymap(str.strip)

df['sentences'].apply(nltk.sent_tokenize)

#extract year from year-month: extract 1997 from 1997-12
def trunc_str(year):
    x = re.findall('([\d]{4})',year)
    if x:
        return(x.group())

df['sentence'] = df['sentences'].apply(lambda x: trunc_str(x))

df['sentence'] = df['sentences'].apply(trunc_str)

df['sentence'] = df['sentences'].str.extract(r'(\d{4})-\d{2}')

df.replace('(-d)','',regex=True, inplace=True)
#remove dash(-) followed by a number from Series

df.replace('[nN]ew','New_',regex=True)
#replace nNew to New_

df['Email'].apply(lambda x: re.sub(r'@', 'at', x))

[x.strip() for x in s.split(',')]

df.apply(lambda x: x.C.max() - x.B.min())

df.groupby['A'][['B','C']].apply(lambda x: x.C.max() - x.B.min())