chatgpt解决建表维度处理逻辑

需求

现有店铺汇总表：小时日月三种维度划分，内涵客流数据销售数据。
需要根据数据计算出服务区南北区每个小时，日，月，年四种维度的客单价客单转换率，服务区每个店铺的每个小时，日，月，年四种维度的客单价客单转换率。
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计算服务区南北区每个小时、日、月、年的客单价和客单转化率：

-- 按小时统计客单价和客单转化率
SELECTdimension,region,HOUR(partition_date) AS hour,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'hour'
GROUP BY dimension, region, HOUR(partition_date);-- 按日统计客单价和客单转化率
SELECTdimension,region,partition_date AS day,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'day'
GROUP BY dimension, region, partition_date;-- 按月统计客单价和客单转化率
SELECTdimension,region,CONCAT_WS('-', YEAR(partition_date), LPAD(MONTH(partition_date), 2, '0')) AS month,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'month'
GROUP BY dimension, region, CONCAT_WS('-', YEAR(partition_date), LPAD(MONTH(partition_date), 2, '0'));-- 按年统计客单价和客单转化率
SELECTdimension,region,YEAR(partition_date) AS year,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'year'
GROUP BY dimension, region, YEAR(partition_date);

计算每个店铺每个小时、日、月、年的客单价和客单转化率：

-- 按小时统计客单价和客单转化率
SELECTdimension,region,store_id,HOUR(partition_date) AS hour,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'hour'
GROUP BY dimension, region, store_id, HOUR(partition_date);-- 按日统计客单价和客单转化率
SELECTdimension,region,store_id,partition_date AS day,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'day'
GROUP BY dimension, region, store_id, partition_date;-- 按月统计客单价和客单转化率
SELECTdimension,region,store_id,CONCAT_WS('-', YEAR(partition_date), LPAD(MONTH(partition_date), 2, '0')) AS month,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'month'
GROUP BY dimension, region, store_id, CONCAT_WS('-', YEAR(partition_date), LPAD(MONTH(partition_date), 2, '0'));-- 按年统计客单价和客单转化率
SELECTdimension,region,store_id,YEAR(partition_date) AS year,AVG(sales_amount / customer_count) AS average_order_value,SUM(CASE WHEN sales_amount > 0 THEN 1 ELSE 0 END) / COUNT(*) AS conversion_rate
FROM store_summary
WHERE dimension = 'year'
GROUP BY dimension, region, store_id, YEAR(partition_date);

以上示例代码假设您的店铺汇总表中包含了正确的数据，并按照维度和分区进行了正确的分组。您可以根据实际情况对清洗语句进行修改和优化。
希望这些示例能满足您的需求。如果您有任何其他问题，请随时提问。

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有意思的是对他的回答进行反问，他会把表名改一下重新发回来，并没有对sql进行实际改变。

结论：对于简单的业务逻辑处理，建表sql，数据清理sql。chatgpt能给出答案的，就是结果必须进行修改，在代码基础上修改的话是可以节省一点对于开发时间，但是和手动开发时间相差不多。