sql查询
customers = pd.DataFrame({'customer_id': {0: 5386596, 1: 32676876}, 'created_at': {0: Timestamp('2017-01-27 00:00:00'), 1: Timestamp('2018-06-07 00:00:00')}, 'venture_code': {0: 'MY', 1: 'ID'}})
visits = Pd.DataFrame({'customer_id': {0: 3434886, 1: 10053}, 'date': {0: Timestamp('2016-10-02 00:00:00'), 1: Timestamp('2017-12-14 00:00:00')}})
orders = Pd.DataFrame({'order_id': {0: 112525, 1: 112525}, 'date': {0: Timestamp('2019-02-01 00:00:00'), 1: Timestamp('2019-02-01 00:00:00')}, 'sku': {0: 'SA108SH89OLAHK', 1: 'RO151AA60REHHK'}, 'customer_id': {0: 46160566, 1: 46160566}})
products = Pd.DataFrame({'sku': {0: 'SA108SH89OLAHK', 1: 'RO151AA60REHHK'}, 'brand': {0: 1, 1: 1}, 'supplier': {0: 'A', 1: 'B'}, 'category': {0: 'Mapp', 1: 'Macc'}, 'price': {0: 15, 1: 45}})
segment = Pd.DataFrame({'Age Range': {0: '<20', 1: '<20'},
'Gender': {0: 'female', 1: 'female'},
'Category': {0: 'Wsho', 1: 'Wapp'},
'Discount %': {0: 0.246607432, 1: 0.174166503},
'NMV': {0: 2509.580375, 1: 8910.447587},
'# Items': {0: 169, 1: 778},
'# Orders': {0: 15, 1: 135}})
buying = Pd.DataFrame({'Supplier Name': {0: 'A', 1: 'A'},
'Brand Name': {0: 1, 1: 2},
'# SKU': {0: 506, 1: 267},
'# Item Before Return': {0: 5663, 1: 3256},
'# Item Returned': {0: 2776, 1: 1395},
'Margin %': {0: 0.266922793, 1: 0.282847894},
'GMV': {0: 191686.749171408, 1: 115560.037075292}})
使用sql或pandas,请告诉我如何
1.比较2019年第四季度所有国家/地区的月度销售额(gmv)趋势(风险企业代码)
2.根据总销售额(gmv)显示每个产品类别的前10个品牌
我写了,但问错了!
SELECT category, SUM(GMV) as Total_Sales FROM products INNER JOIN buying ON products.brand = buying.[Brand Name]
1条答案
按热度按时间cqoc49vn1#
关于这个错误,列名中有一个空格。
在sql中,如果列有空格,请使用括号将列名括起来:
在代码中,使用以下sql:
我无法访问您的数据,因此无法进行测试,但我认为这些查询是正确的。你可能需要调整一下。
第1部分:
第2部分: