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

Another way of doing this is to first added a "helper" column on to your data, then pivot your dataframe using the "helper" column, in the case below "ID_Count":

Using @unutbu setup:

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)})
#Create group
grp = df.groupby('Group')

#Create helper column 
df['ID_Count'] = grp['ID'].cumcount() + 1

#Pivot dataframe using helper column and add 'Value' column to pivoted output.
df_out = df.pivot('Group','ID_Count','ID').add_prefix('ID').assign(Value = grp['Value'].sum())

Output:

ID_Count  ID1  ID2  ID3  Value
Group                         
A         1.0  4.0  NaN     77
B         3.0  NaN  NaN     84
C         2.0  5.0  6.0     86

Answer 2

Using get_dummies and MultiLabelBinarizer (scikit-learn):

import pandas as pd
import numpy as np
from sklearn import preprocessing
df = pd.DataFrame()
df['Group']=['A','C','B','A','C','C']
df['ID']=[1,2,3,4,5,6]
df['Value']=np.random.randint(1,100,6)

mlb = preprocessing.MultiLabelBinarizer(classes=classes).fit([])

df2 = pd.get_dummies(df, '', '', columns=['ID']).groupby(by='Group').sum()
df3 = pd.DataFrame(mlb.inverse_transform(df2[df['ID'].unique()].values), index=df2.index)
df3.columns = ['ID' + str(x + 1) for x in range(df3.shape[0])]
pd.concat([df3, df2['Value']], axis=1)


       ID1  ID2  ID3  Value
Group                      
A        1    4  NaN     63
B        3  NaN  NaN     59
C        2    5    6    230

Answer 3

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