基于Scrapy+redis+mongodb+scrapyd+scrapydweb+Pandas+BI的可视化操作分布式网络爬虫数据可视化分析

提示：所有代码已经开源到最大同性交友网站，有兴趣的朋友可以试试：Git地址
未经作者允许不得私自转发
请注明原作者：https://blog.csdn.net/qq_52420866/article/details/121915057

文章目录

• 项目背景
• 一、安装Scrapy框架
• 二、Scrapy使用步骤
  *
• 2.1 创建爬虫项目
  –
  • 2.1.1 建立好后的爬虫目录
• 2.2配置setting.py文件
• 2.3配置items.py文件
• 2.4配置pafangzi.py（爬虫）文件
  –
  • 2.4.1配置（爬虫）
  • 2.4.2 网站抓包
  • 2.4.3 编写函数
  • 2.4.4 回调函数二次解析
• 三 数据存储的pipelines.py文件
  *
• 3.1 安装连接数据库的库
• 3.2 书写pipelines.py文件
• 3.3 setting.py中配置启用mongodb
• 四 使用下载器中间件
  *
• 4.1 设置请求头
• 4.2 设置代理IP（如果你有的话^ _ ^）
• 五 改写为scrapy-Redis模拟分布式
  *
• 5.1 下载安装redis
• 5.2 打开redis
• 5.3 配置python中的设置
  –
  • 5.3.1 安装scrapy-redis
  • 5.3.2 配置
  • 5.3.3 启动分布式
• 六 使用scrapyd+scrapydweb可视化管理
  *
• 6.1 安装scrapyd
• 6.2 配置scrapyd
• 6.3 安装scrapydweb
• 6.4 运行scrapydweb（保证scrapyd服务器处于运行状态）
• 七 数据分析+大屏可视化
• 最后

项目背景

提示：项目背景：

毕业将近，大部分学生出去实习或工作面临找租房的压力，据研究发现：75％的大学生认为压力主要来源于社会就业。50％的大学生对于自己毕业后的发展前途感到迷茫，没有目标；41.7％的大学生表示目前没考虑太多；只有8．3％的人对自己的未来有明确的目标并且充满信心。很明显，就业已经是每个大学生一进校门就要考虑的事情了。中国是个人口大国，大学生毕业人数逐年上升，而社会需求的岗位数量却变化不大。根据对中国未来新增劳动力人口的测算，未来数年中国青年新增劳动力人口每年仍保持在1500~2200万之间的高位，供大于求，大学毕业生初次就业率逐年下降，直接导致就业压力大。

并且，学业压力是作为学生永远需要面对的主题，因就业压力而造成的新的学业压力也逐渐增大。大学生为了适应社会的需求，把自己变成全能型人才，这就需要学习很多功课，去拿到各种证书 来应对就业。可是人的精力是有限的，往往顾此失彼，结果是学什么都不专。大学教材更新的滞后也给学生带来更多的学习负担。学生一方面要学习学校规定的早已经不实用的课程，一方面要寻找将来能用上的课程去学习，还有各种硬性的考级科目，使学生应对无暇。因为就业压力还使更多的大学生选择了继续读硕士博士课程，为了考研，很多学生是在毕业前一两年就开始准备了，各种考研培训班到处都是，大学生既要付出时间还要付出金钱。

为了使大学生更好的在外面租到房子，避坑，并且对新房子和二手房有更加好的了解，我们从网上爬取下一定数据，并且做可视化分析，解决毕业大学生的租房子问题。

一、安装Scrapy框架

cmd中输入： pip install scrapy
如果出现下面的情况，说明已经成功
PS:windows安装scrapy有非常多的坑，有问题自己百度，可以参考：scrapy安装

; 二、Scrapy使用步骤

如果你要系统学习：scrapy官网

2.1 创建爬虫项目

进入cmd，找到想要建立工程的地方：
输入命令： scrapy startproject myspider
输入： cd myspider
建立爬虫： scrapy genspider [爬虫名] [域名]

2.1.1 建立好后的爬虫目录

; 2.2配置 setting.py 文件

然后把下面代码打开：

DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en'
}

2.3配置 items.py 文件

这个文件中是要创建的爬取实列化对象

import scrapy

class New_houseItem(scrapy.Item):

    province=scrapy.Field()

    city=scrapy.Field()

    name=scrapy.Field()

    price=scrapy.Field()

    rooms=scrapy.Field()

    area=scrapy.Field()

    district=scrapy.Field()

    sale=scrapy.Field()

    new_url=scrapy.Field()

class Old_houseItem(scrapy.Item):

    province = scrapy.Field()

    city = scrapy.Field()

    name = scrapy.Field()

    address = scrapy.Field()

    price = scrapy.Field()

    unit = scrapy.Field()

    old_url = scrapy.Field()

    infos = scrapy.Field()

2.4配置 pafangzi.py（爬虫） 文件

2.4.1配置（爬虫）

; 2.4.2 网站抓包

按F12打开调试工具

获取你要的内容：

2.4.3 编写函数

进一步解析（上代码）安排!

    def parse(self, response):

        trs = response.xpath("//div[@class = 'outCont']//tr")
        province = None

        for tr in trs:

            tds = tr.xpath(".//td[not(@class)]")

            province_text = tds[0]

            city_info = tds[1]

            province_text = province_text.xpath(".//text()").get()
            province_text = re.sub(r"\s", "", province_text)
            if province_text:
                province = province_text

            if province == "其它":
                continue

            city_links = city_info.xpath(".//a")
            for city_link in city_links:

                city = city_link.xpath(".//text()").get()

                city_url = city_link.xpath(".//@href").get()

                url_split = city_url.split("fang")
                url_former = url_split[0]
                url_backer = url_split[1]
                newhouse_url = url_former + "newhouse.fang.com/house/s/"

                old_url = url_former + "esf.fang.com/"

                yield scrapy.Request(url=newhouse_url, callback=self.parse_newhouse, meta={"info": (province, city)})

                yield scrapy.Request(url=old_url, callback=self.parse_oldhouse, meta={"info": (province, city)})

2.4.4 回调函数二次解析

    def parse_newhouse(self, response):
        province, city = response.meta.get("info")
        lis = response.xpath("//div[contains(@class, 'nl_con')]/ul/li[not(@style)]")
        for li in lis:

            name = li.xpath(".//div[@class='nlcd_name']/a/text()").get().strip()

            rooms = li.xpath(".//div[contains(@class, 'house_type')]/a//text()").getall()

            area = li.xpath(".//div[contains(@class, 'house_type')]/text()").getall()
            area = "".join(area).strip()
            area = re.sub(r"/|－|/s| |\n", "", area)

            district = li.xpath(".//div[@class = 'address']/a//text()").getall()
            district = "".join(district)
            district = re.search(r".*\[(.+)\].*", district).group(1)

            sale = li.xpath(".//div[contains(@class, 'fangyuan')]/span/text()").get().strip()

            price = li.xpath(".//div[@class = 'nhouse_price']//text()").getall()
            price = "".join(price).strip()

            new_url = li.xpath(".//div[@class = 'nlcd_name']/a/@href").get()

            item = New_houseItem(province=province, city=city, name=name, rooms=rooms, area=area,
                                 district=district, sale=sale, price=price, new_url=new_url)
            yield item

        next_url = response.xpath("//div[@class = 'page']//a[@class = 'next']/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url), callback=self.parse_newhouse,
                                 meta={"info": (province, city)})

    def parse_oldhouse(self, response):
        province, city = response.meta.get("info")
        dls = response.xpath("//div[contains(@class, 'shop_list')]/dl[@dataflag = 'bg']")
        for dl in dls:
            item =  Old_houseItem(province=province, city=city)

            name = dl.xpath(".//p[@class = 'add_shop']/a/@title").get()
            item["name"] = name

            infos = dl.xpath(".//p[@class = 'tel_shop']/text()").get().strip()
            infos = "".join(infos).strip()
            infos = re.sub(r"'|\|\r|\n|/s| ", "", infos)
            item['infos'] = infos

            address = dl.xpath(".//p[@class = 'add_shop']/span/text()").get()
            item['address'] = address

            price = dl.xpath(".//dd[@class = 'price_right']/span[1]//text()").getall()
            price = "".join(price).strip()
            item['price'] = price

            unit = dl.xpath(".//dd[@class = 'price_right']/span[2]/text()").get()
            item['unit'] = unit

            old_url = dl.xpath(".//h4[@class = 'clearfix']/a/@href").getall()
            old_url = "".join(old_url)
            old_url = response.urljoin(old_url)
            item['old_url'] = old_url
            yield item

        next_url = response.xpath("//div[@class = 'page_al']/p[last()-1]/a/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url), callback=self.parse_oldhouse,
                                 meta={"info": (province, city)})

整体代码块：

import scrapy
import re
from ..items import New_houseItem, Old_houseItem

class PafangziSpider(scrapy.Spider):
    name = 'pafangzi'
    allowed_domains = ['fang.com']
    start_urls = ['https://www.fang.com/SoufunFamily.htm']

    def parse(self, response):

        trs = response.xpath("//div[@class = 'outCont']//tr")
        province = None

        for tr in trs:

            tds = tr.xpath(".//td[not(@class)]")

            province_text = tds[0]

            city_info = tds[1]

            province_text = province_text.xpath(".//text()").get()
            province_text = re.sub(r"\s", "", province_text)
            if province_text:
                province = province_text

            if province == "其它":
                continue

            city_links = city_info.xpath(".//a")
            for city_link in city_links:

                city = city_link.xpath(".//text()").get()

                city_url = city_link.xpath(".//@href").get()

                url_split = city_url.split("fang")
                url_former = url_split[0]
                url_backer = url_split[1]
                newhouse_url = url_former + "newhouse.fang.com/house/s/"

                old_url = url_former + "esf.fang.com/"

                yield scrapy.Request(url=newhouse_url, callback=self.parse_newhouse, meta={"info": (province, city)})

                yield scrapy.Request(url=old_url, callback=self.parse_oldhouse, meta={"info": (province, city)})

    def parse_newhouse(self, response):
        province, city = response.meta.get("info")
        lis = response.xpath("//div[contains(@class, 'nl_con')]/ul/li[not(@style)]")
        for li in lis:

            name = li.xpath(".//div[@class='nlcd_name']/a/text()").get().strip()

            rooms = li.xpath(".//div[contains(@class, 'house_type')]/a//text()").getall()

            area = li.xpath(".//div[contains(@class, 'house_type')]/text()").getall()
            area = "".join(area).strip()
            area = re.sub(r"/|－|/s| |\n", "", area)

            district = li.xpath(".//div[@class = 'address']/a//text()").getall()
            district = "".join(district)
            district = re.search(r".*\[(.+)\].*", district).group(1)

            sale = li.xpath(".//div[contains(@class, 'fangyuan')]/span/text()").get().strip()

            price = li.xpath(".//div[@class = 'nhouse_price']//text()").getall()
            price = "".join(price).strip()

            new_url = li.xpath(".//div[@class = 'nlcd_name']/a/@href").get()

            item = New_houseItem(province=province, city=city, name=name, rooms=rooms, area=area,
                                 district=district, sale=sale, price=price, new_url=new_url)
            yield item

        next_url = response.xpath("//div[@class = 'page']//a[@class = 'next']/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url), callback=self.parse_newhouse,
                                 meta={"info": (province, city)})

    def parse_oldhouse(self, response):
        province, city = response.meta.get("info")
        dls = response.xpath("//div[contains(@class, 'shop_list')]/dl[@dataflag = 'bg']")
        for dl in dls:
            item =  Old_houseItem(province=province, city=city)

            name = dl.xpath(".//p[@class = 'add_shop']/a/@title").get()
            item["name"] = name

            infos = dl.xpath(".//p[@class = 'tel_shop']/text()").get().strip()
            infos = "".join(infos).strip()
            infos = re.sub(r"'|\|\r|\n|/s| ", "", infos)
            item['infos'] = infos

            address = dl.xpath(".//p[@class = 'add_shop']/span/text()").get()
            item['address'] = address

            price = dl.xpath(".//dd[@class = 'price_right']/span[1]//text()").getall()
            price = "".join(price).strip()
            item['price'] = price

            unit = dl.xpath(".//dd[@class = 'price_right']/span[2]/text()").get()
            item['unit'] = unit

            old_url = dl.xpath(".//h4[@class = 'clearfix']/a/@href").getall()
            old_url = "".join(old_url)
            old_url = response.urljoin(old_url)
            item['old_url'] = old_url
            yield item

        next_url = response.xpath("//div[@class = 'page_al']/p[last()-1]/a/@href").get()
        if next_url:
            yield scrapy.Request(url=response.urljoin(next_url), callback=self.parse_oldhouse,
                                 meta={"info": (province, city)})

三 数据存储的 pipelines.py 文件

我们习惯性存到mongodb数据库，想要详细了解mongodb，请你参考：windosw操作mongodb

3.1 安装连接数据库的库

打开cmd
输入： pip install pymongo

3.2 书写 pipelines.py 文件

from itemadapter import ItemAdapter

import pymongo
from .items import New_houseItem, Old_houseItem

class MongodbPipline(object):
    def __init__(self):

        client = pymongo.MongoClient('127.0.0.1', 27017)

        db = client['fangzi']

        self.post_newhouse = db['newhouse3']
        self.post_oldhouse = db['oldhouse3']

    def process_item(self, item, spider):
        if isinstance(item, New_houseItem):

            postItem = dict(item)

            print(postItem)
            self.post_newhouse.insert_one(postItem)
        if isinstance(item, Old_houseItem):

            postItem = dict(item)

            print(postItem)
            self.post_oldhouse.insert_one(postItem)

3.3 setting.py 中配置启用mongodb

添加：
# 数据库配置信息 MONGODB_HOST = '127.0.0.1' MONGODB_PORT = 27017 MONGODB_DBNAME = 'fangzi' MONGODB_DOCNAME1 = 'newhouse3' MONGODB_DOCNAME2 = 'oldhouse3'
打开：

ITEM_PIPELINES = {

    'myspider.pipelines.MongodbPipline': 300,
}

到此，第一步操作，我们的爬虫就完成了！
到爬虫文件中，转到终端
输入： scrapy crawl [爬虫名]
等待运行结束，你的数据库中就有

四 使用下载器中间件

打开 middlewares.py文件

4.1 设置请求头

from scrapy import signals
import random

from itemadapter import is_item, ItemAdapter

class UserAgentDownloadMiddleware(object):

    User_Agents = [
        "Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50",
        "Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50",
        "Mozilla/5.0 (Windows NT 10.0; WOW64; rv:38.0) Gecko/20100101 Firefox/38.0",
        "Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; .NET4.0C; .NET4.0E; .NET CLR 2.0.50727; .NET CLR 3.0.30729; .NET CLR 3.5.30729; InfoPath.3; rv:11.0) like Gecko",
        "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)",
        "Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)",
        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",
        "Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/20100101 Firefox/4.0.1",
        "Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; en) Presto/2.8.131 Version/11.11",
        "Opera/9.80 (Windows NT 6.1; U; en) Presto/2.8.131 Version/11.11",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Maxthon 2.0)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; TencentTraveler 4.0)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; The World)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SE 2.X MetaSr 1.0; SE 2.X MetaSr 1.0; .NET CLR 2.0.50727; SE 2.X MetaSr 1.0)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; 360SE)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Avant Browser)",
        "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)",
        "Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
        "Mozilla/5.0 (iPod; U; CPU iPhone OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
        "Mozilla/5.0 (iPad; U; CPU OS 4_3_3 like Mac OS X; en-us) AppleWebKit/533.17.9 (KHTML, like Gecko) Version/5.0.2 Mobile/8J2 Safari/6533.18.5",
        "Mozilla/5.0 (Linux; U; Android 2.3.7; en-us; Nexus One Build/FRF91) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1",
        "MQQBrowser/26 Mozilla/5.0 (Linux; U; Android 2.3.7; zh-cn; MB200 Build/GRJ22; CyanogenMod-7) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1",
        "Opera/9.80 (Android 2.3.4; Linux; Opera Mobi/build-1107180945; U; en-GB) Presto/2.8.149 Version/11.10",
        "Mozilla/5.0 (Linux; U; Android 3.0; en-us; Xoom Build/HRI39) AppleWebKit/534.13 (KHTML, like Gecko) Version/4.0 Safari/534.13",
        "Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; en) AppleWebKit/534.1+ (KHTML, like Gecko) Version/6.0.0.337 Mobile Safari/534.1+",
        "Mozilla/5.0 (hp-tablet; Linux; hpwOS/3.0.0; U; en-US) AppleWebKit/534.6 (KHTML, like Gecko) wOSBrowser/233.70 Safari/534.6 TouchPad/1.0",
        "Mozilla/5.0 (SymbianOS/9.4; Series60/5.0 NokiaN97-1/20.0.019; Profile/MIDP-2.1 Configuration/CLDC-1.1) AppleWebKit/525 (KHTML, like Gecko) BrowserNG/7.1.18124",
        "Mozilla/5.0 (compatible; MSIE 9.0; Windows Phone OS 7.5; Trident/5.0; IEMobile/9.0; HTC; Titan)",
        "UCWEB7.0.2.37/28/999",
        "NOKIA5700/ UCWEB7.0.2.37/28/999",
        "Openwave/ UCWEB7.0.2.37/28/999",
        "Mozilla/4.0 (compatible; MSIE 6.0; ) Opera/UCWEB7.0.2.37/28/999"
    ]

    def process_request(self, request, spider):
        user_agent = random.choice(self.User_Agents)
        request.headers['User-Agent'] = user_agent

4.2 设置代理IP（如果你有的话^ _ ^）

import base64

class DuXiangIPProxyDownloadMiddleware(object):
    def process_request(self, request, spider):

        proxy = "代理ip地址 : 端口号"

        user_password = "账号 : 密码"
        request.meta["proxy"] = proxy

        b64_user_password = base64.b64encode(user_password.encode('utf-8'))

        request.headers["Proxy-Authorization"] = "Basic " + b64_user_password.decode('utf-8')

不要忘了打开setting

DOWNLOADER_MIDDLEWARES = {

    'myspider.middlewares.UserAgentDownloadMiddleware':543,
}

有没有感觉一切都非常简单！
为了提高爬虫性能，现在我们玩一点好玩的？
将爬虫改写为分布式爬虫

五 改写为scrapy-Redis模拟分布式

5.1 下载安装redis

地址：Redis
详细教程：Redis手册

5.2 打开redis

1. 打开 cmd命令行
2. 进入Redis文件目录
3. 输入： redis-server.exe redis.windows.conf
   出现这个说明成功
   
4. 新开一个cmd，输入：redis-cli
   

; 5.3 配置python中的设置

5.3.1 安装scrapy-redis

输入 pip install scrapy-redis

5.3.2 配置

SCHEDULER = "scrapy_redis.scheduler.Scheduler"

DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"

SCHEDULER_PERSIST = True

REDIS_HOST = '127.0.0.1'
REDIS_PORT = 6379

5.3.3 启动分布式

最最最Z13时刻

多开几个端口，爬虫进入监听，然后redis-cli窗口输入： lpush myspider:start_urls [爬取网址]

; 六 使用scrapyd+scrapydweb可视化管理

是不是感觉命令行操作有点恼火？我也觉得
你想要深入学习？scrapyd手册

6.1 安装scrapyd

输入： pip install scrapyd
输入： pip install scrapyd-client
命令行中输入
scrapyd
在浏览器中输入：http://localhost:6800/，如果出现下面界面则表示启动成功（不要关闭cmd，后面步骤还需要）

; 6.2 配置scrapyd

打开scrapy项目，有个scrapy.cfg文件，按如下进行配置

已经扫描到项目，然后编译 scrapyd-deploy myspider -p myspider

6.3 安装scrapydweb

输入 pip install scrapydweb
会出现一个配置文件

; 6.4 运行scrapydweb（保证scrapyd服务器处于运行状态）

终端： scrapydweb
出现可视化界面

七 数据分析+大屏可视化

因为没有JavaScript大佬教我，所以选择BI工具，安排！
输入： pip install pandas

详细查看：
FineBI手册
Pandas官网

import pymongo
import pandas as pd

client = pymongo.MongoClient('127.0.0.1', 27017)

db = client['fangzi']

old_table = db['oldhouse3']
new_table=db['newhouse3']

data = pd.DataFrame(list(old_table.find()))

筛选自己要的数据，分组存表，导入BI工具，
剩下的就交给你们发挥洛^ _ ^
奉上大图

最后

记得给一个一键三连哦！
未经作者允许不得私自转发
请注明原作者：https://blog.csdn.net/qq_52420866/article/details/121915057

Original: https://blog.csdn.net/qq_52420866/article/details/121915057
Author: _不咬闰土的猹丶
Title: 基于Scrapy+redis+mongodb+scrapyd+scrapydweb+Pandas+BI的可视化操作分布式网络爬虫数据可视化分析