python_ scrapy_ Fang Tianxia

osc_x4ot1joy 2020-11-08 08:04:32
python_ python scrapy_ scrapy fang

scrapy- Explain

xpath Select node The common tag elements are as follows .

Mark describe
extract The extracted content is converted to Unicode character string , The return data type is list
/ Select from root node
// Match the selected current node, select the node in the document
. node
@ attribute
* Any element node
@* Any attribute node
node() Any type of node

Climb to take the house world - Prelude

1、 website :url:
2、 Determine what data to crawl :1) Web address :page.2) Location name :name.3) Price :price.4) Address :address.5) Phone number :tel
2、 Analyze the web page .
 Insert picture description here

 open url after , We can see the data we need , Then you can see that there are still pagination .

 Insert picture description here

 You can see the opening url Then look at the page elements , All the data we need are in a pair of ul tag .

 Insert picture description here
 Insert picture description here

 open li A couple of labels , What we need name Is in a Under the label , And there are unclear spaces around the text, such as line feed, need special treatment .
What we need price Is in 55000 Under the label , Be careful , Some houses have been bought without price display , Step on this pit carefully .
We can find the corresponding by analogy address and tel.

 Insert picture description here

 The pagination tag element shows , Of the current page a Of class="active". In opening the home page is a The text of is 1, It means the first page .

Climb to take the house world - Before the specific implementation process

First new scrapy project
1) Switch to the project folder :Terminal Input... On the console scrapy startproject hotel,hotel It's the project name of the demo , You can customize it according to your own needs .
2) On demand Folder configuration parameters . Five parameters are needed in the analysis , Namely :page,name,price,address,tel. The configuration code is as follows :

class HotelItem(scrapy.Item):
 # The parameters here should correspond to the specific parameters of the crawler implementation 
page = scrapy.Field()
name = scrapy.Field()
price = scrapy.Field()
address = scrapy.Field()
tel = scrapy.Field()

3) Build our new reptile Branch . Switch to spiders Folder ,Terminal Input... On the console scrapy genspider house sh.newhouse.fang.comhouse Is the crawler name of the project , You can customize , It's an area selection for crawling .
stay spider Under the folder we created The file .
The code implementation and explanation are as follows

import scrapy
from ..items import *
class HouseSpider(scrapy.Spider):
name = 'house'
# Crawling area restrictions 
allowed_domains = ['']
# The main page of crawling 
start_urls = ['',]
def start_requests(self):
for url in self.start_urls:
# Return the module name passed by the function , There are no brackets . It's a convention .
yield scrapy.Request(url=url,callback=self.parse)
def parse(self, response):
items = []
# Get the value displayed on the current page 
for p in response.xpath('//a[@class="active"]/text()'):
# extract Convert the extracted content to Unicode character string , The return data type is list
# Determine the last page 
for last in response.xpath('//a[@class="last"]/text()'):
# Switch to the nearest layer of tags .// Select the node in the document from the current node that matches the selection , Regardless of their location / Select from root node 
for each in response.xpath('//div[@class="nl_con clearfix"]/ul/li/div[@class="clearfix"]/div[@class="nlc_details"]'):
# name 
name=each.xpath('//div[@class="house_value clearfix"]/div[@class="nlcd_name"]/a/text()').extract()
# Price 
# Address 
address=each.xpath('//div[@class="relative_message clearfix"]/div[@class="address"]/a/@title').extract()
# Telephone 
tel=each.xpath('//div[@class="relative_message clearfix"]/div[@class="tel"]/p/text()').extract()
# all item The parameters in it have to do with us items The meaning of the parameters in it corresponds to 
item['name'] = [n.replace(' ', '').replace("\n", "").replace("\t", "").replace("\r", "") for n in name]
item['price'] = [p for p in price]
item['address'] = [a for a in address]
item['tel'] = [s for s in tel]
item['page'] = [''+(str)(eval(p.extract())+1)+'/?']
# When crawling to the last page , Class label last Automatically switch to the home page 
if lastpage==' home page ':
# If it's not the last page , Continue crawling to the next page of data , Know all the data 
yield scrapy.Request(url=''+(str)(eval(currentpage)+1)+'/?', callback=self.parse)

4) stay spiders Run the crawler under ,Terminal Input... On the console scrapy crawl house.
The results are shown in the following figure
 Insert picture description here
The overall project structure is shown on the right tts The folder is used to store data on my side txt file . There is no need for this project .
 Insert picture description here
If you find any errors, please contact wechat :sunyong8860
python Crawling along the road


  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database