Python爬取二手房源数据，可视化分析二手房市场行情数据
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
- python 3.8
- pycharm 专业版 >>> 激活码
#模块使用
- requests >>> pip install requests
- parsel >>> pip install parsel
- csv
import requests # 数据请求模块 第三方模块 pip install requests import parsel # 数据解析模块 import re import csv
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)
print(response.text)
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 h1::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='|')
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()
import pandas as pd from pyecharts.charts import Map from pyecharts.charts import Bar from pyecharts.charts import Line from pyecharts.charts import Grid from pyecharts.charts import Pie from pyecharts.charts import Scatter from pyecharts import options as opts
df = pd.read_csv('链家.csv', encoding = 'utf-8') df.head()
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()
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()
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()
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()
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()
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()
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()
