如何遍历Dataframe生成API请求，批量更新电商商品及变体价格？

将电子表格数据导入包含商品信息的Dataframe后，需要通过API发送PUT请求更新电商平台的商品价格，核心需求是：

• 按Product_Id分组处理，同一商品的多个变体信息需合并到同一个请求体中
• 根据商品是否有变体、变体数量，匹配对应的API请求格式

示例Dataframe如下：

API请求场景说明 #

场景1：商品无变体 #

当Variations_id为空时，直接更新商品基础价格：

import requests
import json

token_key = "你的令牌"
base_url = "some_api_url"

product_id = "id005"
price = 130

url = base_url + str(product_id)
body = {'price': int(price)}

payload = json.dumps(body)
headers = {
    'Authorization': f'Bearer {token_key}',
    'Content-Type': 'application/json'
}

response = requests.request("PUT", url, headers=headers, data=payload)
print(response.text)

场景2：商品有单个变体 #

商品仅存在一个变体时，请求体包含单个变体的ID和价格：

import requests
import json

token_key = "你的令牌"
base_url = "some_api_url"

product_id = "id001"
variation_id = "v0101"
price = 100

url = base_url + str(product_id)
body = {
    "variations": [{
        "id": str(variation_id),
        "price": int(price)
    }]   
}

payload = json.dumps(body)
headers = {
    'Authorization': f'Bearer {token_key}',
    'Content-Type': 'application/json'
}

response = requests.request("PUT", url, headers=headers, data=payload)
print(response.text)

场景3：商品有两个及以上变体 #

需将该商品的所有变体ID和价格全部加入请求体，遗漏任意变体将导致该变体从平台消失：

import requests
import json

token_key = "你的令牌"
base_url = "some_api_url"

product_id = "id003"

body =  {
    "variations": [
        {"id": "v0301", "price": 110},
        {"id": "v0302", "price": 115}
    ]
}

payload = json.dumps(body)
headers = {
    'Authorization': f'Bearer {token_key}',
    'Content-Type': 'application/json'
}

response = requests.request("PUT", url, headers=headers, data=payload)
print(response.text)

完整实现代码 #

以下代码实现Dataframe的遍历、分组和请求发送逻辑：

import pandas as pd
import requests
import json

# 初始化Dataframe（实际场景从电子表格导入，比如pd.read_excel/pd.read_csv）
data = [
    ["id001", "v0101", 100],
    ["id002", "v0201", 120],
    ["id003", "v0301", 110],
    ["id003", "v0302", 115],
    ["id004", "v0401", 120],
    ["id005", "", 130]
]
df = pd.DataFrame(data, columns=["Product_Id", "Variations_id", "Price"])

# 处理空值：将空字符串转为None，方便判断
df["Variations_id"] = df["Variations_id"].replace("", None)

token_key = "你的令牌"
base_url = "some_api_url"
headers = {
    'Authorization': f'Bearer {token_key}',
    'Content-Type': 'application/json'
}

# 按Product_Id分组遍历
for product_id, group in df.groupby("Product_Id"):
    # 获取当前商品的所有变体数据，过滤掉空值（无变体的情况）
    variations = group.dropna(subset=["Variations_id"]).to_dict("records")
    
    url = base_url + str(product_id)
    if len(variations) == 0:
        # 场景1：无变体，取第一条数据的价格
        price = group.iloc[0]["Price"]
        body = {'price': int(price)}
    else:
        # 场景2/3：有变体，生成变体列表
        body = {
            "variations": [
                {"id": str(v["Variations_id"]), "price": int(v["Price"])} 
                for v in variations
            ]
        }
    
    # 发送请求
    payload = json.dumps(body)
    response = requests.request("PUT", url, headers=headers, data=payload)
    print(f"商品{product_id}更新结果：{response.text}")

关键说明 #

1. 分组处理：通过groupby("Product_Id")将同一商品的所有行聚合，确保变体不会被拆分
2. 空值处理：提前将空字符串转为None，避免误判变体存在
3. 变体完整性：对于多变体商品，必须包含所有变体数据，否则会导致平台上的变体丢失
4. 请求复用：将请求头提前定义，避免重复生成，提升代码效率

内容的提问来源于stack exchange，提问作者Luis R