3

系统：Cloudera Quickstart VM 5.4 上的 Spark 1.3.0 (Anaconda Python dist.)

这是一个 Spark DataFrame：

from pyspark.sql import SQLContext
from pyspark.sql.types import *
sqlContext = SQLContext(sc)

data = sc.parallelize([('Foo',41,'US',3),
                       ('Foo',39,'UK',1),
                       ('Bar',57,'CA',2),
                       ('Bar',72,'CA',3),
                       ('Baz',22,'US',6),
                       (None,75,None,7)])

schema = StructType([StructField('Name', StringType(), True),
                     StructField('Age', IntegerType(), True),
                     StructField('Country', StringType(), True),
                     StructField('Score', IntegerType(), True)])

df = sqlContext.createDataFrame(data,schema)

data.show()

Name Age Country Score
Foo  41  US      3    
Foo  39  UK      1    
Bar  57  CA      2    
Bar  72  CA      3    
Baz  22  US      6    
null 75  null    7

然而，这些都不起作用！

df.dropna()
df.na.drop()

我收到这条消息：

>>> df.show()
Name Age Country Score
Foo  41  US      3    
Foo  39  UK      1    
Bar  57  CA      2    
Bar  72  CA      3    
Baz  22  US      6    
null 75  null    7    
>>> df.dropna().show()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/pyspark/sql/dataframe.py", line 580, in __getattr__
    jc = self._jdf.apply(name)
  File "/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/usr/lib/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o50.apply.
: org.apache.spark.sql.AnalysisException: Cannot resolve column name "dropna" among (Name, Age, Country, Score);
    at org.apache.spark.sql.DataFrame$$anonfun$resolve$1.apply(DataFrame.scala:162)
    at org.apache.spark.sql.DataFrame$$anonfun$resolve$1.apply(DataFrame.scala:162)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.sql.DataFrame.resolve(DataFrame.scala:161)
    at org.apache.spark.sql.DataFrame.col(DataFrame.scala:436)
    at org.apache.spark.sql.DataFrame.apply(DataFrame.scala:426)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:207)
    at java.lang.Thread.run(Thread.java:745)

有没有其他人遇到过这个问题？解决方法是什么？Pyspark 似乎我正在寻找一个名为“na”的列。任何帮助，将不胜感激！

4

2 回答 2

10

tl;dr方法na和dropna仅在 Spark 1.3.1 之后可用。

你犯的几个错误：

  1. data = sc.parallelize([....('',75,'', 7 )])，您打算使用''来表示None，但是，它只是一个 String 而不是 null

  2. na并且dropna都是 dataFrame 类上的两个方法，因此，你应该用你的df.

可运行代码：

data = sc.parallelize([('Foo',41,'US',3),
                       ('Foo',39,'UK',1),
                       ('Bar',57,'CA',2),
                       ('Bar',72,'CA',3),
                       ('Baz',22,'US',6),
                       (None, 75, None, 7)])

schema = StructType([StructField('Name', StringType(), True),
                 StructField('Age', IntegerType(), True),
                 StructField('Country', StringType(), True),
                 StructField('Score', IntegerType(), True)])

df = sqlContext.createDataFrame(data,schema)

df.dropna().show()
df.na.drop().show()
于 2015-05-15T09:31:39.283 回答
-1

我意识到这个问题是在一年前提出的，以防万一将解决方案留给 Scala，以防万一有人在这里寻找相同的解决方案

val data = sc.parallelize(List(("Foo",41,"US",3), ("Foo",39,"UK",1),   
("Bar",57,"CA",2), ("Bar",72,"CA",3), ("Baz",22,"US",6), (None, 75,  
   None, 7)))
val schema = StructType(Array(StructField("Name", StringType, true),   
  StructField("Age", IntegerType, true), StructField("Country", 
  StringType, true), StructField("Score", IntegerType, true)))

val dat = data.map(d => Row(d._1, d._2, d._3, d._4))
val df = sqlContext.createDataFrame(dat, schema)
df.na.drop()

注意：上述解决方案仍然无法在 Scala 中给出正确的结果，不确定 Scala 和 python 绑定之间的实现有什么不同。如果丢失的数据表示为null ，则na.drop有效。“”和“无”失败。一种替代方法是使用withColumn函数来处理不同形式的缺失值

于 2016-12-28T10:10:49.940 回答