Pandas groupby result into multiple columns
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