[英]pyspark lag function on one column based on the value in another column
I want to be able to create a lag value based on the value in one of the columns.我希望能够根据其中一列中的值创建滞后值。
in the data given Qdf is the Question dataframe and Adf the Answer dataframe.在给定的数据中 Qdf 是问题数据框和 Adf 答案数据框。 I have given an additional explanation column (which I actually dont need in my final data)
我已经给出了一个额外的解释列(在我的最终数据中实际上不需要)
from pyspark.sql.window import Window
import pyspark.sql.functions as func
from pyspark.sql.types import *
from pyspark.sql import SQLContext
ID = ['A' for i in range(0,10)]+ ['B' for i in range(0,10)]
Day = range(1,11)+range(1,11)
Delay = [2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 3]
Despatched = [2, 3, 1, 4, 6, 2, 6, 5, 3, 6, 3, 1, 2, 4, 1, 2, 3, 3, 6, 1]
Delivered = [0, 0, 2, 3, 1, 0, 10, 0, 0, 13, 0, 0, 3, 1, 0, 6, 0, 0, 6, 3]
Explanation = ["-", "-", "-", "-", "-", "-", "10 (4+6)", "-", "-", "13 (2+6+5)", "-", "-", "-", "-", "-", "6 (2+4)", "-", "-", "6 (1+2+3)", "-"]
QSchema = StructType([StructField("ID", StringType()),StructField("Day", IntegerType()),StructField("Delay", IntegerType()),StructField("Despatched", IntegerType())])
Qdata = map(list, zip(*[ID,Day,Delay,Despatched]))
Qdf = spark.createDataFrame(Qdata,schema=QSchema)
Qdf.show()
+---+---+-----+----------+
| ID|Day|Delay|Despatched|
+---+---+-----+----------+
| A| 1| 2| 2|
| A| 2| 2| 3|
| A| 3| 2| 1|
| A| 4| 3| 4|
| A| 5| 2| 6|
| A| 6| 4| 2|
| A| 7| 3| 6|
| A| 8| 2| 5|
| A| 9| 2| 3|
| A| 10| 2| 6|
| B| 1| 2| 3|
| B| 2| 2| 1|
| B| 3| 3| 2|
| B| 4| 2| 4|
| B| 5| 4| 1|
| B| 6| 3| 2|
| B| 7| 2| 3|
| B| 8| 2| 3|
| B| 9| 2| 6|
| B| 10| 3| 1|
+---+---+-----+----------+
The despatched quantity should be recorded as delivered after the delay time.发货数量应在延迟时间后记录为已发货。 Ideally it would be great if I can apply the
lag function
on the despatched column based on the delay.理想情况下,如果我可以根据
lag function
在调度的列上应用lag function
,那就太好了。 The Answer dataset would look like below:答案数据集如下所示:
Adata = map(list, zip(*[ID,Day,Delay,Despatched,Delivered,Explanation]))
ASchema = StructType([StructField("ID", StringType()),StructField("Day", IntegerType()),StructField("Delay", IntegerType()),StructField("Despatched", IntegerType()),StructField("Delivered", IntegerType()),StructField("Explanation", StringType())])
Adf = spark.createDataFrame(Adata,schema=ASchema)
Adf.show()
+---+---+-----+----------+---------+-----------+
| ID|Day|Delay|Despatched|Delivered|Explanation|
+---+---+-----+----------+---------+-----------+
| A| 1| 2| 2| 0| -|
| A| 2| 2| 3| 0| -|
| A| 3| 2| 1| 2| -|
| A| 4| 3| 4| 3| -|
| A| 5| 2| 6| 1| -|
| A| 6| 4| 2| 0| -|
| A| 7| 3| 6| 10| 10 (4+6)|
| A| 8| 2| 5| 0| -|
| A| 9| 2| 3| 0| -|
| A| 10| 2| 6| 13| 13 (2+6+5)|
| B| 1| 2| 3| 0| -|
| B| 2| 2| 1| 0| -|
| B| 3| 3| 2| 3| -|
| B| 4| 2| 4| 1| -|
| B| 5| 4| 1| 0| -|
| B| 6| 3| 2| 6| 6 (2+4)|
| B| 7| 2| 3| 0| -|
| B| 8| 2| 3| 0| -|
| B| 9| 2| 6| 6| 6 (1+2+3)|
| B| 10| 3| 1| 3| -|
+---+---+-----+----------+---------+-----------+
I have tried the below code to get a constant lag of 2:我已经尝试了下面的代码来获得 2 的恒定滞后:
Qdf1=Qdf.withColumn('Delivered_lag',func.lag(Qdf['Despatched'],2).over(Window.partitionBy("ID").orderBy("Day")))
But, when I try to use lag on one column and lag by another column I get the error:但是,当我尝试在一列上使用滞后并在另一列上滞后时,我得到了错误:
Qdf1=Qdf.withColumn('Delivered_lag',func.lag(Qdf['Despatched'],Qdf['Delay']).over(Window.partitionBy("ID").orderBy("Day")))
TypeError: 'Column' object is not callable
类型错误:“列”对象不可调用
How can I get past this?我怎样才能克服这个? I am using PySpark version 2.3.1 and python version 2.7.13.
我使用的是 PySpark 2.3.1 版和 python 2.7.13 版。
The lag -function takes a fixed value as count parameter, but what you can do is to create a loop with when and otherwise to get what you want: 滞后函数采用固定值作为计数参数,但您可以做的是创建一个包含何时和其他方式的循环以获得您想要的:
from pyspark.sql.window import Window
import pyspark.sql.functions as F
import pyspark.sql.types as T
ID = ['A' for i in range(0,10)]+ ['B' for i in range(0,10)]
#I had to modify this line as I'am working with python3
Day = list(range(1,11))+list(range(1,11))
Delay = [2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 3]
Despatched = [2, 3, 1, 4, 6, 2, 6, 5, 3, 6, 3, 1, 2, 4, 1, 2, 3, 3, 6, 1]
Delivered = [0, 0, 2, 3, 1, 0, 10, 0, 0, 13, 0, 0, 3, 1, 0, 6, 0, 0, 6, 3]
Explanation = ["-", "-", "-", "-", "-", "-", "10 (4+6)", "-", "-", "13 (2+6+5)", "-", "-", "-", "-", "-", "6 (2+4)", "-", "-", "6 (1+2+3)", "-"]
QSchema = T.StructType([T.StructField("ID", T.StringType()),T.StructField("Day", T.IntegerType()),T.StructField("Delay", T.IntegerType()),T.StructField("Despatched", T.IntegerType())])
Qdata = map(list, zip(*[ID,Day,Delay,Despatched]))
Qdf = spark.createDataFrame(Qdata,schema=QSchema)
#until here it was basically your code
#At first we add an empty Delivered_lag column to the Qdf
#That allows us to use the same functionality for all iterations of the following loop
Qdf = Qdf.withColumn('Delivered_lag', F.lit(None).cast(T.IntegerType()))
#Now we loop over the distinctive values of Qdf.delay and run the lag function for every value
#otherwise is necessary to keep the previous calculated values
for delay in Qdf.select('delay').distinct().collect():
Qdf = Qdf.withColumn('Delivered_lag', F.when(Qdf['Delay'] == delay.delay, F.lag(Qdf['Despatched'],delay.delay).over(Window.partitionBy("ID").orderBy("Day"))).otherwise(Qdf['Delivered_lag']))
Qdf.show()
Output:输出:
+---+---+-----+----------+-------------+
| ID|Day|Delay|Despatched|Delivered_lag|
+---+---+-----+----------+-------------+
| B| 1| 2| 3| null|
| B| 2| 2| 1| null|
| B| 3| 3| 2| null|
| B| 4| 2| 4| 1|
| B| 5| 4| 1| 3|
| B| 6| 3| 2| 2|
| B| 7| 2| 3| 1|
| B| 8| 2| 3| 2|
| B| 9| 2| 6| 3|
| B| 10| 3| 1| 3|
| A| 1| 2| 2| null|
| A| 2| 2| 3| null|
| A| 3| 2| 1| 2|
| A| 4| 3| 4| 2|
| A| 5| 2| 6| 1|
| A| 6| 4| 2| 3|
| A| 7| 3| 6| 4|
| A| 8| 2| 5| 2|
| A| 9| 2| 3| 6|
| A| 10| 2| 6| 5|
+---+---+-----+----------+-------------+
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