python零售数据分析

在零售行业的数据分析中，Python是一个非常强大的工具。下面是两个例子来介绍如何使用Python进行零售数据分析：

1. 查看主类别的分布情况[^1]：

import plotly.graph_objs as go
import plotly.offline as py
import numpy as np

x = train['general_cat'].value_counts().index.values.astype('str')
y = train['general_cat'].value_counts().values
pct = [("%.2f"%(v*100))+"%"for v in (y/len(train))]

trace1 = go.Bar(x=x, y=y, text=pct)
layout = dict(title= 'Number of Items by Main Category', yaxis = dict(title='Count'), xaxis = dict(title='Category'))
fig=dict(data=[trace1], layout=layout)
py.iplot(fig)

2. 查看主类别与价格之间的关系：

import plotly.graph_objs as go
import plotly.offline as py
import numpy as np

general_cats = train['general_cat'].unique()
x = [train.loc[train['general_cat']==cat, 'price'] for cat in general_cats]
data = [go.Box(x=np.log(x[i]+1), name=general_cats[i]) for i in range(len(general_cats))]
layout = dict(title="Price Distribution by General Category", yaxis = dict(title='Category'), xaxis = dict(title='log(1+price)'))
fig = dict(data=data, layout=layout)
py.iplot(fig)

这些例子使用了Plotly库来可视化数据。第一个例子使用了条形图来展示主类别的数量分布情况，第二个例子使用了箱线图来展示主类别与价格之间的关系。