指定pandas某两行的数据相加，如果是列相加，直接data[‘列1’]=data[‘列2’]+data[‘列3’]即可，但行相加，则没有直接可用方法，采用下面的sum()的方法可以实现两行或多行相加

data.loc[heji[0]]=data.loc[data[‘p’].isin(heji[1])].sum()
data.loc[heji[0],‘p’]=heji[0]

import pandas as pd
chengji = [['N', 95,0], ['N', 100,88], ['N', 88,100],['N', 66,0]]
data=pd.DataFrame(chengji ,columns=['p','x','g'])
data1=data[['p','x']].copy()
data1['p']=data1.index.map(lambda x:"N"+str(x+1))
print(data1)

data.loc[:,'p']=data.index.map(lambda x:"N"+str(x+1))
print(data)
data.columns=['p','营业成本本年累计','营业收入本年累计',]
print(data)
data['Q']=0
data.loc[data['营业收入本年累计']!=0,'Q']=data['营业成本本年累计']/data['营业收入本年累计']
print(data)
heji=['N1+N2',['N1','N2']]
#data.loc[heji[0]]=[np.nan for _ in range(len(data.columns))]	#新加行一定要有index,否则报错
# for countnum,partname in enumerate(heji[1]):
# 	if countnu
print(data.loc[data['p'].isin(heji[1])])
data.loc[heji[0]]=data.loc[data['p'].isin(heji[1])].sum()
data.loc[heji[0],'p']=heji[0]
#data.loc[heji[0]]=[np.nan for _ in range(len(data.columns))]
print(data)

效果如下：

          p  营业成本本年累计  营业收入本年累计         Q
0        N1        95         0  0.000000
1        N2       100        88  1.136364
2        N3        88       100  0.880000
3        N4        66         0  0.000000
N1+N2  N1N2       195        88  1.136364

大佬指点，可以以用data.loc[0]=data.loc[0]+data.loc[1]的方法，改造代码为：

# 方法二
# data.loc[0]=data.loc[0]+data.loc[1] #此种方法可以用
data.loc[heji[0]] = data.loc[data[data['p'] == heji[1][0]].index[0]] + \
    data.loc[data[data['p'] == heji[1][1]].index[0]]

继续改进一点

# 方法二
# data.loc[0]=data.loc[0]+data.loc[1] #此种方法可以用
print(data.index[data['p'] == heji[1][0]][0])
data.loc[heji[0]] = data.loc[data.index[data['p'] == heji[1][0]][0]] + \
    data.loc[data.index[data['p'] == heji[1][1]][0]]

相除方法和相加方法的综合整理版

import numpy as np
import pandas as pd
chengji = [['N', 95, 0], ['N', 100, 88], ['N', 88, 100], ['N', 66, 0]]
data = pd.DataFrame(chengji, columns=['p', 'x', 'g'])
data1 = data[['p', 'x']].copy()
data1['p'] = data1.index.map(lambda x: "N"+str(x+1))
print(data1)

data.loc[:, 'p'] = data.index.map(lambda x: "N"+str(x+1))
print(data)
data.columns = ['p', '营业成本本年累计', '营业收入本年累计', ]
print(data)


def get_benrate(series):
    shouru = series['营业收入本年累计']
    chengben = series['营业成本本年累计']
    if shouru == 0:
        return 0
    else:
        return chengben/shouru*100
# 方法1
# data['Q']=0
# data.loc[data['营业收入本年累计']!=0,'Q']=data['营业成本本年累计']/data['营业收入本年累计']

# 方法2
# 当axis=0时，对每列columns执行指定函数；当axis=1时，对每行row执行指定函数。#
# 无论axis=0还是axis=1，其传入指定函数的默认形式均为Series，
# data['Q']=data.apply(get_benrate,axis=1)

# 方法3
# data['Q'] = data['营业成本本年累计']/data['营业收入本年累计']*100
# data['Q'].replace([np.inf, -np.inf, "", np.nan], 0, inplace=True)

# 方法4
data['Q'] = data['营业成本本年累计']/data['营业收入本年累计']
data.loc[np.isinf(data['Q']), "Q"] = 0


print(data)
heji = ['N1+N2', ['N1', 'N2']]
# print(data.loc[data['p'].isin(heji[1])])

# 方法一
# data.loc[heji[0]]=data.loc[data['p'].isin(heji[1])].sum()
# data.loc[heji[0],'p']=heji[0]
# data.drop(data['p'].isin(heji[1]),inplace=True)

# 方法二
# data.loc[0]=data.loc[0]+data.loc[1] #此种方法可以用
# 方法二.1
#data.loc[heji[0]] = data.loc[data[data['p'] == heji[1][0]].index[0]] + data.loc[data[data['p'] == heji[1][1]].index[0]]# 
# 方法二.2
# print(data.index[data['p'] == heji[1][0]][0])
# data.loc[heji[0]] = data.loc[data.index[data['p'] == heji[1][0]][0]] + \
#     data.loc[data.index[data['p'] == heji[1][1]][0]]
# 方法三。1
data=data.append(data.loc[data['p'].isin(heji[1])].sum(axis=0),ignore_index=True)

# 方法四
# print (type(data[data['p'] == heji[1][0]].values + data[data['p'] == heji[1][1]].values[0]))
# data.loc[heji[0]] = (data[data['p'] == heji[1][0]].values + data[data['p'] == heji[1][1]].values)[0]

# 方法五 综合三、四
print(data[data['p'] == heji[1][0]].values + data[data['p'] == heji[1][1]].values[0])
data=data.append (pd.DataFrame(data[data['p'] == heji[1][0]].values + data[data['p'] == heji[1][1]].values,columns=data.columns),ignore_index=True)
print(data)

运行效果

    p    x
0  N1   95
1  N2  100
2  N3   88
3  N4   66
    p    x    g
0  N1   95    0
1  N2  100   88
2  N3   88  100
3  N4   66    0
    p  营业成本本年累计  营业收入本年累计
0  N1        95         0
1  N2       100        88
2  N3        88       100
3  N4        66         0
    p  营业成本本年累计  营业收入本年累计         Q
0  N1        95         0  0.000000
1  N2       100        88  1.136364
2  N3        88       100  0.880000
3  N4        66         0  0.000000
[['N1N2' 195 88 1.1363636363636365]]
      p 营业成本本年累计 营业收入本年累计        Q
0    N1       95        0        0
1    N2      100       88  1.13636
2    N3       88      100     0.88
3    N4       66        0        0
4  N1N2      195       88  1.13636

***Repl Closed***