标签：null no gender NaN DataFrame Dataset female yes male

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.Column
import org.apache.spark.sql.DataFrameReader
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder
import org.apache.spark.sql.functions._
import org.apache.spark.sql.DataFrameStatFunctions
import org.apache.spark.ml.linalg.Vectors
 
 
math.sqrt(-1.0)
res43: Double = NaN
    
math.sqrt(-1.0).isNaN()
res44: Boolean = true
   
   
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]
    
data1.limit(10).show
+-------+------+---+------------+--------+-------------+---------+----------+------+
|affairs|gender|age|yearsmarried|children|religiousness|education|occupation|rating|
+-------+------+---+------------+--------+-------------+---------+----------+------+
|      0|  male| 37|          10|      no|            3|       18|         7|     4|
|      0|  null| 27|        null|      no|            4|       14|         6|  null|
|      0|  null| 32|        null|     yes|            1|       12|         1|  null|
|      0|  null| 57|        null|     yes|            5|       18|         6|  null|
|      0|  null| 22|        null|      no|            2|       17|         6|  null|
|      0|  null| 32|        null|      no|            2|       17|         5|  null|
|      0|female| 22|        null|      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|
+-------+------+---+------------+--------+-------------+---------+----------+------+
    
 // 删除所有列的空值和NaN
val resNull=data1.na.drop()
resNull: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
    
 resNull.limit(10).show()
+-------+------+---+------------+--------+-------------+---------+----------+------+
|affairs|gender|age|yearsmarried|children|religiousness|education|occupation|rating|
+-------+------+---+------------+--------+-------------+---------+----------+------+
|      0|  male| 37|          10|      no|            3|       18|         7|     4|
|      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|
|      0|  male| 37|          15|     yes|            2|       20|         7|     2|
|      0|  male| 27|           4|     yes|            4|       18|         6|     4|
|      0|  male| 47|          15|     yes|            5|       17|         6|     4|
|      0|female| 22|         1.5|      no|            2|       17|         5|     4|
|      0|female| 27|           4|      no|            4|       14|         5|     4|
|      0|female| 37|          15|     yes|            1|       17|         5|     5|
+-------+------+---+------------+--------+-------------+---------+----------+------+
    
 //删除某列的空值和NaN
val res=data1.na.drop(Array("gender","yearsmarried"))
 
// 删除某列的非空且非NaN的低于10的
data1.na.drop(10,Array("gender","yearsmarried"))
    
    
 //填充所有空值的列
val res123=data1.na.fill("wangxiao123")
res123: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
    
 res123.limit(10).show()
+-------+-----------+---+------------+--------+-------------+---------+----------+-----------+
|affairs|     gender|age|yearsmarried|children|religiousness|education|occupation|     rating|
+-------+-----------+---+------------+--------+-------------+---------+----------+-----------+
|      0|       male| 37|          10|      no|            3|       18|         7|          4|
|      0|wangxiao123| 27| wangxiao123|      no|            4|       14|         6|wangxiao123|
|      0|wangxiao123| 32| wangxiao123|     yes|            1|       12|         1|wangxiao123|
|      0|wangxiao123| 57| wangxiao123|     yes|            5|       18|         6|wangxiao123|
|      0|wangxiao123| 22| wangxiao123|      no|            2|       17|         6|wangxiao123|
|      0|wangxiao123| 32| wangxiao123|      no|            2|       17|         5|wangxiao123|
|      0|     female| 22| wangxiao123|      no|            2|       12|         1|wangxiao123|
|      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|
+-------+-----------+---+------------+--------+-------------+---------+----------+-----------+
    
 //对指定的列空值填充
 val res2=data1.na.fill(value="wangxiao111",cols=Array("gender","yearsmarried") )
res2: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
    
 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|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
    
    
val res3=data1.na.fill(Map("gender"->"wangxiao222","yearsmarried"->"wangxiao567") )
res3: org.apache.spark.sql.DataFrame = [affairs: string, gender: string ... 7 more fields]
    
 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|
+-------+-----------+---+------------+--------+-------------+---------+----------+------+
    
 //查询空值列
data1.filter("gender is null").select("gender").limit(10).show
+------+
|gender|
+------+
|  null|
|  null|
|  null|
|  null|
|  null|
+------+
    
    
 data1.filter("gender is not null").select("gender").limit(10).show
+------+
|gender|
+------+
|  male|
|female|
|  male|
|female|
|  male|
|  male|
|  male|
|  male|
|female|
|female|
+------+
    
    
 data1.filter( data1("gender").isNull ).select("gender").limit(10).show
+------+
|gender|
+------+
|  null|
|  null|
|  null|
|  null|
|  null|
+------+
    
    
 data1.filter("gender<>''").select("gender").limit(10).show
+------+
|gender|
+------+
|  male|
|female|
|  male|
|female|
|  male|
|  male|
|  male|
|  male|
|female|
|female|
+------+

标签：null,no,gender,NaN,DataFrame,Dataset,female,yes,male
来源： https://blog.51cto.com/u_15278282/2931962

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