【大数据基础】厦门租房信息分析展示

https://dblab.xmu.edu.cn/blog/2307/

实验部分

在这里插入图片描述

爬虫程序

首先在工程文件夹下创建名叫rentspider的Python文件。

# -*- coding: utf-8 -*-
import requests
from bs4 import BeautifulSoup
import csv
 
# num表示记录序号
Url_head = "http://fangzi.xmfish.com/web/search_hire.html?h=&hf=&ca=5920"
Url_tail = "&r=&s=&a=&rm=&f=&d=&tp=&l=0&tg=&hw=&o=&ot=0&tst=0&page="
Num = 0
Filename = "rent.csv"
 
 
# 把每一页的记录写入文件中
def write_csv(msg_list):
    out = open(Filename, 'a', newline='')
    csv_write = csv.writer(out,dialect='excel')
    for msg in msg_list:
        csv_write.writerow(msg)
    out.close()
 
 
# 访问每一页
def acc_page_msg(page_url):
    web_data = requests.get(page_url).content.decode('utf8')
    soup = BeautifulSoup(web_data, 'html.parser')
    address_list = []
    area_list = []
    num_address = 0
    num_area = 0
    msg_list = []
 
    # 得到了地址列表，以及区域列表
    for tag in soup.find_all(attrs="list-addr"):
        for em in tag:
            count = 0
            for a in em:
                count += 1
                if count == 1 and a.string != "[":
                    address_list.append(a.string)
                elif count == 2:
                    area_list.append(a.string)
                    num_area += 1
                elif count == 4:
                    if a.string is not None:
                        address_list[num_address] = address_list[num_address] + "-" + a.string
                    else:
                        address_list[num_address] = address_list[num_address] + "-Null"
                    num_address += 1
 
    # 得到了价格列表
    price_list = []
    for tag in soup.find_all(attrs="list-price"):
        price_list.append(tag.b.string)
 
    # 组合成为一个新的tuple——list并加上序号
    for i in range(len(price_list)):
        txt = (address_list[i], area_list[i], price_list[i])
        msg_list.append(txt)
 
    # 写入csv
    write_csv(msg_list)
 
 
# 爬所有的页面
def get_pages_urls():
    urls = []
    # 思明可访问页数134
    for i in range(134):
        urls.append(Url_head + "1" + Url_tail + str(i+1))
    # 湖里可访问页数134
    for i in range(134):
        urls.append(Url_head + "2" + Url_tail + str(i+1))
    # 集美可访问页数27
    for i in range(27):
        urls.append(Url_head + "3" + Url_tail + str(i+1))
    # 同安可访问页数41
    for i in range(41):
        urls.append(Url_head + "4" + Url_tail + str(i+1))
    # 翔安可访问页数76
    for i in range(76):
        urls.append(Url_head + "5" + Url_tail + str(i+1))
    # 海沧可访问页数6
    for i in range(6):
        urls.append(Url_head + "6" + Url_tail + str(i+1))
    return urls
 
 
def run():
    print("开始爬虫")
    out = open(Filename, 'a', newline='')
    csv_write = csv.writer(out, dialect='excel')
    title = ("address", "area", "price")
    csv_write.writerow(title)
    out.close()
    url_list = get_pages_urls()
    for url in url_list:
        try:
            acc_page_msg(url)
        except:
            print("格式出错", url)
    print("结束爬虫")

分析程序

建立一个名为rent_analyse的Python文件：

# -*- coding: utf-8 -*-
from pyspark.sql import SparkSession
from pyspark.sql.types import IntegerType
 
 
def spark_analyse(filename):
    print("开始spark分析")
    # 程序主入口
    spark = SparkSession.builder.master("local").appName("rent_analyse").getOrCreate()
    df = spark.read.csv(filename, header=True)
 
    # max_list存储各个区的最大值，0海沧，1为湖里，2为集美，3为思明，4为翔安，5为同安;同理的mean_list, 以及min_list,approxQuantile中位数
    max_list = [0 for i in range(6)]
    mean_list = [1.2 for i in range(6)]
    min_list = [0 for i in range(6)]
    mid_list = [0 for i in range(6)]
    # 类型转换，十分重要，保证了price列作为int用来比较，否则会用str比较, 同时排除掉一些奇怪的价格，比如写字楼的出租超级贵
    # 或者有人故意标签1元，其实要面议, 还有排除价格标记为面议的
    df = df.filter(df.price != '面议').withColumn("price", df.price.cast(IntegerType()))
    df = df.filter(df.price >= 50).filter(df.price <= 40000)
 
    mean_list[0] = df.filter(df.area == "海沧").agg({
    
    "price": "mean"}).first()['avg(price)']
    mean_list[1] = df.filter(df.area == "湖里").agg({
    
    "price": "mean"}).first()['avg(price)']
    mean_list[2] = df.filter(df.area == "集美").agg({
    
    "price": "mean"}).first()['avg(price)']
    mean_list[3] = df.filter(df.area == "思明").agg({
    
    "price": "mean"}).first()['avg(price)']
    mean_list[4] = df.filter(df.area == "翔安").agg({
    
    "price": "mean"}).first()['avg(price)']
    mean_list[5] = df.filter(df.area == "同安").agg({
    
    "price": "mean"}).first()['avg(price)']
 
    min_list[0] = df.filter(df.area == "海沧").agg({
    
    "price": "min"}).first()['min(price)']
    min_list[1] = df.filter(df.area == "湖里").agg({
    
    "price": "min"}).first()['min(price)']
    min_list[2] = df.filter(df.area == "集美").agg({
    
    "price": "min"}).first()['min(price)']
    min_list[3] = df.filter(df.area == "思明").agg({
    
    "price": "min"}).first()['min(price)']
    min_list[4] = df.filter(df.area == "翔安").agg({
    
    "price": "min"}).first()['min(price)']
    min_list[5] = df.filter(df.area == "同安").agg({
    
    "price": "min"}).first()['min(price)']
 
    max_list[0] = df.filter(df.area == "海沧").agg({
    
    "price": "max"}).first()['max(price)']
    max_list[1] = df.filter(df.area == "湖里").agg({
    
    "price": "max"}).first()['max(price)']
    max_list[2] = df.filter(df.area == "集美").agg({
    
    "price": "max"}).first()['max(price)']
    max_list[3] = df.filter(df.area == "思明").agg({
    
    "price": "max"}).first()['max(price)']
    max_list[4] = df.filter(df.area == "翔安").agg({
    
    "price": "max"}).first()['max(price)']
    max_list[5] = df.filter(df.area == "同安").agg({
    
    "price": "max"}).first()['max(price)']
 
    # 返回值是一个list，所以在最后加一个[0]
    mid_list[0] = df.filter(df.area == "海沧").approxQuantile("price", [0.5], 0.01)[0]
    mid_list[1] = df.filter(df.area == "湖里").approxQuantile("price", [0.5], 0.01)[0]
    mid_list[2] = df.filter(df.area == "集美").approxQuantile("price", [0.5], 0.01)[0]
    mid_list[3] = df.filter(df.area == "思明").approxQuantile("price", [0.5], 0.01)[0]
    mid_list[4] = df.filter(df.area == "翔安").approxQuantile("price", [0.5], 0.01)[0]
    mid_list[5] = df.filter(df.area == "同安").approxQuantile("price", [0.5], 0.01)[0]
 
    all_list = []
    all_list.append(min_list)
    all_list.append(max_list)
    all_list.append(mean_list)
    all_list.append(mid_list)
 
    print("结束spark分析")
 
    return all_list

绘图程序

建立一个名为 draw的python文件

# -*- coding: utf-8 -*-
from pyecharts import Bar
 
 
def draw_bar(all_list):
    print("开始绘图")
    attr = ["海沧", "湖里", "集美", "思明", "翔安", "同安"]
    v0 = all_list[0]
    v1 = all_list[1]
    v2 = all_list[2]
    v3 = all_list[3]
 
    bar = Bar("厦门市租房租金概况")
    bar.add("最小值", attr, v0, is_stack=True)
    bar.add("最大值", attr, v1, is_stack=True)
    bar.add("平均值", attr, v2, is_stack=True)
    bar.add("中位数", attr, v3, is_stack=True)
    bar.render()
    print("结束绘图")

启动程序

建立一个名为run的python文件

# -*- coding: utf-8 -*-
import draw
import rent_analyse
import rentspider
 
 
if __name__ == '__main__':
    print("开始总程序")
    Filename = "rent.csv"
    rentspider.run()
    all_list = rent_analyse.spark_analyse(Filename)
    draw.draw_bar(all_list)
    print("结束总程序")