基于Python+Flask+Echart实现二手车数据分析展示

#汽车品牌
import pandas as pd
df=pd.read_csv("二手车基本信息.csv")
df_title = df.apply(lambda x:x['标题'].split(' ')[0], axis=1)
title_list = df_title.value_counts().index.tolist()[:10]
title_num = df_title.value_counts().tolist()[:10]
from pyecharts import options as opts
from pyecharts.charts import Bar
c = (
    Bar()
    .add_xaxis(title_list)
    .add_yaxis("汽车品牌", title_num)
    .set_colors(["cyan","gray"])
    .set_global_opts(
        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
        title_opts=opts.TitleOpts(title="汽车品牌数量前十"),
    )
    .render("bigdata/cardata/汽车品牌前十.html")
)

#城市前十
import pandas as pd
df=pd.read_csv("二手车基本信息.csv")
city_num = df['城市'].value_counts().tolist()[:10]
city_type = df['城市'].value_counts().index.tolist()[:10]
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c = (
    Pie()
    .add("", [list(z) for z in zip(city_type, city_num)])
    .set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple","black","cyan","gray"])
    .set_global_opts(title_opts=opts.TitleOpts(title="城市前十"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    .render("bigdata/cardata/城市前十.html")
)

#年份分布
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
df=pd.read_csv("二手车基本信息.csv")
df.head()
df['年份'].value_counts().values.tolist()
year_num =df['年份'].value_counts().values.tolist()
year_type=df['年份'].value_counts().index.tolist()
c = (
    Pie()
    .add("", [list(z) for z in zip(year_type, year_num)])
    .set_global_opts(title_opts=opts.TitleOpts(title="年份分布"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    .render("bigdata/cardata/年份分布.html")
)

#车辆价格
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
df=pd.read_csv("二手车基本信息.csv")
def price(x):
    if x<=5.0:
        return '五万元以下'
    elif 5.0<x<=10.0:
        return '5-10万'
    elif 10.0<x<=15.0:
        return '10-15万'
    elif 15.0<x<=20.0:
        return '15-20万'
    elif 20.0<x<=30.0:
        return '20-30万'
    else:
        return '30万以上'
df['价格分级']=df['价格(万元)'].apply(lambda x:price(x))
price_num =df['价格分级'].value_counts().tolist()
price_list=df['价格分级'].value_counts().index.tolist()
# print(price_num)
c = (
    Line()
    .add_xaxis(price_list)
    .add_yaxis("价格", price_num)
    .set_global_opts(title_opts=opts.TitleOpts(title="车辆价格"))
    .render("bigdata/cardata/车辆价格.html")
)

#保修占比
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Liquid
df=pd.read_csv("二手车基本信息.csv")
df["是否保修"].fillna("无保修",inplace=True)
per=df['是否保修'].value_counts()['无保修']/len(df)
c = (
    Liquid()
        .add("lq", [1 - per])
        .set_global_opts(title_opts=opts.TitleOpts(title="保修占比"))
        .render("bigdata/cardata/保修占比.html")
)