scrapy数据清洗:
在爬取数据过程中,有些数据不是我们需要的,或者有的数据格式不符合我们的要求,需要进行处理然后在进行保存,传统的方法就是在items中定义我们需要的字段,例如:

class ShetuItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    img_name = scrapy.Field()
    img_url = scrapy.Field()
    img_time = scrapy.Field()
    img_looknum = scrapy.Field()
    img_collect = scrapy.Field()
    img_down = scrapy.Field()

然后在爬虫文件中

def xiangxi_parse(self,response):

        item = ShetuItem()
        #照片名字
        img_name = response.xpath('//div[@class="photo-view"]/h1/text()')[0].extract()
        #照片url
        img_url = response.xpath('//div[@class="huabu"]/a/img/@src')[0].extract()
        #照片发布时间
        img_time = response.xpath('//div[@class="photo-view"]/div/span[@class="publicityt"]/text()')[0].extract()
        #照片浏览量
        img_looknum = response.xpath('//div[@class="photo-view"]/div/span[@class="look"]/read/text()')[0].extract()
        # 收藏量
        img_collect = response.xpath('//div[@class="photo-view"]/div/span[@class="collect"]/text()')[0].extract().strip("收藏")
        #下载量
        img_down = response.xpath('//div[@class="photo-view"]/div/span[@class="download"]/text()')[1].extract().strip("\n\t下载")

        for field in item.fields.keys():  # 取出所有的键
            item[field] = eval(field)
        yield item

假设我们需要对name,url等进行处理,在这里 可能就要许多判断,造成代码冗余,不美观,为了解决代码简洁的问题我们可以瞎用如下的方法,重新定义items.py

import scrapy

from scrapy.loader import ItemLoader
from scrapy import Field
from scrapy.loader.processors import MapCompose,TakeFirst

def str_convert(value):
    return  'country_' + value

def get_nums(value):
    return value.replace(',','')
class CountryItemLoader(ItemLoader):
    #定义一个默认的输入处理器 全局的  TakeFirst()取出数组的第一个相当于extract_first
    default_output_processor = TakeFirst()
class CountryproItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    title = Field(
        #的定义一个输入处理器,这里将处理器映射到函数中,进行数据清洗
        input_processor = MapCompose(str_convert),
    )
    population = Field(
        input_processor = MapCompose(get_nums),
    )

然后在爬虫 文件中

# -*- coding: utf-8 -*-
import scrapy
from scrapy import Request

from countrypro.items import CountryproItem,CountryItemLoader


class CountrySpider(scrapy.Spider):
    name = 'country'
    allowed_domains = ['example.com']
    def __init__(self):
        self.urls = ['http://example.webscraping.com/places/default/view/China-47']
    def start_requests(self):
        for url_str in self.urls:
            yield Request(url_str,callback=self.parse,dont_filter = True)


    def parse(self, response):
        item = CountryItemLoader(item = CountryproItem(),response=response)
        item.add_css('title','tr#places_country__row td.w2p_fw::text')
        # item.add_xpath()
        item.add_css('population', 'tr#places_population__row td.w2p_fw::text')
        return item.load_item()