最初に行の値を追加し、ラベルのサブセットに基づいて重複する行を削除します。

Question

私はこのコードを持っています。私は、次の達成したい

Class            Offpeak Onpeak 
Constraint_ID Constraint Name          
1049   EAU CLA TR9 FLO EAU CLR XF10   -46.52 -364.68 
1607   OTTUMWA-WAPELLO FLO HILLS-MONTEZUM  -2.60 -237.36 
285770  DKSN-MATTHSON FLO BELFLD-CHRLIE CK   NaN -59.53 
       MATTHSON MATTHDKSN_11_1 1 WAU34028  43.66  NaN 
       MATTHSON_MATTHDKSN_11_1_1_LN    6.55  NaN 
287090  BAKER2_TR11_TR11_XF      11.78 1.63 
289484  BAKER2 TR12 TR12 WAUMDU13     NaN -4.52 
       BAKER2_TR12_TR12_XF      -10.41  NaN 

：どのような援助が大幅に高く評価

1) Add the values for Offpeak and Onpeak columns where Constraint_ID is same. For example:Constraint_ID=285770 has three different Constraint Names and corresponding values. 
2) Drop duplicate Constraint_IDs keeping the first Constraint Name 
3) Create a third column that adds OffPeak and Onpeak 

import pandas as pd 
import urllib.request 
import numpy as np 

url="https://www.misoenergy.org/Library/Repository/Market%20Reports/20170811_da_bc.xls" 
cnstxls = urllib.request.urlopen(url) 
xl = pd.ExcelFile(cnstxls) 
df = xl.parse("Sheet1",skiprows=3) 
constr = df.iloc[:,1:7] 
constr['Class'] = np.where(constr['Hour of Occurrence'].isin([1,2,3,4,5,6]), 'Offpeak', 'Onpeak') 
summary=pd.pivot_table(constr, index=['Constraint_ID','Constraint Name'],values='Shadow Price', columns=['Class'], aggfunc=np.sum) 

は、ここで上記のコードが生成する出力の一部です。

Answer 1

IIUC：

df_out = summary.reset_index().groupby('Constraint_ID')\ 
       .agg({'Constraint Name':'first','Offpeak':'sum','Onpeak':'sum'}) 

df_out['Total'] = df_out['Offpeak'].add(df_out['Onpeak'],fill_value=0)) 

出力：

         Constraint Name Offpeak Onpeak Total 
Constraint_ID                 
1049     EAU CLA TR9 FLO EAU CLR XF10 -46.52 -364.68 -411.20 
1607    OTTUMWA-WAPELLO FLO HILLS-MONTEZUM -2.60 -237.36 -239.96 
4636    KNOXVIL-LUCAS FLO BEACON-TRICOUNTY  NaN -276.62 -276.62 
4862        NEAST_K-11_2_2_LN  NaN 28.03 28.03 
9712        VVWNSP TR1 TR1 BASE  NaN 0.84 0.84 
98333   HORN-TRENTONC FLO NAVARRE 230/120 201  NaN -112.05 -112.05 
107318   LOSTDAU-REDMAPL FLO HIGHWAY22-MORGAN  NaN -96.10 -96.10 
188144     EQIN EQIN-HAM-4 A ALW16X45 26.32  NaN 26.32 
201082    BOGLSA AT3 FLO FRANKLIN-MCKNIGHT  NaN -245.44 -245.44 
248010      OLIVER OLIVROWEND1 1 BASE  NaN 1.48 1.48 
267644   DANVL3-DODSON FLO MT OLIVE-LAYFIELD  NaN -73.25 -73.25 
268470     HENSEL_HENSEDRAYT11_1_1_LN  NaN 1.14 1.14 
270366    LMR-PONDER FLO CONROE BLK-PNDR  NaN -80.71 -80.71 
270888   DUMAS-REEDX FLO WOLF CREEK-MCADAMS  NaN -19.68 -19.68 
281292    NSES-RAM452 FLO BLACKBERRY-NEOSHO -33.80 -780.05 -813.85 
285770   DKSN-MATTHSON FLO BELFLD-CHRLIE CK 50.21 -59.53 -9.32 
287090       BAKER2_TR11_TR11_XF 11.78 1.63 13.41 
289484      BAKER2 TR12 TR12 WAUMDU13 -10.41 -4.52 -14.93