[英]PySpark - Pass list as parameter to UDF + iterative dataframe column addition
我从一个链接借了这个例子!
我想了解为什么数据帧a
-有过栏“之后category
”看似添加到它,不能在后续操作中被引用。 数据框是a
莫名其妙不变? 还有另一种对数据框a
进行操作的方式,以便后续操作可以访问“ category
”列吗? 谢谢你的帮助; 我仍在学习中。 现在,可以一次添加所有列以避免错误,但这不是我想要在此处执行的操作。
#sample data
a= sqlContext.createDataFrame([("A", 20), ("B", 30), ("D", 80),("E",0)],["Letter", "distances"])
label_list = ["Great", "Good", "OK", "Please Move", "Dead"]
#Passing List as Default value to a variable
def cate( feature_list,label=label_list):
if feature_list == 0:
return label[4]
else:
return 'I am not sure!'
def cate2( feature_list,label=label_list):
if feature_list == 0:
return label[4]
elif feature_list.category=='I am not sure!':
return 'Why not?'
udfcate = udf(cate, StringType())
udfcate2 = udf(cate2, StringType())
a.withColumn("category", udfcate("distances"))
a.show()
a.withColumn("category2", udfcate2("category")).show()
a.show()
我得到错误:
C:\Users\gowreden\AppData\Local\Continuum\anaconda3\python.exe C:/Users/gowreden/PycharmProjects/DRC/src/tester.py
2018-08-09 09:06:42 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
+------+---------+--------------+
|Letter|distances| category|
+------+---------+--------------+
| A| 20|I am not sure!|
| B| 30|I am not sure!|
| D| 80|I am not sure!|
| E| 0| Dead|
+------+---------+--------------+
Traceback (most recent call last):
File "C:\Programs\spark-2.3.1-bin-hadoop2.7\python\pyspark\sql\utils.py", line 63, in deco
return f(*a, **kw)
File "C:\Programs\spark-2.3.1-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o34.withColumn.
: org.apache.spark.sql.AnalysisException: cannot resolve '`category`' given input columns: [Letter, distances];;
'Project [Letter#0, distances#1L, cate('category) AS category2#20]
+- AnalysisBarrier
+- LogicalRDD [Letter#0, distances#1L], false
....
我认为您的代码有两个问题:
withColumn
不在适当的位置,您需要相应地修改代码。 cate2
函数不正确。 从某种意义上说,您将其应用于列category
,同时又请求将feature_list.category
与某些内容进行比较。 您可能想要摆脱第一个功能,然后执行以下操作:
import pyspark.sql.functions as F
a=a.withColumn('category', F.when(a.distances==0, label_list[4]).otherwise('I am not sure!'))
a.show()
输出:
+------+---------+--------------+
|Letter|distances| category|
+------+---------+--------------+
| A| 20|I am not sure!|
| B| 30|I am not sure!|
| D| 80|I am not sure!|
| E| 0| Dead|
+------+---------+--------------+
然后对第二个功能执行以下操作:
a=a.withColumn('category2', F.when(a.distances==0, label_list[4]).otherwise(F.when(a.category=='I am not sure!', 'Why not?')))
a.show()
输出:
+------+---------+--------------+---------+
|Letter|distances| category|category2|
+------+---------+--------------+---------+
| A| 20|I am not sure!| Why not?|
| B| 30|I am not sure!| Why not?|
| D| 80|I am not sure!| Why not?|
| E| 0| Dead| Dead|
+------+---------+--------------+---------+
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