Python 在PySpark中查找给定周内的行数

我有一个PySpark数据帧，下面给出了其中的一小部分：

+------+-----+-------------------+-----+
|  name| type|          timestamp|score|
+------+-----+-------------------+-----+
| name1|type1|2012-01-10 00:00:00|   11|
| name1|type1|2012-01-10 00:00:10|   14|
| name1|type1|2012-01-10 00:00:20|    2|
| name1|type1|2012-01-10 00:00:30|    3|
| name1|type1|2012-01-10 00:00:40|   55|
| name1|type1|2012-01-10 00:00:50|   10|
| name5|type1|2012-01-10 00:01:00|    5|
| name2|type2|2012-01-10 00:01:10|    8|
| name5|type1|2012-01-10 00:01:20|    1|
|name10|type1|2012-01-10 00:01:30|   12|
|name11|type3|2012-01-10 00:01:40|  512|
+------+-----+-------------------+-----+

对于选定的时间窗口（例如

1周

），我想知道每个

名称

有多少个

score

（例如

num\u values\u week

）的值。也就是说，在

2012-01-10-2012-01-16

之间，然后在

2012-01-16-2012-01-23

之间，对于

name1

有多少个

score

值（对于所有其他名称，如

name2

等，都是相同的）

我想在新的PySpark数据框中强制转换此信息，该数据框将包含列

name

，

type

，

num\u values\u week

。我该怎么做

可以使用以下代码段创建上面给出的PySpark数据帧：

from pyspark.sql import *
import pyspark.sql.functions as F

df_Stats = Row("name", "type", "timestamp", "score")

df_stat1 = df_Stats('name1', 'type1', "2012-01-10 00:00:00", 11)
df_stat2 = df_Stats('name2', 'type2', "2012-01-10 00:00:00", 14)
df_stat3 = df_Stats('name3', 'type3', "2012-01-10 00:00:00", 2)
df_stat4 = df_Stats('name4', 'type1', "2012-01-17 00:00:00", 3)
df_stat5 = df_Stats('name5', 'type3', "2012-01-10 00:00:00", 55)
df_stat6 = df_Stats('name2', 'type2', "2012-01-17 00:00:00", 10)
df_stat7 = df_Stats('name7', 'type3', "2012-01-24 00:00:00", 5)
df_stat8 = df_Stats('name8', 'type2', "2012-01-17 00:00:00", 8)
df_stat9 = df_Stats('name1', 'type1', "2012-01-24 00:00:00", 1)
df_stat10 = df_Stats('name10', 'type2', "2012-01-17 00:00:00", 12)
df_stat11 = df_Stats('name11', 'type3', "2012-01-24 00:00:00", 512)

df_stat_lst = [df_stat1 , df_stat2, df_stat3, df_stat4, df_stat5, 
            df_stat6, df_stat7, df_stat8, df_stat9, df_stat10, df_stat11]
df = spark.createDataFrame(df_stat_lst)

大概是这样的：

from pyspark.sql.functions import weekofyear, count

df = df.withColumn( "week_nr", weekofyear(df.timestamp) ) # create the week number first
result = df.groupBy(["week_nr","name"]).agg(count("score")) # for every week see how many rows there are