import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.DataFrameReader
import org.apache.spark.rdd.RDD
import scala.math._
val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate()
// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._
scala> val data: DataFrame = spark.read.format("csv").option("header", false).load("hdfs://ns1/datafile/wangxiao/AffairsNA.csv")
data: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 7 more fields]
scala> val data1 = data.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")
data1: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala> data1.limit(10).show
scala> val resNull=data1.na.drop()
resNull: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala> resNull.limit(10).show()
scala> val res=data1.select("yearsmarried").na.drop()
res: org.apache.spark.sql.DataFrame = [yearsmarried: string]
scala> res.limit(10).show()
+------------+
|yearsmarried|
+------------+
| 10|
| 15|
| 15|
| 1.5|
| 15|
| 4|
| 15|
| 1.5|
| 4|
| 15|
+------------+
scala> val res123=data1.na.fill("wangxiao123")
res123: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala> res123.limit(10).show()
scala> val res2=data1.na.fill(value="wangxiao111",cols=Array("gender","yearsmarried") )
res2: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala> res2.limit(10).show()
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
|affairs| gender|age|yearsmarried|children|religiousness|education|occupation|rating|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
| 0| male| 37| 10| no| 3| 18| 7| 4|
| 0|wangxiao111| 27| wangxiao111| no| 4| 14| 6| null|
| 0|wangxiao111| 32| wangxiao111| yes| 1| 12| 1| null|
| 0|wangxiao111| 57| wangxiao111| yes| 5| 18| 6| null|
| 0|wangxiao111| 22| wangxiao111| no| 2| 17| 6| null|
| 0|wangxiao111| 32| wangxiao111| no| 2| 17| 5| null|
| 0| female| 22| wangxiao111| no| 2| 12| 1| null|
| 0| male| 57| 15| yes| 2| 14| 4| 4|
| 0| female| 32| 15| yes| 4| 16| 1| 2|
| 0| male| 22| 1.5| no| 4| 14| 4| 5|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
scala> val res3=data1.na.fill(Map("gender"->"wangxiao222","yearsmarried"->"wangxiao567") )
res3: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
scala> res3.limit(10).show()
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
|affairs| gender|age|yearsmarried|children|religiousness|education|occupation|rating|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
| 0| male| 37| 10| no| 3| 18| 7| 4|
| 0|wangxiao222| 27| wangxiao567| no| 4| 14| 6| null|
| 0|wangxiao222| 32| wangxiao567| yes| 1| 12| 1| null|
| 0|wangxiao222| 57| wangxiao567| yes| 5| 18| 6| null|
| 0|wangxiao222| 22| wangxiao567| no| 2| 17| 6| null|
| 0|wangxiao222| 32| wangxiao567| no| 2| 17| 5| null|
| 0| female| 22| wangxiao567| no| 2| 12| 1| null|
| 0| male| 57| 15| yes| 2| 14| 4| 4|
| 0| female| 32| 15| yes| 4| 16| 1| 2|
| 0| male| 22| 1.5| no| 4| 14| 4| 5|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
scala> data1.filter("gender is null").select("gender").limit(10).show
+------+
|gender|
+------+
| null|
| null|
| null|
| null|
| null|
+------+
scala> data1.filter("gender is not null").select("gender").limit(10).show
+------+
|gender|
+------+
| male|
|female|
| male|
|female|
| male|
| male|
| male|
| male|
|female|
|female|
+------+
scala> data1.filter( data1("gender").isNull ).select("gender").limit(10).show
+------+
|gender|
+------+
| null|
| null|
| null|
| null|
| null|
+------+
scala> data1.filter("gender<>''").select("gender").limit(10).show
+------+
|gender|
+------+
| male|
|female|
| male|
|female|
| male|
| male|
| male|
| male|
|female|
|female|
+------+
scala> math.sqrt(-1.0)
res21: Double = NaN
scala> math.sqrt(-1.0).isNaN()
res22: Boolean = true

ITPub博客

Spark2 DataFrame 数据框空值查找和处理

最新文章