计算促销日期所在周的需求量,以及其前一周,后一周的需求量,代码实现python,并用直方图可视化实现
时间: 2024-05-02 16:23:07 浏览: 16
好的,我理解你的问题了。以下是实现计算促销日期周需求量及可视化的Python代码:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 生成模拟销售数据
start_date = '2022-01-01'
end_date = '2022-12-31'
sales_data = pd.DataFrame({
'date': pd.date_range(start=start_date, end=end_date),
'sales': np.random.randint(1, 100, size=(365,))
})
# 假设促销日期为2022-08-01
promo_date = pd.to_datetime('2022-08-01')
# 计算促销日期所在周的需求量,以及前一周和后一周的需求量
promo_week_sales = sales_data[(sales_data['date'] >= promo_date - pd.Timedelta('3 days')) &
(sales_data['date'] <= promo_date + pd.Timedelta('3 days'))]['sales'].sum()
prior_week_sales = sales_data[(sales_data['date'] >= promo_date - pd.Timedelta('10 days')) &
(sales_data['date'] < promo_date - pd.Timedelta('3 days'))]['sales'].sum()
post_week_sales = sales_data[(sales_data['date'] > promo_date + pd.Timedelta('3 days')) &
(sales_data['date'] <= promo_date + pd.Timedelta('10 days'))]['sales'].sum()
# 输出结果
print(f'Promotion week sales: {promo_week_sales:,}')
print(f'Prior week sales: {prior_week_sales:,}')
print(f'Post week sales: {post_week_sales:,}')
# 可视化销售数据
sales_data.set_index('date', inplace=True)
sales_data.resample('W-MON', label='left', closed='left', loffset=pd.Timedelta('-1 days')).sum().plot(kind='bar', figsize=(20, 5))
plt.axvline(x='2022-08-01', color='r', linestyle='--')
plt.title('Weekly Sales')
plt.xlabel('Week Start Date')
plt.ylabel('Sales')
plt.show()
```
运行以上代码,你可以得到促销周、前一周和后一周的需求量,同时可视化销售数据,并在促销周的垂直线处突出显示。更多信息和细节请查看代码注释和Python文档。