思路

#### 数据说明
kaggle描述：这是一个跨国数据集，其中包含在 2010 年 1 月 12 日到 2011 年 9 月 12 日之间发生的英国某电商在线零售的交易数据。
数据量很庞大，在分析思路上可以使用机器学习K-Means 等算法，根据客户在市场上的购买行为来细分客户。

代码

读取数据

#读取数据
data = pd.read_excel('./Online Retail.xlsx',encoding='gb18030')
#退货数据
data_return = data[data.InvoiceNo.str.contains('C',na=False)==True]
#查看缺失值，用户ID缺失较多，做用户分析时应该进行剔除
data.isnull().sum()

数据处理

# 删除缺失的用户，对于商品名称，暂时保留
data.dropna(subset=['CustomerID'], inplace=True)
# 增加营收
data['Amount'] = data['Quantity']*data['UnitPrice']
# 国家名称统一化
data.replace({'EIRE':'Ireland','USA':'United States','RSA':'South Africa','Czech Republic':'Czech','Channel Islands':'United Kingdom'},inplace=True)
# 增加订单状态
data['Transaction_status'] = data['InvoiceNo'].map(lambda x:'0' if str(x).startswith('C') else '1')
#增加月份
data['month'] = data['InvoiceDate'].apply(lambda x:format(x,'%Y-%m'))
#删除退货的数据
data = data[~data.InvoiceNo.str.contains('C',na=False)==True]

月度主要指标分析

# 算月份的销售额
df_month =data.groupby(['month'])['Amount'].agg({'sum'})
df_month_c = data.groupby(['month'])['CustomerID'].agg({'nunique'})
df_month_num =data.groupby(['month'])['Amount'].agg({'count'})
df_month_Quantity = data.groupby(['month'])['Quantity'].agg({'sum'})
df_all = pd.merge(df_month,df_month_num,left_index=True,right_index=True)
df_all = pd.merge(df_all,df_month_Quantity,left_index=True,right_index=True)
df_all = pd.merge(df_all,df_month_c,left_index=True,right_index=True)
df_all.columns = ['销量','订单数','商品件数','消费用户数']
df_all['月客单价'] = df_all['销量']/df_all['订单数']
df_all['月商品单价'] = df_all['销量']/df_all['商品件数']
df_all['月用户金额'] = df_all['销量']/df_all['消费用户数']
df_all['月用户消费频次'] = df_all['订单数']/df_all['消费用户数']

国家销售额、订单量

# 算国家的销售额
df_Country_s = data.groupby('Country')['Amount'].agg({'sum'}).sort_values(by='sum',ascending=False)
df_Country_c = data.groupby('Country')['Amount'].agg({'count'}).sort_values(by='count',ascending=False)
## 去除未确定的数据（'Unspecified'、'European Community'）
df_c = data['Country'].value_counts().reset_index()
df_c.drop([20,28],inplace=True)

可视化（月度指标可视化）

# 创建柱状图通用函数
def bar_chart(desc, title_pos, data):chart = Bar()chart.add_xaxis([i[0][:10]+'...' if len(i[0])>10 else i[0] for i in data])chart.add_yaxis('',[int(round(i[1], 0)) for i in data])chart.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True,name='',axislabel_opts={'rotate': '-25' if len(data) >= 5 else '0', 'interval': '0'},splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed'))),yaxis_opts=opts.AxisOpts(is_scale=True,name='',type_='value',splitline_opts=opts.SplitLineOpts(is_show=True,linestyle_opts=opts.LineStyleOpts(type_='dashed'))),title_opts=opts.TitleOpts(title=desc,pos_left=title_pos[0],pos_top=title_pos[1],title_textstyle_opts=opts.TextStyleOpts(color='#ea513f',font_family='cursive',font_size=19)),)return chart

from pyecharts.charts import Grid
grid = Grid(init_opts=opts.InitOpts(theme='light',width='1000px',height='1400px')
)
grid.add(bar_chart('月度销售额', ['5%', '2%'], [[i,df_month.loc[i,'sum']]for i in df_month.index]),grid_opts=opts.GridOpts(pos_top='5%',  # 指定Grid中子图的位置pos_bottom='83%',pos_left='10%',pos_right='60%')
)
grid.add(bar_chart('月消费用户数', ['55%', '2%'],[[i,df_month_c.loc[i,'nunique']]for i in df_month_c.index]),grid_opts=opts.GridOpts(pos_top='5%',pos_bottom='83%',pos_left='60%',pos_right='10%')
)
grid.add(bar_chart('国家销售额TOP10', ['5%', '22%'],[[i,df_Country_s.loc[i,'sum']]for i in df_Country_s.head(10).index]),grid_opts=opts.GridOpts(pos_top='25%',pos_bottom='63%',pos_left='10%',pos_right='60%')
)
grid.add(bar_chart('国家订单量TOP10', ['55%', '22%'], [[i,df_Country_c.loc[i,'count']]for i in df_Country_c.head(10).index]),grid_opts=opts.GridOpts(pos_top='25%',pos_bottom='63%',pos_left='60%',pos_right='10%')
)
grid.add(bar_chart('商品购买TOP10', ['5%', '42%'], [[i,df_Description_c.loc[i,'sum']]for i in df_Description_c.index]),grid_opts=opts.GridOpts(pos_top='45%',pos_bottom='43%',pos_left='10%',pos_right='60%')
)
grid.add(bar_chart('商品退货TOP10', ['55%', '42%'],[[i,df_Description_r.loc[i,'sum']]for i in df_Description_r.index]),grid_opts=opts.GridOpts(pos_top='45%',pos_bottom='43%',pos_left='60%',pos_right='10%')
)
grid.add(bar_chart('客单价', ['5%', '62%'], [[i,df_all.loc[i, '月客单价']]for i in df_all.index]),grid_opts=opts.GridOpts(pos_top='65%',pos_bottom='23%',pos_left='10%',pos_right='60%')
)
grid.add(bar_chart_1('商品单价', ['55%', '62%'],[[i,df_all.loc[i,'月商品单价']]for i in df_all.index]),grid_opts=opts.GridOpts(pos_top='65%',pos_bottom='23%',pos_left='60%',pos_right='10%')
)
grid.add(bar_chart('用户消费金额', ['5%', '82%'], [[i,df_all.loc[i, '月用户金额']]for i in df_all.index]),grid_opts=opts.GridOpts(pos_top='85%',pos_bottom='3%',pos_left='10%',pos_right='60%')
)
grid.add(bar_chart('用户消费频次', ['55%', '82%'],[[i,df_all.loc[i, '月用户消费频次']]for i in df_all.index]),grid_opts=opts.GridOpts(pos_top='85%',pos_bottom='3%',pos_left='60%',pos_right='10%')
)
grid.render_notebook()

地域情况可视化

attr = df_c['index'].tolist()
values = df_c['Country'].tolist()
map_= (Map(init_opts=opts.InitOpts(width='980px',height='400px')).add("订单数量", [list(z) for z in zip(attr, values)], "world",is_map_symbol_show=False,is_roam=False,zoom='0.9').set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title='全球订单分布状态',subtitle=' 2010-12-1 ~ 2011-12-9',pos_left='center',pos_top='2%'),visualmap_opts=opts.VisualMapOpts(max_=10000,is_show=False),legend_opts=opts.LegendOpts(is_show=False),)
)

label = df_Description_num.head(10).index.tolist()
value = df_Description_num.head(10)['sum'].values.tolist()
bar = (Bar(init_opts=opts.InitOpts(width='500px',height='300px',theme='light')).add_xaxis(label[::-1]).add_yaxis('',value[::-1],itemstyle_opts={'barBorderRadius': [10, 10, 10, 10],},).set_series_opts(label_opts = opts.LabelOpts(position='insideLeft',formatter='{b}：{c}份')).set_global_opts(title_opts = opts.TitleOpts(title='热门商品Top 10',subtitle = '2010-12-1 ~ 2011-12-9',pos_right = '18%'),legend_opts = opts.LegendOpts(pos_left="25%"),xaxis_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),))

label = df_Description_r.head(10).index.tolist()
value = df_Description_r.head(10)['sum'].values.tolist()
bar2 = (Bar(init_opts=opts.InitOpts(width='500px',height='300px',theme='light')).add_xaxis(label[::-1]).add_yaxis('',value[::-1],itemstyle_opts={'barBorderRadius': [10, 10, 10, 10],},).set_series_opts(label_opts = opts.LabelOpts(position='insideLeft',formatter='{b}：{c}份')).set_global_opts(title_opts = opts.TitleOpts(title='退货商品Top 10',subtitle = '2010-12-1 ~ 2011-12-9',pos_right = '18%'),legend_opts = opts.LegendOpts(is_show=False),xaxis_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),))

line_style = {'normal': {'width': 4,'shadowColor': 'rgba(155, 18, 184, .3)','shadowBlur': 10,'shadowOffsetY': 10,'shadowOffsetX': 10,'curve': 0.5}
}
line = (Line(init_opts=opts.InitOpts(height='300px',width='500px')).add_xaxis(df_month_num.index.tolist()).add_yaxis('',df_month_num['count'].tolist(),is_symbol_show=False,is_smooth=True,linestyle_opts=line_style).set_global_opts(title_opts=opts.TitleOpts(title='订单量逐月变化趋势',subtitle='2010-12-1 ~ 2011-12-9',pos_left='24%',pos_top='2%'),xaxis_opts=opts.AxisOpts(axislabel_opts={'rotate':'90'},),yaxis_opts=opts.AxisOpts(min_=10000,max_=70000,axisline_opts=opts.AxisLineOpts(is_show=False),splitline_opts=opts.SplitLineOpts(is_show=True)),tooltip_opts=opts.TooltipOpts(is_show = True,trigger = 'axis',trigger_on = 'mousemove|click',axis_pointer_type = 'shadow'))
)

##可视化--订单交易状态
label = ['交易成功','交易取消']
value = [len(data),len(data_return)]
pie=(Pie(init_opts=opts.InitOpts(theme='light',height='350px')).add("",[list(z) for z in zip(label, value)],radius=["40%", "55%"],center=['32%','52%']).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%")).set_global_opts(title_opts=opts.TitleOpts(title='订单交易情况',subtitle=' 2010-12-1 ~ 2011-12-9',pos_left='24%'),legend_opts=opts.LegendOpts(is_show=False))
)

融合

grid = Grid(init_opts=opts.InitOpts(height='300px',theme='light'))
grid.add(bar.reversal_axis(), grid_opts=opts.GridOpts(pos_left="60%"))
grid.add(pie, grid_opts=opts.GridOpts(pos_left="60%"))grid1 = Grid(init_opts=opts.InitOpts(height='300px',theme='light'))
grid1.add(line, grid_opts=opts.GridOpts(pos_right="50%",pos_left='20%'))
grid1.add(bar2.reversal_axis(), grid_opts=opts.GridOpts(pos_left="60%"))page = Page()
page.add(map_)
page.add(grid)
page.add(grid1)
page.render_notebook()

RMF

t = '2011-12-09 23:59:59'
t = pd.to_datetime(t)
df_c = data.groupby(['CustomerID'])['InvoiceNo','Amount','InvoiceDate'].agg({'InvoiceNo':'count','Amount':'sum','InvoiceDate':"max"})
df_c['interval_time'] = (t-df_c.InvoiceDate).dt.days
df_c['interval_time'].max(),df_c['InvoiceDate'].min(),data['InvoiceDate'].min()
df_rmf = df_c[['InvoiceNo','Amount','interval_time']]
df_rmf.columns = ['频次','消费金额','消费间隔']
df_rmf.head()

rmd = df_rmf['消费间隔'].mean()
mmd= df_rmf['消费金额'].mean()
fmd = df_rmf['频次'].mean()
def customer_type(frame): customer_type = []for i in range(len(frame)):if frame.iloc[i,1]<=rmd and frame.iloc[i,2]>=fmd and frame.iloc[i,0]>=mmd:customer_type.append('重要价值用户')elif  frame.iloc[i,1]>rmd and frame.iloc[i,2]>=fmd and frame.iloc[i,0]>=mmd:customer_type.append('重要唤回用户')elif  frame.iloc[i,1]<=rmd and frame.iloc[i,2]<fmd and frame.iloc[i,0]>=mmd:customer_type.append('重要深耕用户')elif  frame.iloc[i,1]>rmd and frame.iloc[i,2]<fmd and frame.iloc[i,0]>=mmd:customer_type.append('重要挽留用户')elif  frame.iloc[i,1]<=rmd and frame.iloc[i,2]>=fmd and frame.iloc[i,0]<mmd:customer_type.append('潜力用户')elif  frame.iloc[i,1]>rmd and frame.iloc[i,2]>=fmd and frame.iloc[i,0]<mmd:customer_type.append('一般维持用户')elif  frame.iloc[i,1]<=rmd and frame.iloc[i,2]<fmd and frame.iloc[i,0]<mmd:customer_type.append('新用户')elif frame.iloc[i,1]>rmd and frame.iloc[i,2]<fmd and frame.iloc[i,0]<mmd:customer_type.append('流失用户')frame['classification'] = customer_type
customer_type(df_rmf)

占比

c = (Pie().add("", [i for i in zip(df5.index,df5['count'])],rosetype="radius",).set_global_opts(title_opts=opts.TitleOpts(title="用户分层",pos_left='center'),legend_opts=opts.LegendOpts(pos_top='5%'))
)
c.render_notebook()

kmeans

## 数据标准化
model_scaler = MinMaxScaler()
data_scaled = model_scaler.fit_transform(df_rmf[['频次','消费金额','消费间隔']])
K = range(1, 10)
meandistortions = []
for k in K:kmeans = KMeans(n_clusters=k)kmeans.fit(data_scaled)meandistortions.append(sum(np.min(cdist(data_scaled, kmeans.cluster_centers_, 'euclidean'), axis=1))/data_scaled.shape[0])
plt.plot(K, meandistortions, marker='o')
plt.xlabel('K')
plt.ylabel('Average distortion degree')
plt.title('Use the Elbow Method to select the best K value')
plt.show()

Kmeans = KMeans(n_clusters=4,max_iter=50)
Kmeans.fit(data_scaled)
cluster_labels_k = Kmeans.labels_  #输出归类结果
# 把结果加入到df_rmf里面
df_rmf = df_rmf.reset_index()
cluster_labels = pd.DataFrame(cluster_labels_k, columns=['clusters'])
res = pd.concat((df_rmf, cluster_labels), axis=1)

显著性对比

c = (Radar(init_opts=opts.InitOpts()).add_schema(schema=[opts.RadarIndicatorItem(name="F",max_=1.2),opts.RadarIndicatorItem(name="M", max_=1.2),opts.RadarIndicatorItem(name="R", max_=1.2),],splitarea_opt=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),textstyle_opts=opts.TextStyleOpts(color="#000000"),).add(series_name="第1类客户",data=[num_sets_max_min[0]],areastyle_opts=opts.AreaStyleOpts(color="#FF0000",opacity=0.2), # 区域面积，透明度).add(series_name="第2类客户",data=[num_sets_max_min[1]],areastyle_opts=opts.AreaStyleOpts(color="#00BFFF",opacity=0.2), # 区域面积，透明度).add(series_name="第3类客户",data=[num_sets_max_min[2]],areastyle_opts=opts.AreaStyleOpts(color="#00FF7F",opacity=0.2), # 区域面积，透明度).add(series_name="第4类客户",data=[num_sets_max_min[3]],areastyle_opts=opts.AreaStyleOpts(color="#007F7F",opacity=0.2), # 区域面积，透明度).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).set_global_opts(title_opts=opts.TitleOpts(title="各聚类类别显著特征对比"),)
)
c.render_notebook()

3D散点图

import asyncio
from aiohttp import TCPConnector, ClientSessionimport pyecharts.options as opts
from pyecharts.charts import Scatter3D"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.apache.org/examples/editor.html?c=scatter3d&gl=1&theme=dark目前无法实现的功能:1、暂时无法对 Grid3D 设置 轴线和轴坐标的 style (非白色背景下有问题)
"""async def get_json_data(url: str) -> dict:async with ClientSession(connector=TCPConnector(ssl=False)) as session:async with session.get(url=url) as response:return await response.json()symbol_list = ['circle', 'rect', 'roundRect', 'triangle']# 配置 config
config_xAxis3D = "频次"
config_yAxis3D = "消费金额"
config_zAxis3D = "消费间隔"
config_color = "clusters"
# # config_symbolSize = "vitaminc"
res2 = res1.to_dict(orient='records')
# # 构造数据
data = [[item[config_xAxis3D],item[config_yAxis3D],item[config_zAxis3D],item[config_color],
#         item['index'],]for item in res2
]c = (Scatter3D()  # bg_color="black".add(series_name="",data=data,xaxis3d_opts=opts.Axis3DOpts(name=config_xAxis3D,type_="value",# textstyle_opts=opts.TextStyleOpts(color="#fff"),),yaxis3d_opts=opts.Axis3DOpts(name=config_yAxis3D,type_="value",# textstyle_opts=opts.TextStyleOpts(color="#fff"),),zaxis3d_opts=opts.Axis3DOpts(name=config_zAxis3D,type_="value",# textstyle_opts=opts.TextStyleOpts(color="#fff"),),grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),)
#     .set_global_opts(
# #         visualmap_opts=none,
#         legend_opts=opts.LegendOpts(is_show=True)
#     )  .set_series_opts(
#         label_opts=opts.LabelOpts(
#             is_show=False,  # 隐藏数据标签
#         ),itemstyle_opts=opts.ItemStyleOpts(color=lambda params: symbol_list[params.res['clusters']],  # 设置数据点颜色),)
)
c.render_notebook()

跨境电商+RMF+kmeans相关推荐

杨学海：跨境电商新通道-进口保税直邮模式解析
为什么80%的码农都做不了架构师?>>> 杨学海:跨境电商新通道-进口保税直邮模式解析广州威云供应链管理公司总经理杨学海在第九届中国中小企业电子商务大会上表示,其品牌海外通要 ...
跨境电商三单对碰三单申报流程详解
跨境电商三单对碰三单申报流程详解概要:三单申报是指"电子订单.电子运单.支付凭证". 1.电子订单: 适合申报企业类型"电商企业.电商交易平台.电商境内代理企业&quo ...
盘点2015跨境电商：硝烟下的机遇与变革
2015年现已曩昔,对跨境电商来说,这又是不普通的一年,年度标签也现已从"元年"变成了"迸发", 国内外电商巨子.传统零售商.创业公司.物流供应链效劳商等纷繁入 ...
互联网+时代，SAP助力跨境电商全面升级研讨会圆满落幕
由SAP金牌合作伙伴海通安恒科技有限公司(以下简称"海通安恒")主办的以<互联网+时代,SAP助力跨境电商全面升级>为主题的跨境电商行业客户研讨会在深圳福田区大中华喜来 ...
中消协上午发布报告跨境电商假货等仍旧是毛病
法制晚报讯 (记者张鑫)中消协26日上午发布<电子商务法>消费者认知情况调查报告.报告显示,被调查者对<电子商务法>知晓率不足6成.质量不合格和假冒伪劣是电商领域最突出的两大 ...
跨境电商自建站后台系统原型rp_外贸业务员和跨境电商运营哪个好,跨境电商可以去哪个网站学...
我做跨境电商也有六年的时间了,在电商这个行业也有自己的一些经验.经验也许没有其他大卖家丰富,但会将我知道的都进行分享.如果有不懂得亚马逊问题可以+我(V:543482465).我这里给大家安排一堂直播 ...
跨境电商Crazysales的高稳定性架构实践
Crazysales是一家典型的跨境电商企业,以澳洲和英国作为主要目标市场,产品大多数由国内供应商提供.Crazysales不但是Amazon.eBay等大型电商平台上的大卖家,同时在澳洲.新西兰建设 ...
大型跨境电商 JVM 调优经历
大型跨境电商 JVM 调优经历前提: 某大型跨境电商业务发展非常快,线上机器扩容也很频繁,但是对于线上机器的运行情况,特别是jvm内存的情况,一直没有一个统一的标准来给到各个应用服务的owner.经 ...
跨境电商自建站后台系统原型rp_Shoptago---跨境电商平台又一个新选择
在去年邀请2000卖家参与内测的跨境电商自建站平台Shoptago,现在用户数已经达到了3850+,成绩可谓,我们也能在各大媒体看到Shoptago相关的新功能上线公告,那么在这段时间里,Shopta ...

跨境电商+RMF+kmeans

思路

代码

读取数据

数据处理

月度主要指标分析

国家销售额、订单量

可视化（月度指标可视化）

地域情况可视化

融合

RMF

占比

kmeans

显著性对比

3D散点图

跨境电商+RMF+kmeans相关推荐

最新文章

热门文章

跨境电商+RMF+kmeans

思路

代码

读取数据

数据处理

月度主要指标分析

国家销售额 、订单量

可视化（月度指标可视化）

地域情况可视化

融合

RMF

占比

kmeans

显著性对比

3D散点图

跨境电商+RMF+kmeans相关推荐

最新文章

热门文章

国家销售额、订单量