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You can do

df_out = df.groupby(0).agg(list).reset_index() df_out[[1, 2]] = pd.DataFrame(df_out[1].values.tolist()) 

giving you

  0 1 2 0 Andrew running cars 1 John guitar dancing 2 Michael football None 

Assuming the column names being ['person', 'activity'] you can do

df_out = df.groupby('person').agg(list).reset_index() df_out = pd.concat([df_out, pd.DataFrame(df_out['activity'].values.tolist())], axis=1) df_out = df_out.drop('activity', 1) 

giving you

 person 0 1 0 Andrew running cars 1 John guitar dancing 2 Michael football None 

Given the df being

 0 1 0 John guitar 1 Michael football 2 Andrew running 3 John dancing 4 Andrew cars 

You can do

df_out = df.groupby(0).agg(list).reset_index() pd.concat([df_out.drop(0, 1), pd.DataFrame(df_out[1].values.tolist())], axis=1) 

giving you

 0 1 2 0 Andrew running cars 1 John guitar dancing 2 Michael football None 

You can do

df_out = df.groupby(0).agg(list).reset_index() df_out[[1, 2]] = pd.DataFrame(df_out[1].values.tolist()) 

giving you

 0 1 2 0 Andrew running cars 1 John guitar dancing 2 Michael football None 

You can do

df_out = df.groupby(0).agg(list).reset_index() df_out[[1, 2]] = pd.DataFrame(df_out[1].values.tolist()) 

giving you

 0 1 2 0 Andrew running cars 1 John guitar dancing 2 Michael football None 

Given the df being

 0 1 0 John guitar 1 Michael football 2 Andrew running 3 John dancing 4 Andrew cars 

You can do

df_out = df.groupby(0).agg(list).reset_index() pd.concat([df_out.drop(0, 1), pd.DataFrame(df_out[1].values.tolist())], axis=1) 

giving you

 0 1 2 0 Andrew running cars 1 John guitar dancing 2 Michael football None