本篇内容介绍了“Python爬虫入门案例之实现爬取二手房源数据”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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
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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
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 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='|')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()
“Python爬虫入门案例之实现爬取二手房源数据”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注亿速云网站，小编将为大家输出更多高质量的实用文章！
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