Pandas groupby result into multiple columns

Question

I have a dataframe in which I\'m looking to group and then partition the values within a group into multiple columns.

For example: say I have the following dataframe

Answer 1

You could use

id_df = grouped['ID'].apply(lambda x: pd.Series(x.values)).unstack()

to create id_df without the intermediate result DataFrame.

---

import pandas as pd
import numpy as np
np.random.seed(2016)

df = pd.DataFrame({'Group': ['A', 'C', 'B', 'A', 'C', 'C'],
                   'ID': [1, 2, 3, 4, 5, 6],
                   'Value': np.random.randint(1, 100, 6)})

grouped = df.groupby('Group')
values = grouped['Value'].agg('sum')
id_df = grouped['ID'].apply(lambda x: pd.Series(x.values)).unstack()
id_df = id_df.rename(columns={i: 'ID{}'.format(i + 1) for i in range(id_df.shape[1])})
result = pd.concat([id_df, values], axis=1)
print(result)

yields

       ID1  ID2  ID3  Value
Group                      
A        1    4  NaN     77
B        3  NaN  NaN     84
C        2    5    6     86