Python-groupby上的Pandas小计

Question

这是使用的数据示例：

SCENARIO    DATE    POD         AREA    IDOC    STATUS  TYPE
AAA   02.06.2015    JKJKJKJKJKK 4210    713375  51         1
AAA   02.06.2015    JWERWERE    4210    713375  51         1
AAA   02.06.2015    JAFDFDFDFD  4210    713375  51         9
BBB   02.06.2015    AAAAAAAA    5400    713504  51        43
CCC   05.06.2015    BBBBBBBBBB  4100    756443  51       187
AAA   05.06.2015    EEEEEEEE    4100    756457  53       228

我已经在pandas中将以下代码编写为groupby：

import pandas as pd
import numpy as np

xl = pd.ExcelFile("MRD.xlsx")
df = xl.parse("Sheet3") 
#print (df.column.values)

# The following gave ValueError: Cannot label index with a null key
# dfi = df.pivot('SCENARIO)

# Here i do not actually need it to count every column, just a specific one
table = df.groupby(["SCENARIO", "STATUS", "TYPE"]).agg(['count'])
writer = pd.ExcelWriter('pandas.out.xlsx', engine='xlsxwriter')
table.to_excel(writer, sheet_name='Sheet1')
writer.save()


table2 = pd.DataFrame(df.groupby(["SCENARIO", "STATUS", "TYPE"])['TYPE'].count())
print (table2)
writer2 = pd.ExcelWriter('pandas2.out.xlsx', engine='xlsxwriter')
table2.to_excel(writer2, sheet_name='Sheet1')
writer2.save()

这产生一个结果：

SCENARIO  STATUS  TYPE    TYPE
AAA       51      1       2
                  9       1
          53      228     1
BBB       51      43      1
CCC       51      187     1
Name: TYPE, dtype: int64   

如何为每个组添加小计？理想情况下，我希望实现以下目标：

SCENARIO  STATUS  TYPE    TYPE
AAA       51      1       2
                  9       1
          Total           3
          53      228     1
          Total           1
BBB       51      43      1
          Total           1
CCC       51      187     1
          Total           1
Name: TYPE, dtype: int64   

这可能吗？

Answer 1

使用：

#if necessary convert TYPE column to string
df['TYPE'] = df['TYPE'].astype(str)
df = df.groupby(["SCENARIO", "STATUS", "TYPE"])['TYPE'].count()

#aggregate sum by first 2 levels
df1 = df.groupby(["SCENARIO", "STATUS"]).sum()
#add 3 level of MultiIndex 
df1.index = [df1.index.get_level_values(0),
            df1.index.get_level_values(1),
            ['Total'] * len(df1)]

#thanks MaxU for improving
#df1 = df1.set_index(np.array(['Total'] * len(df1)), append=True) 

print (df1)
SCENARIO  STATUS       
AAA       51      Total    3
          53      Total    1
BBB       51      Total    1
CCC       51      Total    1
Name: TYPE, dtype: int64

#join together and sorts
df = pd.concat([df, df1]).sort_index(level=[0,1])
print (df)
SCENARIO  STATUS  TYPE 
AAA       51      1        2
                  9        1
                  Total    3
          53      228      1
                  Total    1
BBB       51      43       1
                  Total    1
CCC       51      187      1
                  Total    1
Name: TYPE, dtype: int64