Python爬取京东：价格、商品ID、标题、评价、店名、是否自营

import requests
from lxml import etree
import time
import random
import pandas as pd
import json
from sqlalchemy import create_engine
from sqlalchemy.dialects.oracle import DATE,FLOAT,NUMBER,VARCHAR2  
import cx_Oracle

def create_table(table_name):
      conn = cx_Oracle.connect(‘user/password@IP:port/database‘)  
      cursor = conn.cursor ()
      create_shouji = ‘‘‘      CREATE TABLE {}(      商品ID VARCHAR2(256),      价格 number(19,8),      店名 VARCHAR2(256) ,      店属性 VARCHAR2(256) ,      标题 VARCHAR2(256) ,      评论 NUMBER(19),      优评论 NUMBER(19)      )      ‘‘‘.format(table_name)
      cursor.execute(create_shouji)
      cursor.close()
      conn.close()

def mapping_df_types(df_pro):
      dtypedict = {}
      for i, j in zip(df_pro.columns, df_pro.dtypes):
            if "object" in str(j):
                  dtypedict.update({i: VARCHAR2(256)})
            if "float" in str(j):
                  dtypedict.update({i: NUMBER(19,8)})
            if "int" in str(j):
                  dtypedict.update({i: NUMBER(19,8)}) 
            if "datetime" in str(j):
                  dtypedict.update({i: DATE})
      return dtypedict

def sava_oracle(df_pro):
      engine = create_engine(‘oracle://user:password@ip:port/database‘)
      dtypedict = mapping_df_types(df_pro)
      df_pro.to_sql("shouji",con=engine,index=False,if_exists=‘append‘,dtype=dtypedict)

headers={
      ‘user-agent‘: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36 Edg/83.0.478.37‘
}
def requesturl(url):
      session = requests.Session()
      rep = session.get(url,headers=headers)
      return rep

def commreq(url_comm):
      dd_commt = pd.DataFrame(columns=[‘商品ID‘,‘评论‘,‘优评论‘])
      session = requests.Session()
      rep_comm = session.get(url_comm,headers=headers)
      comment = json.loads(rep_comm.text)[‘CommentsCount‘]
      comment_list = []
      for i in comment:
            comment_list.append({‘商品ID‘:str(i[‘ProductId‘]),‘评论‘:i[‘CommentCount‘],‘优评论‘:i[‘GoodCount‘]})
      dd_commt = dd_commt.append(comment_list)
      return dd_commt

def parse(rep):
      df = pd.DataFrame(columns=[‘商品ID‘,‘价格‘,‘店名‘,‘店属性‘,‘标题‘])
      html = etree.HTML(rep.text)
      all_pro = html.xpath("//ul[@class=‘gl-warp clearfix‘]/li")
      proid = ‘,‘.join(html.xpath("//li/@data-sku"))
      # 商品评价url
      # referenceIds=之后到&callback之前,都是商品的id,只需要在商品列表获取商品id拼接即可
      # 1. 评论解析
      url_comm = r‘https://club.jd.com/comment/productCommentSummaries.action?referenceIds={}‘.format(proid)
      dd_commt = commreq(url_comm)
      # 2. 商品列表信息解析
      pro_list = []
      for product in all_pro:
            proid = ‘‘.join(product.xpath("@data-sku"))
            price = ‘‘.join(product.xpath("div[@class=‘gl-i-wrap‘]//strong/i/text()"))
            target = ‘‘.join(product.xpath("div[@class=‘gl-i-wrap‘]//a/em//text()")).replace(‘\t\n‘,‘‘).replace(‘\\u2122‘,‘‘)
            shopname = ‘‘.join(product.xpath("div[@class=‘gl-i-wrap‘]//span/a/@title"))
            shoptips = product.xpath("div[@class=‘gl-i-wrap‘]//i[contains(@class,‘goods-icon‘)]/text()")
            if ‘自营‘ in shoptips:
                  shoptips=‘自营‘
            else:
                  shoptips=‘非自营‘            
            pro_list.append(dict(商品ID=proid,价格=price,店名=shopname,店属性=shoptips,标题=target))
      df = df.append(pro_list)
      # 3. 合并商品评论和列表
      df_pro = pd.merge(df,dd_commt,on=‘商品ID‘)
      return df_pro

if __name__ == "__main__":
      create_table(‘shouji‘)
      for i in range(1,81):
            url = ‘https://search.jd.com/s_new.php?keyword=手机&wq手机&ev=3613_104528%5E&page={0}&s=30‘.format(i)
            rep = requesturl(url)
            df_pro = parse(rep)
            sava_oracle(df_pro)
            time.sleep(random.randrange(1,4))
            print(‘完成:‘,i)