[英]Sql query to do pandas factorise. cumulative sum after a group by?
I have this data frame : basically each row being a transaction carried out by one customer on a day. 我有这个数据框:基本上每一行都是一个客户在一天内完成的交易。 there are multiple transactions by same customer on same day and on different dates.
同一客户在同一天和不同日期进行多笔交易。 I want to get a column for a customers number of previous visits.
我想为客户提供以前访问次数的列。
id date purchase
id1 date1 $10
id1 date1 $50
id1 date2 $30
id2 date1 $10
id2 date1 $10
id3 date3 $10
after adding visits column: 添加访问列后:
id date purchase visit
id1 date1 $10 0
id1 date1 $50 0
id1 date2 $30 1
id2 date1 $10 0
id2 date2 $10 1
id2 date3 $10 2
I do this in pandas using factorize : 我在使用factorize的pandas中这样做:
df.visits = 1
df.visits = df.groupby('id')['date'].transform(lambda x: pd.factorize(x)[0])
I want to do it through SQL, what would the query be like ? 我想通过SQL来做,查询会是什么样的?
You need DENSE_RANK()
with PARTITION BY
: 你需要
DENSE_RANK()
和PARTITION BY
:
Creation of example dataset: 创建示例数据集:
IF OBJECT_ID('Source', 'U') IS NOT NULL
DROP TABLE Source;
CREATE TABLE Source
(
id varchar(30),
Date varchar(30),
purchase varchar(30)
)
INSERT INTO Source
VALUES
('id1', 'date1', '$10'),
('id1', 'date1', '$50'),
('id1', 'date2', '$30'),
('id2', 'date1', '$10'),
('id2', 'date2', '$10'),
('id2', 'date3', '$10')
SELECT *,
DENSE_RANK() OVER (PARTITION BY id ORDER BY date) - 1 AS visit
FROM Source
Output 产量
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