Python去除价格字符串中的€并转为int以计算年均车价

问题 #

• 爬取二手车网站时，获取的价格数据带有€符号，需要转为int类型计算年均车价，但触发错误：ValueError: invalid literal for int() with base 10: 'price'，尝试方案未解决。
• 年份为字符串类型，是否需要转为int以便运算？

爬虫代码 #

import requests
import pandas as pd
from bs4 import BeautifulSoup


url = "https://jammer.ie/used-cars?page={}&per-page=12"

all_data = []

for page in range(1, 4):  # <-- increase number of pages here
    soup = BeautifulSoup(requests.get(url.format(page)).text, "html.parser")

    for car in soup.select(".car"):
        info = car.select_one(".top-info").get_text(strip=True, separator="|")
        info = info.split("|")
        if len(info) == 4:
            make, model, year, price = info
        else:
            make, year, price = info
            model = "N/A"
        dealer_name = car.select_one(".dealer-name h6").get_text(
            strip=True, separator=" "
        )
        address = car.select_one(".address").get_text(strip=True)

        features = {}
        for feature in car.select(".car--features li"):
            k = feature.img["src"].split("/")[-1].split(".")[0]
            v = feature.span.text
            features[f"feature_{k}"] = v

        all_data.append(
            {
                "make": make,
                "model": model,
                "year": year,
                "price": price,
                "dealer_name": dealer_name,
                "address": address,
                "url": "https://jammer.ie"
                + car.select_one("a[href*=vehicle]")["href"],
                **features,
            }
        )

df = pd.DataFrame(all_data)
# prints sample data to screen:
print(df.tail().to_markdown(index=False))
# saves all data to CSV
df.to_csv("data.csv", index=False)

尝试的错误代码 #

df = pd.read_csv('data.csv', usecols= ['price','year'])

print(type("price"))
print(int("price"))

错误原因 #

你尝试的代码里int("price")是直接把字符串字面量"price"转为整数，这完全错误——应该处理DataFrame中price列的具体数值，而不是列名本身。另外爬取的价格带€符号，必须先清理符号才能转数值类型。

解决方案 #

1. 价格数据处理 #

方式一：爬取时直接处理

在爬虫代码中获取price后，立即清理符号并转int：

# 清理价格中的€和千分位逗号，转为int
price_clean = price.replace('€', '').replace(',', '').strip()
price_int = int(price_clean) if price_clean.isdigit() else None  # 处理可能的无效值

将原代码中的"price": price替换为"price": price_int即可。

方式二：读取CSV后处理

如果已经保存了CSV，读取后批量处理：

import pandas as pd

df = pd.read_csv('data.csv')
# 清理price列的符号
df['price'] = df['price'].str.replace('€', '').str.replace(',', '').str.strip()
# 转为数值类型，无效值转为NaN
df['price'] = pd.to_numeric(df['price'], errors='coerce')
# 如需int类型，填充NaN后转换
df['price'] = df['price'].fillna(0).astype(int)

2. 年份类型处理 #

如果需要用年份做数值运算（比如计算车龄：当前年份 - 车辆出厂年份），建议转为int类型：

爬取时处理

year_int = int(year) if year.isdigit() else None

替换原代码中的"year": year为"year": year_int。

读取CSV后处理

df['year'] = pd.to_numeric(df['year'], errors='coerce').fillna(0).astype(int)

修正后的完整爬虫代码 #

import requests
import pandas as pd
from bs4 import BeautifulSoup


url = "https://jammer.ie/used-cars?page={}&per-page=12"

all_data = []

for page in range(1, 4):  # <-- increase number of pages here
    soup = BeautifulSoup(requests.get(url.format(page)).text, "html.parser")

    for car in soup.select(".car"):
        info = car.select_one(".top-info").get_text(strip=True, separator="|")
        info = info.split("|")
        if len(info) == 4:
            make, model, year, price = info
        else:
            make, year, price = info
            model = "N/A"
        
        # 处理价格
        price_clean = price.replace('€', '').replace(',', '').strip()
        price_int = int(price_clean) if price_clean.isdigit() else None
        
        # 处理年份
        year_int = int(year) if year.isdigit() else None
        
        dealer_name = car.select_one(".dealer-name h6").get_text(
            strip=True, separator=" "
        )
        address = car.select_one(".address").get_text(strip=True)

        features = {}
        for feature in car.select(".car--features li"):
            k = feature.img["src"].split("/")[-1].split(".")[0]
            v = feature.span.text
            features[f"feature_{k}"] = v

        all_data.append(
            {
                "make": make,
                "model": model,
                "year": year_int,
                "price": price_int,
                "dealer_name": dealer_name,
                "address": address,
                "url": "https://jammer.ie"
                + car.select_one("a[href*=vehicle]")["href"],
                **features,
            }
        )

df = pd.DataFrame(all_data)
# prints sample data to screen:
print(df.tail().to_markdown(index=False))
# saves all data to CSV
df.to_csv("data.csv", index=False)

内容的提问来源于stack exchange，提问作者user20637309