加载所需文件

attachment_1 = pd.read_excel(r"C:\Users\86136\Desktop\C题(1)\附件1.xlsx") attachment_2 = pd.read_excel(r"C:\Users\86136\Desktop\C题(1)\附件2.xlsx") attachment_3 = pd.read_excel(r"C:\Users\86136\Desktop\C题(1)\附件3.xlsx") attachment_4 = pd.read_excel(r"C:\Users\86136\Desktop\C题(1)\附件4.xlsx")

显示每个数据集的前几行，进行初步检查

attachment_1.head(), attachment_2.head(), attachment_3.head(), attachment_4.head()

合并数据集，根据"单品编码"合并

merged_data = pd.merge(attachment_2, attachment_1, on="单品编码", how="left")

保存合并后的数据集到桌面

merged_data.to_excel(r"C:\Users\86136\Desktop\merged_dataset.xlsx", index=False)

按品类和商品分类销售量

category_sales = merged_data.groupby('分类名称')['销量(千克)'].sum().sort_values(ascending=False) product_sales = merged_data.groupby('单品名称')['销量(千克)'].sum().sort_values(ascending=False)

绘制销售量分布图

fig, ax = plt.subplots(2, 1, figsize=(12, 12))

品类销售量分布图

category_sales.plot(kind='bar', ax=ax[0], color='teal') ax[0].set_title('Total Sales Volume by Category') ax[0].set_ylabel('Sales Volume (kg)') ax[0].set_xlabel('Category Name')

单品销售量分布图（前10个）

product_sales.head(10).plot(kind='bar', ax=ax[1], color='coral') ax[1].set_title('Total Sales Volume by Product (Top 10)') ax[1].set_ylabel('Sales Volume (kg)') ax[1].set_xlabel('Product Name')

plt.tight_layout() plt.show()

根据销售日期和品类分组，计算销售量

category_date_sales = merged_data.groupby(['销售日期', '分类名称'])['销量(千克)'].sum().reset_index()

绘制按时间分类的销售量趋势图

plt.figure(figsize=(16, 8)) for category in category_date_sales['分类名称'].unique(): subset = category_date_sales[category_date_sales['分类名称'] == category] plt.plot(subset['销售日期'], subset['销量(千克)'], label=category)

plt.title('Sales Volume Trend by Category')