Scrapy框架
1.官方文档
官网链接：https://docs.scrapy.org/en/latest/topics/architecture.html
The data flow in Scrapy is controlled by the execution engine, and goes like this:
- The Engine gets the initial Requests to crawl from the Spider.
- The Engine schedules the Requests in the Scheduler and asks for the next Requests to crawl.
- The Scheduler returns the next Requests to the Engine.
- The Engine sends the Requests to the Downloader, passing through the Downloader Middlewares (see
process_request()). - Once the page finishes downloading the Downloader generates a Response (with that page) and sends it to the Engine, passing through the Downloader Middlewares (see
process_response()). - The Engine receives the Response from the Downloader and sends it to the Spider for processing, passing through the Spider Middleware (see
process_spider_input()). - The Spider processes the Response and returns scraped items and new Requests (to follow) to the Engine, passing through the Spider Middleware (see
process_spider_output()). - The Engine sends processed items to Item Pipelines, then send processed Requests to the Scheduler and asks for possible next Requests to crawl.
- The process repeats (from step 1) until there are no more requests from the Scheduler.
2.scrapy各组件
Components：
1、引擎(EGINE)
引擎负责控制系统所有组件之间的数据流，并在某些动作发生时触发事件。有关详细信息，请参见上面的数据流部分。
2、调度器(SCHEDULER)
用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL的优先级队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址。
3、下载器(DOWLOADER)
用于下载网页内容, 并将网页内容返回给EGINE，下载器是建立在twisted这个高效的异步模型上的。
4、爬虫(SPIDERS)
SPIDERS是开发人员自定义的类，用来解析responses，并且提取items，或者发送新的请求。
5、项目管道(ITEM PIPLINES)
在items被提取后负责处理它们，主要包括清理、验证、持久化（比如存到数据库）等操作。
6、下载器中间件(Downloader Middlewares)位于Scrapy引擎和下载器之间，主要用来处理从EGINE传到DOWLOADER的请求request，已经从DOWNLOADER传到EGINE的响应response，
你可用该中间件做以下几件事：
(1) process a request just before it is sent to the Downloader (i.e. right before Scrapy sends the request to the website);
(2) change received response before passing it to a spider;
(3) send a new Request instead of passing received response to a spider;
(4) pass response to a spider without fetching a web page;
(5) silently drop some requests.
7、爬虫中间件(Spider Middlewares)
位于EGINE和SPIDERS之间，主要工作是处理SPIDERS的输入（即responses）和输出（即requests）
3.scrapy基本使用cmd
1、进入终端cmd：
-scrapy
2、创建scrapy项目
1.创建文件夹存放scrapy项目
-D:\Scrapy_project\
2.cmd终端输入命令
-scrapy starproject Spider_Project
会在D:\Scrapy_project\下生成文件夹
-Spider_Project ：Scrapy项目文件
3.创建好后会提示
-cd Spider_Project #切换到scrapy项目目录下
#爬虫程序名称 #目标网站域名
-scrapy genspider baidu www.baidu.com #创建爬虫程序
3.启动scrapy项目，执行爬虫程序
# 找到爬虫程序文件执行
scrapy runspider 爬虫程序.py
# 切换到爬虫程序执行目录下
-cd D:\Scrapy_project\Spider_Project\Spider_Project\spiders
-scrapy runspider baidu.py
# 根据爬虫名称找到相应的爬虫程序执行
scrapy crawl 爬虫程序名称
# 切换到项目目录下
- cd D:\Scrapy_prject\Spider_Project
- scrapy crawl baidu
** Scarpy在pycharm中的使用 **
1、创建一个py文件
from scrapy.cmdline import execute
execute() # 写scrapy执行命令
4.Scrapy在Pycharm中使用
'''
main.py
'''
from scrapy.cmdline import execute
# 写终端命令
# scrapy crawl baidu
# 执行baidu爬虫程序
# execute(['scrapy', 'crawl', 'baidu'])
# 创建爬取链家网程序
# execute(['scrapy', 'genspider', 'lianjia', 'lianjia.com'])
# --nolog 去除日志
execute('scrapy crawl --nolog lianjia'.split(' '))
'''
Scrapy在Pycharm中使用
1.创建scrapy项目
在settings.py文件中有
-ROBOTSTXT_OBEY = True #默认遵循robot协议
修改为：
-ROBOTSTXT_OBEY = False
'''
'''
lianjia.py
'''
# -*- coding: utf-8 -*-
import scrapy
from scrapy import Request
# response的类
class LianjiaSpider(scrapy.Spider):
name = 'lianjia' # 爬虫程序名
# 只保留包含lianjia.com的url
allowed_domains = ['lianjia.com'] # 限制域名
# 存放初始请求url
start_urls = ['https://bj.lianjia.com/ershoufang/']
def parse(self, response): # response返回的响应对象
# print(response)
# print(type(response))
# 获取文本
# print(response.text)
# print(response.url)
# //*[@id="position"]/dl[2]/dd/div[1]
# 获取城区列表url
area_list = response.xpath('//div[@data-role="ershoufang"]/div/a')
# 遍历所有区域列表
for area in area_list:
# print(area)
'''
.extract()提取多个
.extract_first()提取一个
'''
# 1、区域名称
area_name = area.xpath('./text()').extract_first()
# 2、区域二级url
area_url = 'https://bj.lianjia.com/' + area.xpath('./@href').extract_first()
# 会把area_url的请求响应数据交给parse_area方法
# yield后面跟着的都会添加到生成器中
yield Request(url=area_url, callback=self.parse_area)
def parse_area(self, response):
# print(response)
# 获取主页房源ul标签对象
house_list = response.xpath('//ul[@class="sellListContent"]')
# print(house_list)
if house_list:
for house in house_list:
# 房源名称
# //*[@id="leftContent"]/ul/li[1]/div/div[1]/a
house_name = house.xpath('.//div[@class="title"]/a/text()').extract_first()
print(house_name)
# 房源价格
# //*[@id="leftContent"]/ul/li[1]/div/div[4]/div[2]/div[1]/span
house_cost = house.xpath('.//div[@class="totalPrice"]/span/text()').extract_first() + '万'
print(house_cost)
# 房源单价
# //*[@id="leftContent"]/ul/li[1]/div/div[4]/div[2]/div[2]/span
house_price = house.xpath('.//div[@class="unitPrice"]/span/text()').extract_first()
print(house_price)
# yield Request(url='下一级url', callback=self.parse_area)
pass
5.微信好友统计
from wxpy import Bot
from pyecharts import Pie
import webbrowser
# 实例化一个微信机器人对象
bot = Bot()
# 获取到微信的所有好友
friends = bot.friends()
# 设定男性\女性\位置性别好友名称
attr = ['男朋友', '女朋友', '人妖']
# 初始化对应好友数量
value = [0, 0, 0]
# 遍历所有的好友,判断这个好友是男性还是女性
for friend in friends:
if friend.sex == 1:
value[0] += 1
elif friend.sex == 2:
value[1] += 1
else:
value[2] += 1
# 实例化一个饼状图对象
pie = Pie('Forver的好友们!')
# 图表名称str，属性名称list，属性所对应的值list，is_label_show是否现在标签
pie.add('', attr, value, is_label_show=True)
# 生成一个html文件
pie.render('friends.html')
# 打开html文件
webbrowser.open('friends.html')
