# best way data['resume'] = data[['Resume_title', 'City', 'State', 'Description', 'work_experiences', 'Educations', 'Skills', 'Certificates', 'Additional Information']].agg(' '.join, axis=1) # other way df["period"] = df["Year"] + df["quarter"] df['Period'] = df['Year'] + ' ' + df['Quarter'] df["period"] = df["Year"].astype(str) + df["quarter"] #If one (or both) of the columns are not string typed #Beware of NaNs when doing this! df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) #for multiple string columns df['period'] = df[['Year', 'quarter']].apply(lambda x: ''.join(x), axis=1) #method cat() of the .str accessor df['Period'] = df.Year.str.cat(df.Quarter) df['Period'] = df.Year.astype(str).str.cat(df.Quarter.astype(str), sep='q') df['AllTogether'] = df['Country'].str.cat(df[['State', 'City']], sep=' - ') #add parameter na_rep to replace the NaN values with a string if have nan columns = ['whatever', 'columns', 'you', 'choose'] df['period'] = df[columns].astype(str).sum(axis=1) #a function def str_join(df, sep, *cols): ...: from functools import reduce ...: return reduce(lambda x, y: x.astype(str).str.cat(y.astype(str), sep=sep), ...: [df[col] for col in cols]) ...: In [4]: df['cat'] = str_join(df, '-', 'c0', 'c1', 'c2', 'c3')
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