如何将条件应用于 PySpark 中的 groupby 数据框

Question

我有这样一个数据框：

ID   Transaction_time     Status     final_time
1     1981-01-12           hit    
1     1981-01-13           hit        
1     1981-01-14           good     1981-01-15   
1     1981-01-15           OK       1981-01-16
2     1981-01-06           good     1981-01-17
3     1981-01-07           hit      1981-01-16
4     1981-01-06           hit      
4     1981-01-07           good      
4     1981-01-08           good     1981-01-10

我想保留 ID 如果：

Status 有“成功”和“好”/“好”
最后Transaction_time的final_time不为空

那么，我想提取：

id - ID
status - 最后一个Transaction_time
start_time - 当 Status 从“命中”变为“好”时的 Transaction_time
finish_time - 最后的 final_time Transaction_time

对于上面的例子，它将是：

id    status       start_time       finish_time
1     OK           1981-01-14       1981-01-16
4     good         1981-01-07       1981-01-10

如何在 PySpark 中实现？

Answer 1

您的开始时间是状态“良好”时。可以创建一个列以仅获取状态良好的日期并将其分组。尝试了我的方法，希望这对您有所帮助。

from pyspark.sql import functions as f

df.show()
+---+----------------+------+----------+
| ID|Transaction_time|Status|final_time|
+---+----------------+------+----------+
|  1|      1981-01-12|   hit|      null|
|  1|      1981-01-13|   hit|      null|
|  1|      1981-01-14|  good|1981-01-15|
|  1|      1981-01-15|    OK|1981-01-16|
|  2|      1981-01-06|  good|1981-01-17|
|  3|      1981-01-07|   hit|1981-01-16|
|  4|      1981-01-06|   hit|      null|
|  4|      1981-01-07|  good|      null|
|  4|      1981-01-08|  good|1981-01-10|
+---+----------------+------+----------+


df = df.withColumn('trans_time',f.when(f.col('Status') == 'good',f.col('Transaction_time')).otherwise(None))
+---+----------------+------+----------+----------+
| ID|Transaction_time|Status|final_time|trans_time|
+---+----------------+------+----------+----------+
|  1|      1981-01-12|   hit|      null|      null|
|  1|      1981-01-13|   hit|      null|      null|
|  1|      1981-01-14|  good|1981-01-15|1981-01-14|
|  1|      1981-01-15|    OK|1981-01-16|      null|
|  2|      1981-01-06|  good|1981-01-17|1981-01-06|
|  3|      1981-01-07|   hit|1981-01-16|      null|
|  4|      1981-01-06|   hit|      null|      null|
|  4|      1981-01-07|  good|      null|1981-01-07|
|  4|      1981-01-08|  good|1981-01-10|1981-01-08|
+---+----------------+------+----------+----------+

cnd1 = f.when((f.max('Status') == 'hit') & (f.min('Status').isin(['OK','good'])),f.first('trans_time',ignorenulls=True))

cnd2 = f.when((f.max('Status') == 'hit') & (f.min('Status').isin(['OK','good'])),f.last('final_time',ignorenulls=True))
df.groupby('id').agg(cnd1.name("start_time"),f.min('Status').name('status'),cnd2.name('finish_time')).dropna().show()

+---+----------+------+-----------+
| id|start_time|status|finish_time|
+---+----------+------+-----------+
|  1|1981-01-14|    OK| 1981-01-16|
|  4|1981-01-08|  good| 1981-01-10|
+---+----------+------+-----------+

Answer 2

我主要使用 window 函数而不是 groupby:

w1 = Window.partitionBy('ID').orderBy(F.col('Transaction_time').desc())
w2 = Window.partitionBy('ID').orderBy(F.col('final_time').desc())

df2 = df1.withColumn('next_st', F.lag('Status', 1).over(w1)) \
         .withColumn('next_tt', F.lag('Transaction_time', 1).over(w1)) \
         .withColumn('max_tt', F.max('Transaction_time').over(w1)) \
         .withColumn('max_ft', F.max('final_time').over(w2))
df3 = df2.join(df2.filter((F.col('Transaction_time') == F.col('max_tt')) & F.col('final_time').isNotNull()), 'ID', 'leftsemi')
df4 = df3.filter((F.col('Status') == 'hit') & F.col('next_st').isin(['good', 'OK']))
df5 = (
    df4.alias('df4')
    .join(df1.alias('df1'), (df1.ID == df4.ID) & (F.col('df1.final_time') == F.col('df4.max_ft')))
    .select(
        F.col('df4.ID').alias('id'),
        F.col('df1.Status').alias('status'),
        F.col('df4.next_tt').alias('start_time'),
        F.col('df4.max_ft').alias('finish_time')
    )
)
df5.show()
#  +---+------+----------+-----------+
#  | id|status|start_time|finish_time|
#  +---+------+----------+-----------+
#  |  4|  good|1981-01-07| 1981-01-10|
#  |  1|    OK|1981-01-14| 1981-01-16|
#  +---+------+----------+-----------+

进口：

from pyspark.sql import functions as F, Window

原始数据集：

data = [
(1, '1981-01-12', 'hit', None),
(1, '1981-01-13', 'hit', None),
(1, '1981-01-14', 'good', '1981-01-15'),
(1, '1981-01-15', 'OK', '1981-01-16'),
(2, '1981-01-06', 'good', '1981-01-17'),
(3, '1981-01-07', 'hit', '1981-01-16'),
(4, '1981-01-06', 'hit', None),
(4, '1981-01-07', 'good', None),
(4, '1981-01-08', 'good', '1981-01-10')]
df1 = spark.createDataFrame(data, ['ID', 'Transaction_time', 'Status', 'final_time'])
df1 = df1.withColumn('Transaction_time', F.col('Transaction_time').cast('date')) \
         .withColumn('final_time', F.col('final_time').cast('date'))
df1.show()
#  +---+----------------+------+----------+
#  | ID|Transaction_time|Status|final_time|
#  +---+----------------+------+----------+
#  |  1|      1981-01-12|   hit|      null|
#  |  1|      1981-01-13|   hit|      null|
#  |  1|      1981-01-14|  good|1981-01-15|
#  |  1|      1981-01-15|    OK|1981-01-16|
#  |  2|      1981-01-06|  good|1981-01-17|
#  |  3|      1981-01-07|   hit|1981-01-16|
#  |  4|      1981-01-06|   hit|      null|
#  |  4|      1981-01-07|  good|      null|
#  |  4|      1981-01-08|  good|1981-01-10|
#  +---+----------------+------+----------+

中级dfs：

df1
+---+----------------+------+----------+
| ID|Transaction_time|Status|final_time|
+---+----------------+------+----------+
|  1|      1981-01-12|   hit|      null|
|  1|      1981-01-13|   hit|      null|
|  1|      1981-01-14|  good|1981-01-15|
|  1|      1981-01-15|    OK|1981-01-16|
|  2|      1981-01-06|  good|1981-01-17|
|  3|      1981-01-07|   hit|1981-01-16|
|  4|      1981-01-06|   hit|      null|
|  4|      1981-01-07|  good|      null|
|  4|      1981-01-08|  good|1981-01-10|
+---+----------------+------+----------+

df2
+---+----------------+------+----------+-------+----------+----------+----------+
| ID|Transaction_time|Status|final_time|next_st|   next_tt|    max_tt|    max_ft|
+---+----------------+------+----------+-------+----------+----------+----------+
|  1|      1981-01-15|    OK|1981-01-16|   null|      null|1981-01-15|1981-01-16|
|  1|      1981-01-14|  good|1981-01-15|     OK|1981-01-15|1981-01-15|1981-01-16|
|  1|      1981-01-13|   hit|      null|   good|1981-01-14|1981-01-15|1981-01-16|
|  1|      1981-01-12|   hit|      null|    hit|1981-01-13|1981-01-15|1981-01-16|
|  3|      1981-01-07|   hit|1981-01-16|   null|      null|1981-01-07|1981-01-16|
|  2|      1981-01-06|  good|1981-01-17|   null|      null|1981-01-06|1981-01-17|
|  4|      1981-01-08|  good|1981-01-10|   null|      null|1981-01-08|1981-01-10|
|  4|      1981-01-07|  good|      null|   good|1981-01-08|1981-01-08|1981-01-10|
|  4|      1981-01-06|   hit|      null|   good|1981-01-07|1981-01-08|1981-01-10|
+---+----------------+------+----------+-------+----------+----------+----------+

df3
+---+----------------+------+----------+-------+----------+----------+----------+
| ID|Transaction_time|Status|final_time|next_st|   next_tt|    max_tt|    max_ft|
+---+----------------+------+----------+-------+----------+----------+----------+
|  1|      1981-01-15|    OK|1981-01-16|   null|      null|1981-01-15|1981-01-16|
|  1|      1981-01-14|  good|1981-01-15|     OK|1981-01-15|1981-01-15|1981-01-16|
|  1|      1981-01-13|   hit|      null|   good|1981-01-14|1981-01-15|1981-01-16|
|  1|      1981-01-12|   hit|      null|    hit|1981-01-13|1981-01-15|1981-01-16|
|  3|      1981-01-07|   hit|1981-01-16|   null|      null|1981-01-07|1981-01-16|
|  2|      1981-01-06|  good|1981-01-17|   null|      null|1981-01-06|1981-01-17|
|  4|      1981-01-08|  good|1981-01-10|   null|      null|1981-01-08|1981-01-10|
|  4|      1981-01-07|  good|      null|   good|1981-01-08|1981-01-08|1981-01-10|
|  4|      1981-01-06|   hit|      null|   good|1981-01-07|1981-01-08|1981-01-10|
+---+----------------+------+----------+-------+----------+----------+----------+

df4
+---+----------------+------+----------+-------+----------+----------+----------+
| ID|Transaction_time|Status|final_time|next_st|   next_tt|    max_tt|    max_ft|
+---+----------------+------+----------+-------+----------+----------+----------+
|  1|      1981-01-13|   hit|      null|   good|1981-01-14|1981-01-15|1981-01-16|
|  4|      1981-01-06|   hit|      null|   good|1981-01-07|1981-01-08|1981-01-10|
+---+----------------+------+----------+-------+----------+----------+----------+

df5
+---+------+----------+-----------+
| id|status|start_time|finish_time|
+---+------+----------+-----------+
|  4|  good|1981-01-07| 1981-01-10|
|  1|    OK|1981-01-14| 1981-01-16|
+---+------+----------+-----------+

如何将条件应用于 PySpark 中的 groupby 数据框

How to apply conditions to groupby dataframe in PySpark

group-by

window-functions

apache-spark

apache-spark-sql

pyspark