做网站前端需要编程基础吗如何做关于旅游的网站页面

本项目纯学习使用。

1 scrapy 代码

爬取逻辑非常简单，根据url来处理翻页，然后获取到详情页面的链接，再去爬取详情页面的内容即可，最终数据落地到excel中。
经测试，总计获取 11299条中医药材数据。

import pandas as pd
import scrapyclass ZhongyaoSpider(scrapy.Spider):name = "zhongyao"start_urls = [f"https://www.zysj.com.cn/zhongyaocai/index__{i}.html" for i in range(1, 27)]def __init__(self, *args, **kwargs):self.data = []def parse(self, response):for li in response.css('div#list-content ul li'):a_tag = li.css('a')title = a_tag.css('::attr(title)').get()href = a_tag.css('::attr(href)').get()if title and href:# 构建完整的详情页 URLdetail_url = response.urljoin(href)yield scrapy.Request(detail_url, callback=self.parse_detail, meta={'title': title})# 解析逻辑def parse_detail(self, response):title = response.meta['title']pinyin = response.css('div.item.pinyin_name_phonetic div.item-content::text').get(default='').strip()alias = response.css('div.item.alias div.item-content p::text').get(default='').strip()english_name = response.css('div.item.english_name div.item-content::text').get(default='').strip()source = response.css('div.item.alias div.item-content p::text').get(default='').strip()# 性味flavor = response.css('div.item.flavor div.item-content p::text').get(default='').strip()functional_indications = response.css('div.item.flavor div.item-content p::text').get(default='').strip()usage = response.css('div.item.usage div.item-content p::text').get(default='').strip()excerpt = response.css('div.item.excerpt div.item-content::text').get(default='').strip()#habitat = response.css('div.item.habitat div.item-content p::text').get(default='').strip()# 出处provenance = response.css('div.item.provenance div.item-content p::text').get(default='').strip()# 性状shape_properties = response.css('div.item.shape_properties div.item-content p::text').get(default='').strip()# 归经attribution = response.css('div.item.attribution div.item-content p::text').get(default='').strip()#  原形态prototype = response.css('div.item.prototype div.item-content p::text').get(default='').strip()# 名家论述discuss = response.css('div.item.discuss div.item-content p::text').get(default='').strip()# 化学成分chemical_composition = response.css('div.item.chemical_composition div.item-content p::text').get(default='').strip()item = {'title': title,'pinyin': pinyin,'alias': alias,'source': source,'english_name': english_name,'habitat': habitat,'flavor': flavor,'functional_indications': functional_indications,'usage': usage,'excerpt': excerpt,'provenance': provenance,'shape_properties': shape_properties,'attribution':  attribution,'prototype': prototype,'discuss': discuss,'chemical_composition': chemical_composition,}self.data.append(item)yield itemdef closed(self, reason):# 当爬虫关闭时，保存数据到 Excel 文件df = pd.DataFrame(self.data)df.to_excel('zhongyao_data.xlsx', index=False)

2 爬取截图

3 爬取数据截图