PySpark - 将列表作为参数传递给 UDF + 迭代数据框列添加

Question

这个例子是从a link借来的！

我想了解为什么数据框a - 在看似添加了列“category”之后，无法在后续操作中引用。数据框a 在某种程度上是不可变的吗？是否有另一种方法可以对数据框a 进行操作，以便后续操作可以访问列'category'？谢谢你的帮助;我还在学习曲线上。现在，可以一次添加所有列以避免错误，但这不是我想要在这里做的。

#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
....

Answer 1

我认为您的代码有两个问题：

• 首先，正如@pault 所说，withColumn 没有到位，您需要相应地修改您的代码。
• 其次，你的cate2函数不正确。从某种意义上说，您将其应用于列category，同时您请求将feature_list.category 与某些内容进行比较。

您可能希望摆脱第一个功能并执行以下操作：

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|
+------+---------+--------------+

为第二个函数做这样的事情：

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|
+------+---------+--------------+---------+