1.创建项目
scrapy startproject weather # weather是项目名称
scrapy crawl spidername开始运行，程序自动使用start_urls构造Request并发送请求，然后调用parse函数对其进行解析，
在这个解析过程中使用rules中的规则从html（或xml）文本中提取匹配的链接，通过这个链接再次生成Request，如此不断循环，直到返回的文本中再也没有匹配的链接，或调度器中的Request对象用尽，程序才停止。
2.确定爬取目标:
scrapy构建的爬虫的爬取过程:
scrapy crawl spidername开始运行，程序自动使用start_urls构造Request并发送请求，然后调用parse函数对其进行解析，在这个解析过程中使用rules中的规则从html（或xml）文本中提取匹配的链接，
通过这个链接再次生成Request，如此不断循环，直到返回的文本中再也没有匹配的链接，或调度器中的Request对象用尽，程序才停止。
allowed_domains:顾名思义,允许的域名,爬虫只会爬取该域名下的url
rule:定义爬取规则,爬虫只会爬取符合规则的url
rule有allow属性,使用正则表达式书写匹配规则.正则表达式不熟悉的话可以写好后在网上在线校验,尝试几次后,简单的正则还是比较容易的,我们要用的也不复杂.
rule有callback属性可以指定回调函数,爬虫在发现符合规则的url后就会调用该函数,注意要和默认的回调函数parse作区分.(爬取的数据在命令行里都可以看到)
rule有follow属性.为True时会爬取网页里所有符合规则的url,反之不会. 我这里设置为了False,因为True的话要爬很久.大约两千多条天气信息
import scrapy
from weather.items import WeatherItem
from scrapy.spiders import Rule, CrawlSpider
from scrapy.linkextractors import LinkExtractor
class Spider(CrawlSpider):
name = 'weatherSpider'
#allowed_domains = "www.weather.com.cn"
start_urls = [
#"http://www.weather.com.cn/weather1d/101020100.shtml#search"
"http://www.weather.com.cn/forecast/"
]
rules = (
#Rule(LinkExtractor(allow=('http://www.weather.com.cn/weather1d/101\d{6}.shtml#around2')), follow=False, callback='parse_item'),
Rule(LinkExtractor(allow=('http://www.weather.com.cn/weather1d/101\d{6}.shtml$')), follow=True,callback='parse_item'),
)
#多页面爬取时需要自定义方法名称,不能用parse
def parse_item(self, response):
item = WeatherItem()
#city = response.xpath("//div[@class='crumbs fl']/a[2]/text()").extract_first()
item['city'] = response.xpath("//div[@class='crumbs fl']/a[2]/text()").extract_first() # 获取省或者直辖市名称
#if city == '>':
#item['city'] = response.xpath("//div[@class='crumbs fl']/a[last()-1]/text()").extract_first()#获取非直辖省
#item['city'] = response.xpath("//div[@class ='crumbs fl']/a[2]/text()").extract_first()#获取直辖市
#item['city_addition'] = response.xpath("//div[@class ='crumbs fl']/a[last()]/text()").extract_first()#获取直辖市
#city_addition = response.xpath("//div[@class ='crumbs fl']/a[last()]/text()").extract_first() #获取>字符
#print("aaaaa"+city)
#print("nnnnn"+city_addition)
#if city_addition != city:
#item['city_addition'] = response.xpath("//div[@class='crumbs fl']/a[2]/text()").extract_first()
item['city_addition'] = response.xpath("//div[@class ='crumbs fl']/a[last()]/text()").extract_first() # 获取城市名或者直辖市名称
#else:
#item['city_addition'] = ''
#item['city_addition2'] = response.xpath("//div[@class='crumbs fl']/span[3]/text()").extract_first()
weatherData = response.xpath("//div[@class='today clearfix']/input[1]/@value").extract_first() #获取当前的气温
item['data'] = weatherData[0:6] #获取日期
print("data:"+item['data'])
item['weather'] = response.xpath("//p[@class='wea']/text()").extract_first() #获取天气
item['temperatureMax'] = response.xpath("//ul[@class='clearfix']/li[1]/p[@class='tem']/span[1]/text()").extract_first() #最高温度
item['temperatureMin'] = response.xpath("//ul[@class='clearfix']/li[2]/p[@class='tem']/span[1]/text()").extract_first() #最低温度
yield item
spider.py顾名思义就是爬虫文件
在填写spider.py之前,我们先看看如何获取需要的信息
刚才的命令行应该没有关吧,关了也没关系
win+R在打开cmd,键入:scrapy shell http://www.weather.com.cn/weather1d/101020100.shtml#search #网址是你要爬取的url
这是scrapy的shell命令,可以在不启动爬虫的情况下，对网站的响应response进行处理调试等,主要是调试xpath获取元素的
3.填写Items.py
Items.py只用于存放你要获取的字段:
给自己要获取的信息取个名字:
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
class WeatherItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
city = scrapy.Field()
city_addition = scrapy.Field()
city_addition2 = scrapy.Field()
weather = scrapy.Field()
data = scrapy.Field()
temperatureMax = scrapy.Field()
temperatureMin = scrapy.Field()
pass
这里写了管道文件,还要在settings.py设置文件里启用这个pipeline:
6.填写settings.py
# -*- coding: utf-8 -*-
# Scrapy settings for weather project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
# https://doc.scrapy.org/en/latest/topics/settings.html
# https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
BOT_NAME = 'weather'
SPIDER_MODULES = ['weather.spiders']
NEWSPIDER_MODULE = 'weather.spiders'
# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'weather (+http://www.yourdomain.com)'
# Obey robots.txt rules
ROBOTSTXT_OBEY = False
# 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://doc.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
DOWNLOAD_DELAY = 1
# 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://doc.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
# 'weather.middlewares.WeatherSpiderMiddleware': 543,
#}
# Enable or disable downloader middlewares
# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
# 'weather.middlewares.WeatherDownloaderMiddleware': 543,
#}
# Enable or disable extensions
# See https://doc.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
# 'scrapy.extensions.telnet.TelnetConsole': None,
#}
# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
'weather.pipelines.TxtPipeline': 600,
#'weather.pipelines.JsonPipeline': 6,
#'weather.pipelines.ExcelPipeline': 300,
}
# Enable and configure the AutoThrottle extension (disabled by default)
# See https://doc.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://doc.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'
5.填写pipeline.py
但要保存爬取的数据的话,还需写下pipeline.py
# -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
import os
import codecs
import json
import csv
from scrapy.exporters import JsonItemExporter
from openpyxl import Workbook
base_dir = os.getcwd()
filename = base_dir + '\\' + 'weather.txt'
with open(filename,'w+') as f:#打开文件
f.truncate()#清空文件内容
class JsonPipeline(object):
# 使用FeedJsonItenExporter保存数据
def __init__(self):
self.file = open('weather1.json','wb')
self.exporter = JsonItemExporter(self.file,ensure_ascii =False)
self.exporter.start_exporting()
def process_item(self,item,spider):
print('Write')
self.exporter.export_item(item)
return item
def close_spider(self,spider):
print('Close')
self.exporter.finish_exporting()
self.file.close()
class TxtPipeline(object):
def process_item(self, item, spider):
#获取当前工作目录
#base_dir = os.getcwd()
#filename = base_dir + 'weather.txt'
#print('创建Txt')
print("city:"+item['city'])
print("city_addition:"+item['city_addition'])
#从内存以追加方式打开文件,并写入对应的数据
with open(filename, 'a') as f: #追加
if item['city'] != item['city_addition']:
f.write('城市:' + item['city'] + '>')
f.write(item['city_addition'] + '\n')
else:
f.write('城市:' + item['city'] + '\n')
#f.write(item['city_addition'] + '\n')
f.write('日期:' + item['data'] + '\n')
f.write('天气:' + item['weather'] + '\n')
f.write('温度:' + item['temperatureMin'] + '~' + item['temperatureMax'] + '℃\n')
class ExcelPipeline(object):
#创建EXCEL,填写表头
def __init__(self):
self.wb = Workbook()
self.ws = self.wb.active
#设置表头
self.ws.append(['省', '市', '县(乡)', '日期', '天气', '最高温', '最低温'])
def process_item(self, item, spider):
line = [item['city'], item['city_addition'], item['city_addition2'], item['data'], item['weather'], item['temperatureMax'], item['temperatureMin']]
self.ws.append(line) #将数据以行的形式添加仅xlsx中
self.wb.save('weather.xlsx')
return item
'''def process_item(self, item, spider):
base_dir = os.getcwd()
filename = base_dir + 'weather.csv'
print('创建EXCEL')
with open(filename,'w') as f:
fieldnames = ['省','市', '县(乡)', '天气', '日期', '最高温','最低温'] # 定义字段的名称
writer = csv.DictWriter(f,fieldnames=fieldnames) # 初始化一个字典对象
write.writeheader() # 调用writeheader()方法写入头信息
# 传入相应的字典数据
write.writerow(dict(item))
'''
爬虫效果：
确定爬取目标:
这里选择中国天气网做爬取素材,爬取网页之前一定要先分析网页,要获取那些信息,怎么获取更加方便,网页源代码这里只展示部分:
<div class="ctop clearfix">
<div class="crumbs fl">
<a href="http://js.weather.com.cn" target="_blank">江苏</a>
<span>></span>
<a href="http://www.weather.com.cn/weather/101190801.shtml" target="_blank">徐州</a><span>></span> <span>鼓楼</span>
</div>
<div class="time fr"></div>
</div>
如果是非直辖市：获取省名称
//div[@class='crumbs fl']/a[last()-1]/text()
取xpath最后一个book元素
book[last()]
取xpath最后第二个book元素
book[last()-1]
作者：Agoly 本文版权归作者所有，欢迎转载，但未经作者同意必须保留此段声明，且在文章页面明显位置给出原文连接，否则保留追究法律责任的权利。 如果文中有什么错误，欢迎指出。以免更多的人被误导。 |
取xpath最后一个book元素
book[last()]
取xpath最后第二个book元素
book[last()-1]
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
