前言

数据采集的步骤是固定:

1. 发送请求, 模拟浏览器对于url地址发送请求
2. 获取数据, 获取网页数据内容 --> 请求那个链接地址, 返回服务器响应数据
3. 解析数据, 提取我们需要的数据内容
4. 保存数据, 保存本地文件

所需模块

win + R 输入cmd 输入安装命令 pip install 模块名 (如果你觉得安装速度比较慢, 你可以切换国内镜像源)

# 数据请求模块 第三方模块 需要安装 pip install requests
import requests
# 数据解析模块 第三方模块 需要安装 pip install parsel
import parsel
# 导入csv模块 内置模块 不需要安装
import csv  # 固定模板
# 导入pandas模块
import pandas as pd

二手房源数据获取

请求数据

# 模拟浏览器
headers = {# 用户代理 表示浏览器基本身份信息'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36'
}
# 请求链接
url = 'https://cs.lianjia.com/ershoufang/'
# 发送请求
response = requests.get(url=url, headers=headers)
# 输出内容 <Response [200]> 响应对象 表示请求成功
print(response)

解析数据

我们这次选用css选择器: 根据标签属性提取数据内容

• 获取所有房源所在li标签

selector = parsel.Selector(response.text)  # 选择器对象
# 获取所有房源所在li标签
lis = selector.css('.sellListContent li .info')

• for循环遍历

for li in lis:title = li.css('.title a::text').get()  # 标题area_info = li.css('.positionInfo a::text').getall()  # 区域信息area_1 = area_info[0]  # 小区area_2 = area_info[1]  # 区域totalPrice = li.css('.totalPrice span::text').get()  # 总价unitPrice = li.css('.unitPrice span::text').get().replace('元/平', '')  # 单价houseInfo = li.css('.houseInfo::text').get().split(' | ')  # 房源信息HouseType = houseInfo[0]  # 户型HouseArea = houseInfo[1].replace('平米', '')  # 面积HouseFace = houseInfo[2]  # 朝向HouseInfo_1 = houseInfo[3]  # 装修fool = houseInfo[4]  # 楼层HouseInfo_2 = houseInfo[-1]  # 建筑结构href = li.css('.title a::attr(href)').get()  # 详情页dit = {'标题': title,'小区': area_1,'区域': area_2,'总价': totalPrice,'单价': unitPrice,'户型': HouseType,'面积': HouseArea,'朝向': HouseFace,'装修': HouseInfo_1,'楼层': fool,'年份': date,'建筑结构': HouseInfo_2,'详情页': href,}print(dit)

保存数据

f = open('二手房.csv', mode='w', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=['标题','小区','区域','总价','单价','户型','面积','朝向','装修','楼层','年份','建筑结构','详情页',
])
csv_writer.writeheader()

接下来就是数据可视化

二手房源户型分布

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Fakerc = (Pie().add("",[list(z)for z in zip(house_type, house_num)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="二手房源户型分布"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.load_javascript()

二手房源朝向分布

face_type = df['朝向'].value_counts().index.to_list()
face_num = df['朝向'].value_counts().to_list()
c = (Pie().add("",[list(z)for z in zip(face_type, face_num)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="二手房源朝向分布"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

二手房源装修分布

face_type = df['装修'].value_counts().index.to_list()
face_num = df['装修'].value_counts().to_list()
c = (Pie().add("",[list(z)for z in zip(face_type, face_num)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="二手房源装修分布"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

二手房源年份分布

face_type = df['年份'].value_counts().index.to_list()
face_num = df['年份'].value_counts().to_list()
c = (Pie().add("",[list(z)for z in zip(face_type, face_num)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="二手房源年份分布"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

二手房源建筑结构分布

face_type = df['建筑结构'].value_counts().index.to_list()
face_num = df['建筑结构'].value_counts().to_list()
c = (Pie().add("",[list(z)for z in zip(face_type, face_num)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="二手房源建筑结构分布"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
#     .render("pie_scroll_legend.html")
)
c.render_notebook()

各大区域房价平均价

avg_salary = df.groupby('区域')['总价'].mean()
CityType = avg_salary.index.tolist()
CityNum = [int(a) for a in avg_salary.values.tolist()]
from pyecharts.charts import Bar
# 创建柱状图实例
c = (Bar().add_xaxis(CityType).add_yaxis("", CityNum).set_global_opts(title_opts=opts.TitleOpts(title="各大区域房价平均价"),visualmap_opts=opts.VisualMapOpts(dimension=1,pos_right="5%",max_=30,is_inverse=True,),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45))  # 设置X轴标签旋转角度为45度).set_series_opts(label_opts=opts.LabelOpts(is_show=False),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="min", name="最小值"),opts.MarkLineItem(type_="max", name="最大值"),opts.MarkLineItem(type_="average", name="平均值"),]),)
)c.render_notebook()

各大区域房价单价平均价格

import pandas as pd
from pyecharts.charts import Bar
import pyecharts.options as opts# 清理数据并将'单价'列转换为整数类型
df['单价'] = df['单价'].str.replace(',', '').astype(int)# 计算平均价
avg_salary = df.groupby('区域')['单价'].mean()# 获取城市类型和城市平均价格
CityType = avg_salary.index.tolist()
CityNum = [int(a) for a in avg_salary.values.tolist()]# 创建柱状图实例
c = (Bar().add_xaxis(CityType).add_yaxis("", CityNum).set_global_opts(title_opts=opts.TitleOpts(title="各大区域房价单价平均价格"),visualmap_opts=opts.VisualMapOpts(dimension=1,pos_right="5%",max_=30,is_inverse=True,),xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45))  # 设置X轴标签旋转角度为45度).set_series_opts(label_opts=opts.LabelOpts(is_show=False),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="min", name="最小值"),opts.MarkLineItem(type_="max", name="最大值"),opts.MarkLineItem(type_="average", name="平均值"),]),)
)# 在Notebook中显示柱状图
c.render_notebook()

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