【python数据分析】对乐高、奥特曼、高达进行分析，看看哪个卖的最好

importpandasaspdimportpyecharts.optionsasoptsfrompyecharts.chartsimport*frompyecharts.globalsimportThemeType#设定主题frompyecharts.commons.utilsimportJsCode

df1=pd.read_csv(r'京东-乐高.csv', engine='python', encoding='utf-8-sig')
df2=pd.read_csv(r'6K高达.csv', engine='python', encoding='utf-8-sig')
df3=pd.read_csv(r'6K奥特曼.csv', engine='python', encoding='utf-8-sig')
df1.head(1)

df2.head(1)

df3.head(1)

df_all=pd.concat([df1,df2,df3])
df_all.info()

df_all.drop_duplicates(inplace=True)

df_all=df_all.drop(['商品SKU','商品链接','封面图链接','评论链接','店铺链接','页码','当前时间','页面网址'],axis=1)
df_all.head(1)

df_all=df_all[df_all['是否广告'] =='否']

df_all=df_all.reset_index(drop=True)
df_all.info()

shopname=df_all.groupby('商家店名')['商品名称'].count().sort_values(ascending=False).head(20)
shopname

bar1= (
Bar(init_opts=opts.InitOpts(theme='dark', width='1000px',height='500px'))
    .add_xaxis(shopname.index.tolist())
    .add_yaxis("",shopname.values.tolist())
    .set_series_opts(
label_opts=opts.LabelOpts(
is_show=True, 
position='insideRight',
font_style='italic'            ),
源码、解答、教程+VX：qian97378itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""new echarts.graphic.LinearGradient(1, 0, 0, 0,                  [{offset: 0,color: 'rgb(255,99,71)'}, {offset: 1,color: 'rgb(32,178,170)'}])"""            )
        )
    )
    .set_global_opts(
title_opts=opts.TitleOpts(title="商家上线的商品数目Top20"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=45)),
legend_opts=opts.LegendOpts(is_show=True))
    .reversal_axis()
)
bar1.render_notebook()

deftranform_price(x):
ifx<=100.0:
return'0~100元'elifx<=500.0:
return'101~500元'elifx<=2000.0:
return'501~2000元'elifx<=5000.0:
return'2001~5000元'elifx<=10000.0:
return'5001~10000元'else:
return'10000以上'

df_all['分级'] =df_all["价格"].apply(lambdax:tranform_price(x))
price_score=df_all['分级'].value_counts()
print(price_score)

datas_pair= [(i, int(j)) fori, jinzip(price_score.index, price_score.values)]
print(datas_pair)

pie1= (
Pie(init_opts=opts.InitOpts(theme='dark',width='1000px',height='600px'))
    .add('', datas_pair, radius=['35%', '60%'])
    .set_global_opts(
title_opts=opts.TitleOpts(title='不同价格区间的销售额整体表现'), 
legend_opts=opts.LegendOpts(orient='vertical', pos_top='15%', pos_left='2%')
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%"))
    .set_global_opts(
title_opts=opts.TitleOpts(
# 源码、解答、教程+VX：qian97378title="乐高、奥特曼、高达\n\n价格区间", 
pos_left='center', 
pos_top='center',
title_textstyle_opts=opts.TextStyleOpts(
color='#F0F8FF', 
font_size=20, 
font_weight='bold'            ),
        )
    )
    .set_colors(['#EF9050', '#3B7BA9', '#6FB27C', '#FFAF34', '#D8BFD8', '#00BFFF', '#7FFFAA'])
)
pie1.render_notebook()

#单价最高的商品price_top=df_all.groupby('商品名称')['价格'].sum().sort_values(ascending=False).head(20)
price_top

bar=(
Bar(init_opts=opts.InitOpts(height='500px',width='1000px',theme='dark'))
    .add_xaxis(price_top.index.tolist())
    .add_yaxis(
'单价最高的商品',
price_top.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='单价最高的商品详细柱状图'),
xaxis_opts=opts.AxisOpts(name='玩具名称',
type_='category',                                           
axislabel_opts=opts.LabelOpts(rotate=90),
        ),
yaxis_opts=opts.AxisOpts(
name='单价/元',
min_=0,
max_=39980.0,
源码、解答、教程+VX：qian97378splitline_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()