在PySpark数据帧中的选定时间间隔内复制日期间隔之间的记录
在PySpark数据帧中的选定时间间隔内复制日期间隔之间的记录
提问于 2020-12-11 06:46:17
我有一个PySpark数据，它可以跟踪产品价格和状态在几个月内发生的变化。这意味着只有在与前一个月相比发生更改(无论是状态还是价格)时才创建新行，如下面的虚拟数据所示。
----------------------------------------
|product_id| status | price| month |
----------------------------------------
|1 | available | 5 | 2019-10|
----------------------------------------
|1 | available | 8 | 2020-08|
----------------------------------------
|1 | limited | 8 | 2020-10|
----------------------------------------
|2 | limited | 1 | 2020-09|
----------------------------------------
|2 | limited | 3 | 2020-10|
----------------------------------------我想要创建一个显示过去6个月中每一个值的数据。这意味着，每当在上面的数据中出现空白时，我就需要复制记录。例如，如果最后6个月是2020-07,2020-08，. 2020-12，那么上述数据的结果应该是
----------------------------------------
|product_id| status | price| month |
----------------------------------------
|1 | available | 5 | 2020-07|
----------------------------------------
|1 | available | 8 | 2020-08|
----------------------------------------
|1 | available | 8 | 2020-09|
----------------------------------------
|1 | limited | 8 | 2020-10|
----------------------------------------
|1 | limited | 8 | 2020-11|
----------------------------------------
|1 | limited | 8 | 2020-12|
----------------------------------------
|2 | limited | 1 | 2020-09|
----------------------------------------
|2 | limited | 3 | 2020-10|
----------------------------------------
|2 | limited | 3 | 2020-11|
----------------------------------------
|2 | limited | 3 | 2020-12|
----------------------------------------请注意，对于product_id =1，在2019-10年有一个较旧的记录，该记录一直传播到2020-08，然后被裁剪，而对于product_id =2，在2020-09之前没有记录，因此2020-07,2020-08月份没有填充(因为该产品在2020-09之前并不存在)。
由于数据文件由数百万条记录组成，所以使用循环和检查每个product_id的“蛮力”解决方案相当缓慢。使用窗口函数解决这个问题似乎是可能的，方法是创建另一个列next_month，然后根据该列填充空白，但我不知道如何实现这一点。
回答 2
Stack Overflow用户
回答已采纳
发布于 2020-12-16 09:56:13
关于@jxc注释，我已经为这个用例准备了答案。
下面是代码片段。
- 导入spark函数
from pyspark.sql import functions as F, Window
- 准备示例数据
simpleData = ((1,"Available",5,"2020-07"),
(1,"Available",8,"2020-08"),
(1,"Limited",8,"2020-12"),
(2,"Limited",1,"2020-09"),
(2,"Limited",3,"2020-12")
)
columns= ["product_id", "status", "price", "month"]- 创建示例数据的数据格式
df = spark.createDataFrame(data = simpleData, schema = columns)
- 在dataframe中添加日期列以获得正确的格式化日期
df0 = df.withColumn("date",F.to_date('month','yyyy-MM'))
df0.show()
+----------+---------+-----+-------+----------+
|product_id| status|price| month| date|
+----------+---------+-----+-------+----------+
| 1|Available| 5|2020-07|2020-07-01|
| 1|Available| 8|2020-08|2020-08-01|
| 1| Limited| 8|2020-12|2020-12-01|
| 2| Limited| 1|2020-09|2020-09-01|
| 2| Limited| 3|2020-12|2020-12-01|
+----------+---------+-----+-------+----------+- 创建WinSpec w1并使用窗口聚合函数引导查找下一个日期结束(W1)，将其转换为前几个月以设置日期序列：
w1 = Window.partitionBy('product_id').orderBy('date')
df1 = df0.withColumn('end_date',F.coalesce(F.add_months(F.lead('date').over(w1),-1),'date'))
df1.show()
+----------+---------+-----+-------+----------+----------+
|product_id| status|price| month| date| end_date|
+----------+---------+-----+-------+----------+----------+
| 1|Available| 5|2020-07|2020-07-01|2020-07-01|
| 1|Available| 8|2020-08|2020-08-01|2020-11-01|
| 1| Limited| 8|2020-12|2020-12-01|2020-12-01|
| 2| Limited| 1|2020-09|2020-09-01|2020-11-01|
| 2| Limited| 3|2020-12|2020-12-01|2020-12-01|
+----------+---------+-----+-------+----------+----------+- 使用months_between(end_date，date)计算两个日期之间月份的#，并使用转换函数迭代序列(0，# months )，用date=add_months(date，i)和price=IF(i=0，价格，价格)创建named_struct，使用inline_outer爆炸结构数组。
df2 = df1.selectExpr("product_id", "status", inline_outer( transform( sequence(0,int(months_between(end_date, date)),1), i -> (add_months(date,i) as date, IF(i=0,price,price) as price) ) ) )
df2.show()
+----------+---------+----------+-----+
|product_id| status| date|price|
+----------+---------+----------+-----+
| 1|Available|2020-07-01| 5|
| 1|Available|2020-08-01| 8|
| 1|Available|2020-09-01| 8|
| 1|Available|2020-10-01| 8|
| 1|Available|2020-11-01| 8|
| 1| Limited|2020-12-01| 8|
| 2| Limited|2020-09-01| 1|
| 2| Limited|2020-10-01| 1|
| 2| Limited|2020-11-01| 1|
| 2| Limited|2020-12-01| 3|
+----------+---------+----------+-----+ - Partitioning
product_id上的dataframe并在df3中添加一个秩列以获得每一行的行号。然后，为每个df4
使用新列max_rank存储rank列值的最大值，并将max_rank存储到product_id中。
w2 = Window.partitionBy('product_id').orderBy('date')
df3 = df2.withColumn('rank',F.row_number().over(w2))
Schema: DataFrame[product_id: bigint, status: string, date: date, price: bigint, rank: int]
df3.show()
+----------+---------+----------+-----+----+
|product_id| status| date|price|rank|
+----------+---------+----------+-----+----+
| 1|Available|2020-07-01| 5| 1|
| 1|Available|2020-08-01| 8| 2|
| 1|Available|2020-09-01| 8| 3|
| 1|Available|2020-10-01| 8| 4|
| 1|Available|2020-11-01| 8| 5|
| 1| Limited|2020-12-01| 8| 6|
| 2| Limited|2020-09-01| 1| 1|
| 2| Limited|2020-10-01| 1| 2|
| 2| Limited|2020-11-01| 1| 3|
| 2| Limited|2020-12-01| 3| 4|
+----------+---------+----------+-----+----+
df4 = df3.groupBy("product_id").agg(F.max('rank').alias('max_rank'))
Schema: DataFrame[product_id: bigint, max_rank: int]
df4.show()
+----------+--------+
|product_id|max_rank|
+----------+--------+
| 1| 6|
| 2| 4|
+----------+--------+在get max_rank上加入df3和df4数据的
df5 = df3.join(df4,df3.product_id == df4.product_id,"inner") \
.select(df3.product_id,df3.status,df3.date,df3.price,df3.rank,df4.max_rank)
Schema: DataFrame[product_id: bigint, status: string, date: date, price: bigint, rank: int, max_rank: int]
df5.show()
+----------+---------+----------+-----+----+--------+
|product_id| status| date|price|rank|max_rank|
+----------+---------+----------+-----+----+--------+
| 1|Available|2020-07-01| 5| 1| 6|
| 1|Available|2020-08-01| 8| 2| 6|
| 1|Available|2020-09-01| 8| 3| 6|
| 1|Available|2020-10-01| 8| 4| 6|
| 1|Available|2020-11-01| 8| 5| 6|
| 1| Limited|2020-12-01| 8| 6| 6|
| 2| Limited|2020-09-01| 1| 1| 4|
| 2| Limited|2020-10-01| 1| 2| 4|
| 2| Limited|2020-11-01| 1| 3| 4|
| 2| Limited|2020-12-01| 3| 4| 4|
+----------+---------+----------+-----+----+--------+然后，
- 使用
between函数对df5数据进行过滤，以获得最新的6个月数据。
FinalResultDF = df5.filter(F.col('rank') \
.between(F.when((F.col('max_rank') > 5),(F.col('max_rank')-6)).otherwise(0),F.col('max_rank'))) \
.select(df5.product_id,df5.status,df5.date,df5.price)
FinalResultDF.show(truncate=False) +----------+---------+----------+-----+
|product_id|status |date |price|
+----------+---------+----------+-----+
|1 |Available|2020-07-01|5 |
|1 |Available|2020-08-01|8 |
|1 |Available|2020-09-01|8 |
|1 |Available|2020-10-01|8 |
|1 |Available|2020-11-01|8 |
|1 |Limited |2020-12-01|8 |
|2 |Limited |2020-09-01|1 |
|2 |Limited |2020-10-01|1 |
|2 |Limited |2020-11-01|1 |
|2 |Limited |2020-12-01|3 |
+----------+---------+----------+-----+Stack Overflow用户
发布于 2020-12-17 12:07:26
使用spark sql：
给定输入数据：
val df = spark.sql(""" with t1 (
select 1 c1, 'available' c2, 5 c3, '2019-10' c4 union all
select 1 c1, 'available' c2, 8 c3, '2020-08' c4 union all
select 1 c1, 'limited' c2, 8 c3, '2020-10' c4 union all
select 2 c1, 'limited' c2, 1 c3, '2020-09' c4 union all
select 2 c1, 'limited' c2, 3 c3, '2020-10' c4
) select c1 product_id, c2 status , c3 price, c4 month from t1
""")
df.createOrReplaceTempView("df")
df.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2019-10|
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
+----------+---------+-----+-------+筛选日期窗口，即从2020-07到2020-12的6个月，并将它们存储在df1中
val df1 = spark.sql("""
select * from df where month > '2020-07' and month < '2020-12'
""")
df1.createOrReplaceTempView("df1")
df1.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
+----------+---------+-----+-------+较低的边界--当月<='2020-07‘时得到最大值。将月份改写为“2020-07”
val df2 = spark.sql("""
select product_id, status, price, '2020-07' month from df where (product_id,month) in
( select product_id, max(month) from df where month <= '2020-07' group by 1 )
""")
df2.createOrReplaceTempView("df2")
df2.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2020-07|
+----------+---------+-----+-------+上界-使用<='2020-12‘得到最大值。将月份改写为“2020-12”
val df3 = spark.sql("""
select product_id, status, price, '2020-12' month from df where (product_id, month) in
( select product_id, max(month) from df where month <= '2020-12' group by 1 )
""")
df3.createOrReplaceTempView("df3")
df3.show(false)
+----------+-------+-----+-------+
|product_id|status |price|month |
+----------+-------+-----+-------+
|1 |limited|8 |2020-12|
|2 |limited|3 |2020-12|
+----------+-------+-----+-------+现在合并所有的3并将其存储在df4中
val df4 = spark.sql("""
select product_id, status, price, month from df1 union all
select product_id, status, price, month from df2 union all
select product_id, status, price, month from df3
order by product_id, month
""")
df4.createOrReplaceTempView("df4")
df4.show(false)
+----------+---------+-----+-------+
|product_id|status |price|month |
+----------+---------+-----+-------+
|1 |available|5 |2020-07|
|1 |available|8 |2020-08|
|1 |limited |8 |2020-10|
|1 |limited |8 |2020-12|
|2 |limited |1 |2020-09|
|2 |limited |3 |2020-10|
|2 |limited |3 |2020-12|
+----------+---------+-----+-------+结果：使用序列(date1，date2，间隔1个月)生成缺少月份的日期数组。引爆阵列你就能得到结果。
spark.sql("""
select product_id, status, price, month, explode(dt) res_month from
(
select t1.*,
case when months_between(lm||'-01',month||'-01')=1.0 then array(month||'-01')
when month='2020-12' then array(month||'-01')
else sequence(to_date(month||'-01'), add_months(to_date(lm||'-01'),-1), interval 1 month )
end dt
from (
select product_id, status, price, month,
lead(month) over(partition by product_id order by month) lm
from df4
) t1
) t2
order by product_id, res_month
""")
.show(false)
+----------+---------+-----+-------+----------+
|product_id|status |price|month |res_month |
+----------+---------+-----+-------+----------+
|1 |available|5 |2020-07|2020-07-01|
|1 |available|8 |2020-08|2020-08-01|
|1 |available|8 |2020-08|2020-09-01|
|1 |limited |8 |2020-10|2020-10-01|
|1 |limited |8 |2020-10|2020-11-01|
|1 |limited |8 |2020-12|2020-12-01|
|2 |limited |1 |2020-09|2020-09-01|
|2 |limited |3 |2020-10|2020-10-01|
|2 |limited |3 |2020-10|2020-11-01|
|2 |limited |3 |2020-12|2020-12-01|
+----------+---------+-----+-------+----------+页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/65246883
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