能绕赤道（4万公里）5圈的车是什么样的？Python爬取懂车帝网站数据，并做数据可视化展示二手车概况

import requests
import parsel
import csv

url f https://www.dongchedi.com/usedcar/x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x?sh_city_name %E5%85%A8%E5%9B%BD page 1 
html_data requests.get(url).text

selector parsel.Selector(html_data)
lis selector.css( #__next div:nth-child(2) div.new-main.new div div div.wrap ul li )
for li in lis:
 title li.css( dl dt p::text ).get()
 info_list li.css( dl dd )
 info .join(info_list.css( dd:nth-child(2)::text ).getall()).split( | )
 car_age info[0]
 mileage info[1].replace( 万公里 , )
 city info[2]

if len(info_list) 4:
 dcd_auth info_list.css( dd span::text ).get()
 price info_list.css( dd:nth-child(4)::text ).get()
 original_price info_list.css( dd:nth-child(5)::text ).get()
else:
 dcd_auth 无认证 
 price info_list.css( dd:nth-child(3)::text ).get()
 original_price info_list.css( dd:nth-child(4)::text ).get()
price price.replace( 万 , )
original_price original_price.replace( 新车含税价: , ).replace( 万 , )
print(title, car_age, mileage, city, dcd_auth, price, original_price)

csv_dcd open( dcd.csv , mode a , encoding utf-8 , newline )
csv_write csv.writer(csv_dcd)
csv_write.writerow([ 品牌 , 车龄 , 里程(万公里) , 城市 , 认证 , 售价(万元) , 原价(万元) ])

import pandas as pd
from pyecharts.charts import *
from pyecharts.commons.utils import JsCode
from pyecharts import options as opts

df pd.read_csv( dcd.csv , encoding utf-8 )
df.head()

bar (
 Bar(init_opts opts.InitOpts(height 500px ,width 1000px ,theme dark ))
 .add_xaxis(counts.index.tolist())
 .add_yaxis(
 城市二手车数量 ,
 counts.values.tolist(),
 label_opts opts.LabelOpts(is_show True,position top ),
 itemstyle_opts opts.ItemStyleOpts(
 color JsCode( new echarts.graphic.LinearGradient(
 0, 0, 0, 1,[{offset: 0,color: rgb(255,99,71) }, {offset: 1,color: rgb(32,178,170) }])
 .set_global_opts(
 title_opts opts.TitleOpts(
 title 各个城市二手车数量柱状图 ),
 xaxis_opts opts.AxisOpts(name 书籍名称 ,
 type_ category , 
 axislabel_opts opts.LabelOpts(rotate 90),
 yaxis_opts opts.AxisOpts(
 name 数量 ,
 min_ 0,
 max_ 1400.0,
 splitline_opts opts.SplitLineOpts(is_show True,linestyle_opts opts.LineStyleOpts(type_ dash ))
 tooltip_opts opts.TooltipOpts(trigger axis ,axis_pointer_type cross )
 .set_series_opts(
 markline_opts opts.MarkLineOpts(
 data [
 opts.MarkLineItem(type_ average ,name 均值 ),
 opts.MarkLineItem(type_ max ,name 最大值 ),
 opts.MarkLineItem(type_ min ,name 最小值 ),
bar.render_notebook()

bar (
 Bar(init_opts opts.InitOpts(height 500px ,width 1000px ,theme dark ))
 .add_xaxis(means.index.tolist())
 .add_yaxis(
 城市二手车平均价格 ,
 means.values.tolist(),
 label_opts opts.LabelOpts(is_show True,position top ),
 itemstyle_opts opts.ItemStyleOpts(
 color JsCode( new echarts.graphic.LinearGradient(
 0, 0, 0, 1,[{offset: 0,color: rgb(255,99,71) }, {offset: 1,color: rgb(32,178,170) }])
 .set_global_opts(
 title_opts opts.TitleOpts(
 title 各个城市二手车平均价格柱状图 ),
 xaxis_opts opts.AxisOpts(name 城市名称 ,
 type_ category , 
 axislabel_opts opts.LabelOpts(rotate 90),
 yaxis_opts opts.AxisOpts(
 name 平均价格 ,
 min_ 0,
 max_ 40.0,
 splitline_opts opts.SplitLineOpts(is_show True,linestyle_opts opts.LineStyleOpts(type_ dash ))
 tooltip_opts opts.TooltipOpts(trigger axis ,axis_pointer_type cross )
 .set_series_opts(
 markline_opts opts.MarkLineOpts(
 data [
 opts.MarkLineItem(type_ average ,name 均值 ),
 opts.MarkLineItem(type_ max ,name 最大值 ),
 opts.MarkLineItem(type_ min ,name 最小值 ),
bar.render_notebook()

pie1 (
 Pie(init_opts opts.InitOpts(theme dark ,width 1000px ,height 600px ))
 .add( , datas_pair_1, radius [ 35% , 60% ])
 .set_series_opts(label_opts opts.LabelOpts(formatter {b}:{d}% ))
 .set_global_opts(
 title_opts opts.TitleOpts(
 title 懂车帝二手车nn数量占比区间 , 
 pos_left center , 
 pos_top center ,
 title_textstyle_opts opts.TextStyleOpts(
 color #F0F8FF ,
 font_size 20,
 font_weight bold 
pie1.render_notebook() 

pie1 (
 Pie(init_opts opts.InitOpts(theme dark ,width 1000px ,height 600px ))
 .add( , datas_pair_1, radius [ 35% , 60% ])
 .set_series_opts(label_opts opts.LabelOpts(formatter {b}:{d}% ))
 .set_global_opts(
 title_opts opts.TitleOpts(
 title 懂车帝二手车nn里程占比区间 , 
 pos_left center , 
 pos_top center ,
 title_textstyle_opts opts.TextStyleOpts(
 color #F0F8FF ,
 font_size 20,
 font_weight bold 
pie1.render_notebook()