学习笔记 scrapy 爬虫框架

矫情吗；* 2022-09-16 05:58 369阅读 0赞

## scrapy 入门 ##

**1.创建一个scrapy项目**  
scrapy startproject myspider  
**2.生成一个爬虫**  
scrapy genspider itcast itcast.cn  
**3.提取数据**  
完善spider，使用xpath等方法  
**4.保存数据**  
pipeline中保存数据

## 解释 ##

class ItcastSpider(scrapy.Spider):
        name = 'itcast'   #爬虫名字
        allowed_domains = ['itcast.cn']   #允许爬取的范围
        start_urls = ['https://maoyan.com/board']    #最开始请求的url范围

ret1 = response.xpath("//div[@class='maincon']//h2/text()").extract() 
    extract解释：提取所有的文字信息。

## 操作命令 ##

cd +文件名：进入到项目文件内。  
tree：查看树。  
scrapy crawl itcast：启动爬虫。  
dir：查看当前目录中的文件和文件夹；

## 学习进度 ##

itcast.py文件

**①**

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5bCP5pyx5a2m6ZW_size_20_color_FFFFFF_t_70_g_se_x_16]  
**②**

import scrapy
    class ItcastSpider(scrapy.Spider):
        name = 'itcast'
        allowed_domains = ['itcast.cn']
        start_urls = ['https://www.itcast.cn/channel/teacher.shtml']
    
        def parse(self, response):
            #处理start_urls地址对应的响应
            #ret1 = response.xpath("//div[@class='maincon']//h2/text()").extract()
            #print(ret1)
            #分组
            li_list = response.xpath("//div[@class='maincon']//li")
            for li in li_list:
                item = {}
                item["name"] = li.xpath(".//h2/text()").extract()[0]
                item["title"] = li.xpath(".//span/text()").extract()[0]
                #item["入职时间"] = li.xpath(".//h3/text()").extract()
                print(item)

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5bCP5pyx5a2m6ZW_size_20_color_FFFFFF_t_70_g_se_x_16 1]

## 完整项目 ##

**爬虫文件**

import scrapy
    import json
    import requests
    import numpy as np
    import pandas as pd
    from pandas import DataFrame
    #2067111119 朱博
    #由于内蒙古科技大学acm源码不可看，我发现如果获取到公开的URl接口，用json的方法获取，后转为python对象，也可以实现了作业需要的功能。
    #在设置里开启了LOG_LEVEL="WARNING"过滤掉了日志，方便调试。
    class PaimingSpider(scrapy.Spider):
        name = 'paiming'
        allowed_domains = ['paiming.cn']
        start_urls = ['https://imustacm.cn/#/imustoj/rankList/1']
        def parse(self, response):
            url = "https://imustacm.cn/api/problem/listRank/1"
            response = requests.get(url=url).text
            json_res = json.loads(response)
            user_list = json_res['data']['us']
            #创建ndarray
            x = np.array(user_list, dtype =str)  #将转为py对象的user_list存入x为作业二准备
            #创建Series
            y = pd.Series(user_list)  #将转为py对象的user_list存入y为作业三准备
            #创建DataFrame
            z = DataFrame(user_list)  #将转为py对象的user_list存入z为作业四准备
            a_list={}
            for user in user_list:
                a_list["user_id"]=user["user_id"]
                a_list["nick"]=user["nick"]
                a_list["professional"]=user["professional"]
                a_list["school"]=user["school"]
                if a_list["professional"]==None:
                    a_list["professional"]='未知'
                if a_list["school"]==None:
                    a_list["school"]='未知'
                yield(a_list)
    #scrapy crawl paiming   运行爬虫文件可以完整的爬取排行榜的各项数据，并且完整的存入csv文件，经过检测，完成了作业一的各项要求。

**管道**

# Define your item pipelines here
    #
    # Don't forget to add your pipeline to the ITEM_PIPELINES setting
    # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html
    # useful for handling different item types with a single interface
    from itemadapter import ItemAdapter
    class ImustacmPipeline:
        with open('D:/内蒙古科技大学acm排名表.csv','a', encoding="utf-8") as a:
            a.write("姓名"+","+"学号"+","+"专业"+","+"学校"+"\n")
        def process_item(self, item, spider):
            print(item)
            with open('D:/内蒙古科技大学acm排名表.csv','a', encoding="utf-8") as a:
                a.write(item["user_id"]+","+item["nick"]+","+item["professional"]+","+item["school"]+"\n")
            return item

**设置文件**

# Scrapy settings for imustacm project
    #
    # For simplicity, this file contains only settings considered important or
    # commonly used. You can find more settings consulting the documentation:
    #
    #     https://docs.scrapy.org/en/latest/topics/settings.html
    #     https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
    #     https://docs.scrapy.org/en/latest/topics/spider-middleware.html
    
    BOT_NAME = 'imustacm'
    
    SPIDER_MODULES = ['imustacm.spiders']
    NEWSPIDER_MODULE = 'imustacm.spiders'
    
    LOG_LEVEL="WARNING"
    # Crawl responsibly by identifying yourself (and your website) on the user-agent
    #USER_AGENT = 'imustacm (+http://www.yourdomain.com)'
    
    # Obey robots.txt rules
    ROBOTSTXT_OBEY = True
    
    # Configure maximum concurrent requests performed by Scrapy (default: 16)
    #CONCURRENT_REQUESTS = 32
    
    # Configure a delay for requests for the same website (default: 0)
    # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
    # See also autothrottle settings and docs
    #DOWNLOAD_DELAY = 3
    # The download delay setting will honor only one of:
    #CONCURRENT_REQUESTS_PER_DOMAIN = 16
    #CONCURRENT_REQUESTS_PER_IP = 16
    
    # Disable cookies (enabled by default)
    #COOKIES_ENABLED = False
    
    # Disable Telnet Console (enabled by default)
    #TELNETCONSOLE_ENABLED = False
    
    # Override the default request headers:
    #DEFAULT_REQUEST_HEADERS = {
    #   'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
    #   'Accept-Language': 'en',
    #}
    
    # Enable or disable spider middlewares
    # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
    #SPIDER_MIDDLEWARES = {
    #    'imustacm.middlewares.ImustacmSpiderMiddleware': 543,
    #}
    
    # Enable or disable downloader middlewares
    # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
    #DOWNLOADER_MIDDLEWARES = {
    #    'imustacm.middlewares.ImustacmDownloaderMiddleware': 543,
    #}
    
    # Enable or disable extensions
    # See https://docs.scrapy.org/en/latest/topics/extensions.html
    #EXTENSIONS = {
    #    'scrapy.extensions.telnet.TelnetConsole': None,
    #}
    
    # Configure item pipelines
    # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
    ITEM_PIPELINES = {
        'imustacm.pipelines.ImustacmPipeline': 300,
    }
    
    # Enable and configure the AutoThrottle extension (disabled by default)
    # See https://docs.scrapy.org/en/latest/topics/autothrottle.html
    #AUTOTHROTTLE_ENABLED = True
    # The initial download delay
    #AUTOTHROTTLE_START_DELAY = 5
    # The maximum download delay to be set in case of high latencies
    #AUTOTHROTTLE_MAX_DELAY = 60
    # The average number of requests Scrapy should be sending in parallel to
    # each remote server
    #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
    # Enable showing throttling stats for every response received:
    #AUTOTHROTTLE_DEBUG = False
    
    # Enable and configure HTTP caching (disabled by default)
    # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
    #HTTPCACHE_ENABLED = True
    #HTTPCACHE_EXPIRATION_SECS = 0
    #HTTPCACHE_DIR = 'httpcache'
    #HTTPCACHE_IGNORE_HTTP_CODES = []
    #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

**numpy**

import numpy as np
    import pandas as pd
    import json
    import os
    import csv
    with open('内蒙古科技大学acm排名表.csv',"r",encoding="utf-8") as a:
        #city_list = json.load(a)
        reader = csv.reader(a)
        list=[]
        for row in reader:
          list.append(row)
    x = np.array(list, dtype =str)
    #数据的截取 截取了前20位同学的信息。
    y=x[1:20]
    #数据的排序，按第一个元素从小到大进行排序。
    z=np.lexsort([y[:,2], y[:,1], y[:,0]])
    sorted_data = y[z, :]
    print(sorted_data)

series

import pandas as pd
    import csv
    with open('内蒙古科技大学acm排名表.csv',"r",encoding="utf-8") as a:
        #city_list = json.load(a)
        reader = csv.reader(a)
        list=[]
        for row in reader:
            list.append(row)
    y = pd.Series(list)
    #print(y)
    #作业一结果的Series，进行截取操作,这里截取了前20位同学的排名信息。
    x=y[1:21]
    print(x)

**DataFrame**

import numpy as np
    import pandas as pd
    import json
    import os
    import csv
    from pandas import DataFrame
    with open('内蒙古科技大学acm排名表.csv',"r",encoding="utf-8") as a:
        reader = csv.reader(a)
        list=[]
        for row in reader:
    
            list.append(row)
    #DataFrame对象有所不同，它有两个索引数组。
    #分组
    
    z = DataFrame(list[1:21], columns=["学号", "姓名", "专业","学校"])
    print(z)
    #使用count函数计算得出前二十位同学的专业统计总数。
    g = z.groupby(['专业']).count()
    print(g)

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5bCP5pyx5a2m6ZW_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/f2a3be9c0fbb4543a46a25e6a531e7cf.png
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