R&S®ZN-Z8x 开关矩阵

Python爬虫学习（四）

• 线程池
• 获取北京新发地网页中的蔬菜价格等信息并写入csv文件

线程池

一次性开辟一些线程。用户直接给线程池提交任务，线程任务的调度交给线程池来完成

from concurrent.futures import ThreadPoolExecutor

def fn(name):
    for i in range(100):
        print(name, i)


if __name__ == '__main__':
    # 创建线程池
    with ThreadPoolExecutor(50) as t:
        for i in range(100):
            t.submit(fn, name=f'线程{i}')

    # 等待线程池中的任务全部执行完毕，才继续执行（守护）
    print("123")

获取北京新发地网页中的蔬菜价格等信息并写入csv文件

思路：

1. 先实现提取单个页面的数据
2. 上线程池，多个页面同时抓取

# 爬取北京新发地网页中的蔬菜价格等信息并写入csv文件
import csv
import time
import requests
from concurrent.futures import ThreadPoolExecutor

hearders = {
    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0',
}

f = open('data_file/' + 'veg_price_data_multi_thread.csv', mode='w', encoding='utf-8')
csvwriter = csv.writer(f)


def download_one_page(url, page):
    data = {
        'limit': '20',
        'current': page,
    }
    # 拿到页面源代码
    resp = requests.post(url, data=data, headers=hearders)
    # print(resp.status_code)
    resp.encoding = 'utf-8'

    # print(resp.json())

    vegPriceList = dict(resp.json())['list']

    for veg in vegPriceList:
        veg_name = veg['prodName']
        veg_lowPrice = veg['lowPrice']
        veg_avgPrice = veg['avgPrice']
        veg_hightPrice = veg['highPrice']
        veg_place = veg['place']

        veg_list = [veg_name, veg_lowPrice, veg_avgPrice, veg_hightPrice, veg_place]

        # print(veg_name, veg_lowPrice, veg_avgPrice, veg_hightPrice, veg_place)
        csvwriter.writerow(veg_list)

    print(f'第{page}页，提取成功！')


if __name__ == '__main__':
    url = 'http://www.xinfadi.com.cn/getPriceData.html'
    # download_one_page(url, 10)

    # 单线程下载数据
    # for i in range(1, 100):
    #     download_one_page(url, i)
    #     time.sleep(3)

    # 线程池下载数据
    with ThreadPoolExecutor(50) as t:
        for i in range(1, 100):
            # 把下载任务提交给线程池
            t.submit(download_one_page, url=url, page=i)
            # 注意：不加会导致爬取几页就结束，应该是网页增加了反爬取机制，但是加了会导致线程池的性能失效
            time.sleep(1)

    print('全部下载完毕！')