获取某东的里的信息,并保存至csv,mysql(类)

# -*- coding: utf-8 -*-
# @Time    : 2018/12/28  12:01
# @Author  : zhangxinxin
# @Email   : 778786617@qq.com
# @Software: PyCharm
import requests
import time
import json
import csv
import pymysql.cursors


class JdComment(object):
    def __init__(self):
        self.url = 'https://sclub.jd.com/comment/productPageComments.action'
        self.headers = {
            'authority': 'sclub.jd.com',
            'cookie': '您的cookie',
            'referer': 'https://item.jd.com/100000287113.html',
            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36'
        }
        self.js_content = ''
        self.conn = None
        self.cursor = None
        self.headers_csv()

    def connect_sql(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            port=3306,
            user='您的账号',
            password='您的密码',
            db='jd'
        )
        self.cursor = self.conn.cursor()

    def close_sql(self):
        self.cursor.close()
        self.conn.close()

    def get_html(self, params):
        """获取返回信息,对信息进行分割,去掉无用信息"""
        # resp = requests.get(self.url, headers=self.headers).text
        # self.js_content = resp.split('15267(')[-1].split(');')[0]
        self.js_content = requests.get(self.url, headers=self.headers, params=params).text
        # print(self.js_content)

    def parse_html(self):
        content_dict = json.loads(self.js_content)
        comments = content_dict['comments']
        data_list = []
        for data in comments:
            small_list = []
        #     超复杂,写了一半,舍弃
        #     id = data['id']
        #     nickname = ['nickname']
        #     content = data['content']
        #     # creationTime = data['creationTime']
        #     referenceName = data['referenceName']
        #     usefulVoteCount = data['usefulVoteCount']
        #     replyCount = data['replyCount']
        #     score = data['score']
        # #     images = data['images']
        # #     # for image in images:
        # #         # print(image['imgUrl'])
        # #     print(id, content)
        # # goodRateShow = content_dict['productCommentSummary']['goodRateShow']
        # # comments = content_dict['comments']
        # # for data in comments:
        #     userLevelName = data['userLevelName']
        #     color = data['productColor']
        #     size = data['productSize']
        # 中等复杂, 已写完
            small_list.append(data['id'])
            small_list.append(data['nickname'])
            small_list.append(data['content'])
            small_list.append(data['referenceName'])
            small_list.append(data['usefulVoteCount'])
            small_list.append(data['replyCount'])
            small_list.append(data['score'])
            small_list.append(data['userLevelName'])
            small_list.append(data['productColor'])
            small_list.append(data['productSize'])
            data_list.append(small_list)
        # 精简模式,已写完,暂时不用
        #     kes=['id', 'nickname', 'content', '此处数据待添加']
        #     for k in kes:
        #         print(small_list.append(data[k]))
        #     data_list.append(small_list)
        #
        # print(len(data_list))
        return data_list

    def headers_csv(self):
        """创建headers, 初始化时写入"""
        with open('Jd_js_comments.csv', 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow(['用户ID', '用户名', '评论内容', '手机信息', '点赞数', '评论数', '排序规则', '会员等级', '手机颜色', '型号'])

    def save_csv(self, data):
        """存储到csv文件中"""
        with open('Jd_js_comments.csv', 'a', newline='') as f:
            writer = csv.writer(f)
            writer.writerows(data)

    def save_sql(self, data):
        """数据写入数据库"""
        self.connect_sql()
        for i in data:
            print(i)
            sql = "INSERT INTO jd.comments VALUES (NULL , '{}','{}','{}','{}','{}','{}','{}','{}','{}','{}')".format(str(i[0]), str(i[1]), i[2], i[3], str(i[4]), str(i[5]), str(i[6]), i[7], i[8], i[9])
            self.cursor.execute(sql)
            self.conn.commit()
        self.close_sql()

    def run(self):
        for x in range(10):
            page = x
            params = {
                'productId': 5089253,
                'sortType': 5,
                'score': 0,
                'page': page,
                'pageSize': 10,
                # 'callback': 'fetchJSON_comment98vv105467',
                # 'isShadowSku': 0,
                # 'fold': 1
            }
            self.get_html(params)
            data = self.parse_html()
            self.save_csv(data)
            self.save_sql(data)
            print('第{}页数据爬取完毕,等待数据保存'.format(x + 1))
            time.sleep(5)


if __name__ == '__main__':
    s = JdComment()
    s.run()

提供MySQL数据库图:

 

Scrapy是一个强大的Python爬虫框架,用于高效地抓取网络数据。如果你想要利用Scrapy爬取租房信息将其保存CSV文件和MySQL数据库,可以按照以下步骤操作: 1. **安装依赖**: - 安装Scrapy库:`pip install scrapy` - 如果需要处理CSV,安装pandas库:`pip install pandas` - 对于MySQL连接,安装mysqlclient或pymysql:`pip install mysql-connector-python` 2. **创建Scrapy项目**: - 使用命令行创建一个新的Scrapy项目:`scrapy startproject rental_scraper` 3. **定义爬虫**: - 在`rental_scraper/spiders`目录下编写Spider,如`rental_spider.py`。设置开始URL、解析规则等,提取租房信息,比如房源标题、价格、地址等。 4. **处理数据**: - 在Spider的解析函数(如parse())中,将获取的数据存入一个字典或者list中。 - 对于CSV,你可以用pandas创建DataFrame,然后使用`to_csv()`方法保存数据: ```python import pandas as pd data = ... # 从解析结果中提取的数据 df = pd.DataFrame(data) df.to_csv('rentals.csv', index=False) # 将数据保存CSV文件 ``` 5. **连接MySQL**: - 需要在项目的settings.py中配置MySQL连接: ```python DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'your_database_name', 'USER': 'your_username', 'PASSWORD': 'your_password', 'HOST': 'localhost', 'PORT': '', } } ``` - 使用SQLAlchemy或Scrapy的内置支持(如果有的话),例如使用items(Item Pipeline)将数据持久化到数据库: ```python from scrapy.pipelines.sqlitemongo import SqlitePipeline or from scrapy.contrib.pipeline.mysql import MySQLPipeline pipeline_classes = [ MyRentalsPipeline, # 自定义的保存租房数据的管道 SqlitePipeline, # 或者MySQLPipeline ] ``` 6. **自定义管道**: - 创建一个自定义的pipeline(如MyRentalsPipeline),在这个,你需要实现`process_item()`方法来处理数据将它们插入到MySQL表中。 7. **运行爬虫**: - 使用`scrapy crawl rental_spider`启动爬虫。
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