i have a df that looks like this:
a date c 0 ABC 2020-06-01 0.1 1 ABC 2020-05-01 0.2 2 DEF 2020-07-01 0.3 3 DEF 2020-01-01 0.4 4 DEF 2020-02-01 0.5 5 DEF 2020-07-01 0.6 i would like to "unstack" column 'a' so my new df looks like this
a date1 c1 date2 c2 date3 c3 date4 c4 0 ABC 2020-06-01 0.1 2020-05-01 0.2 nan nan nan nan 1 DEF 2020-07-01 0.3 2020-01-01 0.4 2020-02-01 0.5 2020-07-01 0.6 how can i do this?
Use GroupBy.cumcount for helper counter for MultiIndex and reshape by DataFrame.unstack, then for correct order is used DataFrame.sort_index with map for flatten MultiIndex:
df = (df.set_index(['a',df.groupby('a').cumcount().add(1)]) .unstack() .sort_index(axis=1, level=[1, 0], ascending=[True, False])) df.columns = df.columns.map(lambda x: f'{x[0]}{x[1]}') df = df.reset_index() print (df) a date1 c1 date2 c2 date3 c3 date4 c4 0 ABC 2020-06-01 0.1 2020-05-01 0.2 NaN NaN NaN NaN 1 DEF 2020-07-01 0.3 2020-01-01 0.4 2020-02-01 0.5 2020-07-01 0.6 Or if sorting is not possible because different columns names one idea is use DataFrame.reindex:
df1 = df.set_index(['a',df.groupby('a').cumcount().add(1)]) mux = pd.MultiIndex.from_product([df1.index.levels[1], ['date','c']]) df = df1.unstack().swaplevel(1,0, axis=1).reindex(mux, axis=1) df.columns = df.columns.map(lambda x: f'{x[1]}{x[0]}') df = df.reset_index() print (df) a date1 c1 date2 c2 date3 c3 date4 c4 0 ABC 2020-06-01 0.1 2020-05-01 0.2 NaN NaN NaN NaN 1 DEF 2020-07-01 0.3 2020-01-01 0.4 2020-02-01 0.5 2020-07-01 0.6 
df.set_index(['a',df.groupby('a').cumcount().add(1)]) to df.set_index(['a','col', df.groupby(['a', 'col1'] ).cumcount().add(1)])