Python爬虫入门案例之实现爬取二手房源数据

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本文重点

• 系统分析网页性质

• 结构化的数据解析

• csv数据保存

环境介绍

• python 3.8

• pycharm 专业版 >>> 激活码

#模块使用

• requests >>> pip install requests

• parsel >>> pip install parsel

• csv

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爬虫代码实现步骤: 发送请求 >>> 获取数据 >>> 解析数据 >>> 保存数据

导入模块

import requests # 数据请求模块 第三方模块 pip install requestsimport parsel # 数据解析模块import reimport csv

发送请求, 对于房源列表页发送请求

url = 'https://bj.lianjia.com/ershoufang/pg1/'# 需要携带上 请求头: 把python代码伪装成浏览器 对于服务器发送请求# User-Agent 浏览器的基本信息headers = {    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}response = requests.get(url=url, headers=headers)

获取数据

print(response.text)

解析数据

selector_1 = parsel.Selector(response.text)# 把获取到response.text 数据内容转成 selector 对象href = selector_1.css('div.leftContent li div.title a::attr(href)').getall()for link in href:    html_data = requests.get(url=link, headers=headers).text    selector = parsel.Selector(html_data)    # css选择器 语法    # try:    title = selector.css('.title h2::text').get() # 标题    area = selector.css('.areaName .info a:nth-child(1)::text').get()  # 区域    community_name = selector.css('.communityName .info::text').get()  # 小区    room = selector.css('.room .mainInfo::text').get()  # 户型    room_type = selector.css('.type .mainInfo::text').get()  # 朝向    height = selector.css('.room .subInfo::text').get().split('/')[-1]  # 楼层    # 中楼层/共5层 split('/') 进行字符串分割  ['中楼层', '共5层'] [-1]    # ['中楼层', '共5层'][-1] 列表索引位置取值 取列表中最后一个元素  共5层    # re.findall('共(\d+)层', 共5层) >>>  [5][0] >>> 5    height = re.findall('共(\d+)层', height)[0]    sub_info = selector.css('.type .subInfo::text').get().split('/')[-1]  # 装修    Elevator = selector.css('.content li:nth-child(12)::text').get()  # 电梯    # if Elevator == '暂无数据电梯' or Elevator == None:    #     Elevator = '无电梯'    house_area = selector.css('.content li:nth-child(3)::text').get().replace('㎡', '')  # 面积    price = selector.css('.price .total::text').get()  # 价格(万元)    date = selector.css('.area .subInfo::text').get().replace('年建', '')  # 年份    dit = {        '标题': title,        '市区': area,        '小区': community_name,        '户型': room,        '朝向': room_type,        '楼层': height,        '装修情况': sub_info,        '电梯': Elevator,        '面积(㎡)': house_area,        '价格(万元)': price,        '年份': date,    }    csv_writer.writerow(dit)    print(title, area, community_name, room, room_type, height, sub_info, Elevator, house_area, price, date,          sep='|')

保存数据

f = open('二手房数据.csv', mode='a', encoding='utf-8', newline='')csv_writer = csv.DictWriter(f, fieldnames=[    '标题',    '市区',    '小区',    '户型',    '朝向',    '楼层',    '装修情况',    '电梯',    '面积(㎡)',    '价格(万元)',    '年份',])csv_writer.writeheader()

数据可视化

导入所需模块

import pandas as pdfrom pyecharts.charts import Mapfrom pyecharts.charts import Barfrom pyecharts.charts import Linefrom pyecharts.charts import Gridfrom pyecharts.charts import Piefrom pyecharts.charts import Scatterfrom pyecharts import options as opts

读取数据

df = pd.read_csv('链家.csv', encoding = 'utf-8')df.head()

各城区二手房数量北京市地图

new = [x + '区' for x in region]m = (        Map()        .add('', [list(z) for z in zip(new, count)], '北京')        .set_global_opts(            title_opts=opts.TitleOpts(title='北京市二手房各区分布'),            visualmap_opts=opts.VisualMapOpts(max_=3000),        )    )m.render_notebook()

各城区二手房数量-平均价格柱状图

df_price.values.tolist()price = [round(x,2) for x in df_price.values.tolist()]bar = (    Bar()    .add_xaxis(region)    .add_yaxis('数量', count,              label_opts=opts.LabelOpts(is_show=True))    .extend_axis(        yaxis=opts.AxisOpts(            name="价格（万元）",            type_="value",            min_=200,            max_=900,            interval=100,            axislabel_opts=opts.LabelOpts(formatter="{value}"),        )    )    .set_global_opts(        title_opts=opts.TitleOpts(title='各城区二手房数量-平均价格柱状图'),        tooltip_opts=opts.TooltipOpts(            is_show=True, trigger="axis", axis_pointer_type="cross"        ),        xaxis_opts=opts.AxisOpts(            type_="category",            axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),        ),        yaxis_opts=opts.AxisOpts(name='数量',            axistick_opts=opts.AxisTickOpts(is_show=True),            splitline_opts=opts.SplitLineOpts(is_show=False),)    ))line2 = (    Line()    .add_xaxis(xaxis_data=region)    .add_yaxis(                series_name="价格",        yaxis_index=1,        y_axis=price,        label_opts=opts.LabelOpts(is_show=True),        z=10        ))bar.overlap(line2)grid = Grid()grid.add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True)grid.render_notebook()

area0 = top_price['小区'].values.tolist()count = top_price['价格(万元)'].values.tolist()bar = (    Bar()    .add_xaxis(area0)    .add_yaxis('数量', count,category_gap = '50%')    .set_global_opts(        yaxis_opts=opts.AxisOpts(name='价格(万元)'),        xaxis_opts=opts.AxisOpts(name='数量'),    ))bar.render_notebook()

散点图

s = (    Scatter()    .add_xaxis(df['面积(㎡)'].values.tolist())    .add_yaxis('',df['价格(万元)'].values.tolist())    .set_global_opts(xaxis_opts=opts.AxisOpts(type_='value')))s.render_notebook()

房屋朝向占比

directions = df_direction.index.tolist()count = df_direction.values.tolist()c1 = (    Pie(init_opts=opts.InitOpts(            width='800px', height='600px',            )       )        .add(        '',        [list(z) for z in zip(directions, count)],        radius=['20%', '60%'],        center=['40%', '50%'],#         rosetype="radius",        label_opts=opts.LabelOpts(is_show=True),        )            .set_global_opts(title_opts=opts.TitleOpts(title='房屋朝向占比',pos_left='33%',pos_top="5%"),                        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%",pos_top="25%",orient="vertical")                        )        .set_series_opts(label_opts=opts.LabelOpts(formatter='{b}:{c} ({d}%)'),position="outside")    )c1.render_notebook()

装修情况/有无电梯玫瑰图（组合图）

fitment = df_fitment.index.tolist()count1 = df_fitment.values.tolist()directions = df_direction.index.tolist()count2 = df_direction.values.tolist()bar = (    Bar()    .add_xaxis(fitment)    .add_yaxis('', count1, category_gap = '50%')    .reversal_axis()    .set_series_opts(label_opts=opts.LabelOpts(position='right'))        .set_global_opts(        xaxis_opts=opts.AxisOpts(name='数量'),        title_opts=opts.TitleOpts(title='装修情况/有无电梯玫瑰图（组合图）',pos_left='33%',pos_top="5%"),        legend_opts=opts.LegendOpts(type_="scroll", pos_left="90%",pos_top="58%",orient="vertical")    ))c2 = (    Pie(init_opts=opts.InitOpts(            width='800px', height='600px',            )       )        .add(        '',        [list(z) for z in zip(directions, count2)],        radius=['10%', '30%'],        center=['75%', '65%'],        rosetype="radius",        label_opts=opts.LabelOpts(is_show=True),        )            .set_global_opts(title_opts=opts.TitleOpts(title='有/无电梯',pos_left='33%',pos_top="5%"),                        legend_opts=opts.LegendOpts(type_="scroll", pos_left="90%",pos_top="15%",orient="vertical")                        )        .set_series_opts(label_opts=opts.LabelOpts(formatter='{b}:{c} \n ({d}%)'),position="outside")    )bar.overlap(c2)bar.render_notebook()

二手房楼层分布柱状缩放图

floor = df_floor.index.tolist()count = df_floor.values.tolist()bar = (    Bar()    .add_xaxis(floor)    .add_yaxis('数量', count)    .set_global_opts(        title_opts=opts.TitleOpts(title='二手房楼层分布柱状缩放图'),        yaxis_opts=opts.AxisOpts(name='数量'),        xaxis_opts=opts.AxisOpts(name='楼层'),        datazoom_opts=opts.DataZoomOpts(type_='slider')    ))bar.render_notebook()

房屋面积分布纵向柱状图

area = df_area.index.tolist()count = df_area.values.tolist()bar = (    Bar()    .add_xaxis(area)    .add_yaxis('数量', count)    .reversal_axis()    .set_series_opts(label_opts=opts.LabelOpts(position="right"))    .set_global_opts(        title_opts=opts.TitleOpts(title='房屋面积分布纵向柱状图'),        yaxis_opts=opts.AxisOpts(name='面积(㎡)'),        xaxis_opts=opts.AxisOpts(name='数量'),    ))bar.render_notebook()

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