Pivot a groupby object in Pandas?

Question

I have the following dataframe:

df = pd.DataFrame([
        [123, 'abc', '121'],
        [124, 'abc', '121'],
        [456, 'def', '121'],
        [123, 'abc', '122'],
        [123, 'abc', '122'],
        [456, 'def', '145'],
        [456, 'def', '145'],
        [456, 'def', '146'],
    ], columns=['userid', 'name', 'dt'])

I have grouped it according to the date: df2 = df.groupby('dt').apply(lambda df: df.reset_index(drop=True))

Now, the dataframe looks like this:

Now, I want to pivot the above such that they are in this format: userid name_1, name_2, ..., name_k for each group such that the end df looks something like this:

userid   name
123      abc
124      abc
456      def
123      abc, abc

Answer 1

You can use cumcount with pivot_table , where parameter index use columns userid and dt , so it looks like create df2 is not necessary:

df['cols'] = 'name_' + (df.groupby(['userid','dt']).cumcount() + 1).astype(str)

print (df.pivot_table(index=['userid', 'dt'],columns='cols', values='name', aggfunc=''.join))
cols       name_1 name_2
userid dt               
123    121    abc   None
       122    abc    abc
124    121    abc   None
456    121    def   None
       145    def    def
       146    def   None

Answer 2

Check out groupby and apply . Their respective docs are here and here . You can unstack ( docs ) the extra level of the MultiIndex that is created.

df = df.set_index(['userid', 'dt'])['name']
df = df.groupby(level=[0,1]).apply(
    lambda st: pd.Series(st.values, index=['name_%i'%i for i in range(st.shape[0])])
).unstack(level=-1)
print(df)

outputs

           name_0 name_1
userid dt               
123    121    abc   None
       122    abc    abc
124    121    abc   None
456    121    def   None
       145    def    def
       146    def   None