在pandas数据帧中，如何将值添加到iterrow中的每一行的末尾？

Question

假设我有一个简单的数据框架，其中包含产品、当前价格和平均价格。我想使用当前价格和平均价格来计算零售价。我的尝试是：

import csv
from pandas import Series, DataFrame
import pandas as pd
import os

frame = pd.read_csv('/ ... /data.csv')

for index, row in frame.iterrows():    
  product = row['product']
  price = row['price']    
  ave_price = row['ave_price']
  weight_price = 2.0
  max_price = ave_price * weight_price

  retail_price = max_price / (1.0 + 2.0 * price / ave+price)
  retail_total = rs_price * 1.0875

frame.to_csv('/Users ... /output.csv', encoding ='utf-8')

如何获取retail_total并将其添加到打印整个数据帧的方式，包括产品、当前价格、平均价格和零售价格？

当我尝试这样做时，它只填充所有产品的零售价作为产品列表中的最后一个：

Answer 1

将柱添加到框架中：

frame['retail_price'] = Series(np.zeros(len(frame)), index=frame.index)

然后将每一行的值存储在for循环中

for index, row in frame.iterrows():    
  product = row['product']
  price = row['price']    
  ave_price = row['ave_price']
  weight_price = 2.0
  max_price = ave_price * weight_price

  retail_price = max_price / (1.0 + 2.0 * price / ave_price)
  retail_total = retail_price * 1.0875

  row['retail_price'] = retail_price

Answer 2

import pandas as pd
import numpy as np

# simulate some artificial data
# ===========================================
np.random.seed(0)
product = np.random.choice(list('ABCDE'), size=10)
price = np.random.randint(100, 200, size=10)
avg_price = np.random.randint(100, 200, size=10)
df = pd.DataFrame(dict(product=product, price=price, avg_price=avg_price))
df

   avg_price  price product
0        125    188       E
1        177    112       A
2        172    158       D
3        109    165       D
4        120    139       D
5        180    187       B
6        169    146       D
7        179    188       C
8        147    181       E
9        164    137       A


# processing
# ===========================================
# some constant parameters
weight_price = 2.0

df['retail_price'] = df['avg_price'] * weight_price / (1.0 + 2.0 * df['price'] / df['avg_price'])

df['retail_total'] = df['retail_price'] * 1.0875

df

   avg_price  price product  retail_price  retail_total
0        125    188       E       62.3752       67.8331
1        177    112       A      156.2544      169.9266
2        172    158       D      121.2459      131.8549
3        109    165       D       54.1276       58.8637
4        120    139       D       72.3618       78.6935
5        180    187       B      116.9675      127.2022
6        169    146       D      123.9089      134.7509
7        179    188       C      115.4631      125.5661
8        147    181       E       84.9077       92.3371
9        164    137       A      122.8128      133.5589

# df.to_csv()