06.【转载】Dataset (DataFrame) 的基础操作（二）

Leefs 2021-07-16 PM 1888℃ 0条

# 06.【转载】Dataset (DataFrame) 的基础操作（二）

### 三、Column 对象

#### 导读

> Column 表示了 Dataset 中的一个列, 并且可以持有一个表达式, 这个表达式作用于每一条数据, 对每条数据都生成一个值, 之所以有单独这样的一个章节是因为列的操作属于细节, 但是又比较常见, 会在很多算子中配合出现

![06.【转载】Dataset (DataFrame) 的基础操作（二）01.png](https://lilinchao.com/usr/uploads/2021/07/1430759110.png)

![06.【转载】Dataset (DataFrame) 的基础操作（二）02.png](https://lilinchao.com/usr/uploads/2021/07/655379913.png)

![06.【转载】Dataset (DataFrame) 的基础操作（二）03.png](https://lilinchao.com/usr/uploads/2021/07/1772257516.png)

![06.【转载】Dataset (DataFrame) 的基础操作（二）04.png](https://lilinchao.com/usr/uploads/2021/07/682342499.png)

**全套代码展示：**

```scala
package com.spark.transformation

import com.spark.Person
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql
import org.apache.spark.sql.{ColumnName, DataFrame, Dataset, SparkSession}
import org.junit.Test

class Column {
  Logger.getLogger("org").setLevel(Level.ERROR)
  val spark: SparkSession = SparkSession.builder()
    .master("local[6]")
    .appName(this.getClass.getSimpleName)
    .getOrCreate()

import spark.implicits._
  import org.apache.spark.sql.functions._
  @Test
  def creation(): Unit ={

val ds: Dataset[Person] = Seq(Person("zhangsan", 20),Person("zhangsan", 18), Person("lisi", 15)).toDS
    val df: DataFrame = Seq(Person("zhangsan", 20),Person("zhangsan", 18), Person("lisi", 15)).toDF
    //1. '
    val column:Symbol = 'name

//2. $
    val column1: ColumnName = $"name"

//3. col (必须导入functions)import org.apache.spark.sql.functions._
    val column2: sql.Column = col("name")

//4. column (必须导入functions)
    val column3: sql.Column = column("name")

/**
     * 这四种创建方式有关联的DataSet么
     */
    ds.select(column).show()

//DataSet可以 DataFrame 可以使用 column 对象选中行吗
    df.select(column).show()

//select 方法可以使用column 对象来选中某个列，那么其他的算子行么
    df.where(column === "zhangsan").show()

/**
     * column 有几个创建方式？ -> 四种
     * column 对象可以用作与DataSet 和 DataFrame 中
     * column 可以和命令式的弱类型的API 配合使用select where
     *
     */
    //5. dataset.col
    val column4 = ds.col("name")
    val column5 = df.col("name")

//报错
    //ds.select(column5).show()

//为什么要和dataset 来绑定呢
   // ds.join(df,ds.col("name") === df.col("name"))

//6.dataset.apple
    val column6 = ds.apply("name")
    val column7 = ds("name")

}

@Test
  def as(): Unit ={
    val ds: Dataset[Person] = Seq(Person("zhangsan", 20),Person("zhangsan", 18), Person("lisi", 15)).toDS

ds.select ('name as "new_name").show()

ds.select('age.as[Long]).show()
  }

@Test
  def api(): Unit ={
    val ds: Dataset[Person] = Seq(Person("zhangsan", 20),Person("zhangsan", 18), Person("lisi", 15)).toDS
    //需求一：ds增加列，双倍年龄
    // age*2 其实本质上就是是将一个表达式(逻辑计划表达式) 附着到column对象上
    //表达式在执行的时候对应每一条数据进行操作
    ds.withColumn("doubled",'age * 2).show()
    //需求二：模糊查询
    //select * from table where name like zhang%
    ds.where('name like "zhang%")

//需求三：排序，正反序
    ds.sort('age asc).show()

//需求四：枚举判断
    ds.where('name isin("zhangsan","wangwu","zhaoliu")).show()
  }
}
case class Person(name:String,age:Int)
```

### 四、缺失值处理

#### **导读**

> 1. DataFrame 中什么时候会有无效值
> 2. DataFrame 如何处理无效的值
> 3. DataFrame 如何处理 null

**缺失值的处理思路**

如果想探究如何处理无效值, 首先要知道无效值从哪来, 从而分析可能产生的无效值有哪些类型, 在分别去看如何处理无效值

#### 4.1 **什么是缺失值**

一个值本身的含义是这个值不存在则称之为缺失值, 也就是说这个值本身代表着缺失, 或者这个值本身无意义, 比如说 null, 比如说空字符串

![06.【转载】Dataset (DataFrame) 的基础操作（二）05.png](https://lilinchao.com/usr/uploads/2021/07/2153279182.png)

关于数据的分析其实就是统计分析的概念, 如果这样的话, 当数据集中存在缺失值, 则无法进行统计和分析, 对很多操作都有影响

#### 4.2 **缺失值如何产生的**

![06.【转载】Dataset (DataFrame) 的基础操作（二）06.png](https://lilinchao.com/usr/uploads/2021/07/2580343342.png)

Spark 大多时候处理的数据来自于业务系统中, 业务系统中可能会因为各种原因, 产生一些异常的数据

例如说因为前后端的判断失误, 提交了一些非法参数. 再例如说因为业务系统修改 MySQL 表结构产生的一些空值数据等. 总之在业务系统中出现缺失值其实是非常常见的一件事, 所以大数据系统就一定要考虑这件事.

#### 4.3 缺失值的类型

常见的缺失值有两种

- null, NaN 等特殊类型的值, 某些语言中 null 可以理解是一个对象, 但是代表没有对象, NaN 是一个数字, 可以代表不是数字

针对这一类的缺失值, Spark 提供了一个名为 DataFrameNaFunctions 特殊类型来操作和处理

+ Null”, “NA”, " " 等解析为字符串的类型, 但是其实并不是常规字符串数据

针对这类字符串, 需要对数据集进行采样, 观察异常数据, 总结经验, 各个击破

#### 4.4 **DataFrameNaFunctions**

DataFrameNaFunctions 使用 Dataset 的 na 函数来获取

```scala
val df = ...
val naFunc: DataFrameNaFunctions = df.na
```

当数据集中出现缺失值的时候, 大致有两种处理方式, 一个是丢弃, 一个是替换为某值, DataFrameNaFunctions 中包含一系列针对空值数据的方案

+ DataFrameNaFunctions.drop 可以在当某行中包含 null 或 NaN 的时候丢弃此行
+ DataFrameNaFunctions.fill 可以在将 null 和 NaN 填充为其它值
+ DataFrameNaFunctions.replace 可以把 null 或 NaN 替换为其它值, 但是和 fill 略有一些不同, 这个方法针对值来进行替换

#### 4.5 **如何使用 SparkSQL 处理 null 和 NaN ?**

首先要将数据读取出来, 此次使用的数据集直接存在 NaN, 在指定 Schema 后, 可直接被转为 Double.NaN

```scala
val schema = StructType(
  List(
    StructField("id", IntegerType),
    StructField("year", IntegerType),
    StructField("month", IntegerType),
    StructField("day", IntegerType),
    StructField("hour", IntegerType),
    StructField("season", IntegerType),
    StructField("pm", DoubleType)
  )
)

val df = spark.read
  .option("header", value = true)
  .schema(schema)
  .csv("input/beijingpm_with_nan.csv")
```

对于缺失值的处理一般就是丢弃和填充

#### 4.6 **丢弃包含 null 和 NaN 的行**

当某行数据所有值都是 null 或者 NaN 的时候丢弃此行

```scala
df.na.drop("all").show()
```

当某行中特定列所有值都是 null 或者 NaN 的时候丢弃此行

```scala
df.na.drop("all", List("pm", "id")).show()
```

当某行数据任意一个字段为 null 或者 NaN 的时候丢弃此行

```scala
df.na.drop().show()
df.na.drop("any").show()
```

当某行中特定列任意一个字段为 null 或者 NaN 的时候丢弃此行

```scala
df.na.drop(List("pm", "id")).show()
df.na.drop("any", List("pm", "id")).show()
```

#### 4.7 **填充包含 null 和 NaN 的列**

填充所有包含 null 和 NaN 的列

```scala
df.na.fill(0).show()
```

填充特定包含 null 和 NaN 的列

```scala
df.na.fill(0, List("pm")).show()
```

根据包含 null 和 NaN 的列的不同来填充

```scala
import scala.collection.JavaConverters._

df.na.fill(Map[String, Any]("pm" -> 0).asJava).show
```

#### 4.8 **如何使用 SparkSQL 处理异常字符串 ?**

读取数据集, 这次读取的是最原始的那个 PM 数据集

```scala
val df = spark.read
  .option("header", value = true)
  .csv("dataset/BeijingPM20100101_20151231.csv")
```

使用函数直接转换非法的字符串

```scala
df.select('No as "id", 'year, 'month, 'day, 'hour, 'season,
    when('PM_Dongsi === "NA", 0)
    .otherwise('PM_Dongsi cast DoubleType)
    .as("pm"))
  .show()
```

使用 where 直接过滤

```scala
df.select('No as "id", 'year, 'month, 'day, 'hour, 'season, 'PM_Dongsi)
  .where('PM_Dongsi =!= "NA")
  .show()
```

使用 DataFrameNaFunctions 替换, 但是这种方式被替换的值和新值必须是同类型

```scala
df.select('No as "id", 'year, 'month, 'day, 'hour, 'season, 'PM_Dongsi)
  .na.replace("PM_Dongsi", Map("NA" -> "NaN"))
  .show()
```

**全套代码如下：**

数据准备：（beijingpm_with_nan.csv）

```
id,year,month,day,hour,season,pm
1,2010,1,1,0,4,NaN
2,2010,1,1,1,4,NaN
3,2010,1,1,2,4,NaN
4,2010,1,1,3,4,NaN
5,2010,1,1,4,4,NaN
6,2010,1,1,5,4,NaN
7,2010,1,1,6,4,NaN
8,2010,1,1,7,4,NaN
9,2010,1,1,8,4,NaN
10,2010,1,1,9,4,NaN
11,2010,1,1,10,4,NaN
12,2010,1,1,11,4,NaN
13,2010,1,1,12,4,NaN
14,2010,1,1,13,4,NaN
15,2010,1,1,14,4,NaN
16,2010,1,1,15,4,NaN
17,2010,1,1,16,4,NaN
18,2010,1,1,17,4,NaN
19,2010,1,1,18,4,NaN
20,2010,1,1,19,4,NaN
21,2010,1,1,20,4,NaN
22,2010,1,1,21,4,NaN
23,2010,1,1,22,4,NaN
24,2010,1,1,23,4,NaN
25,2010,1,2,0,4,NaN
26,2010,1,2,1,4,NaN
27,2010,1,2,2,4,NaN
28,2010,1,2,3,4,NaN
29,2010,1,2,4,4,NaN
30,2010,1,2,5,4,NaN
31,2010,1,2,6,4,NaN
32,2010,1,2,7,4,NaN
33,2010,1,2,8,4,NaN
34,2010,1,2,9,4,NaN
35,2010,1,2,10,4,NaN
36,2010,1,2,11,4,NaN
37,2010,1,2,12,4,NaN
38,2010,1,2,13,4,NaN
39,2010,1,2,14,4,NaN
40,2010,1,2,15,4,NaN
41,2010,1,2,16,4,NaN
42,2010,1,2,17,4,NaN
43,2010,1,2,18,4,NaN
44,2010,1,2,19,4,NaN
45,2010,1,2,20,4,NaN
46,2010,1,2,21,4,NaN
47,2010,1,2,22,4,NaN
48,2010,1,2,23,4,NaN
49,2010,1,3,0,4,NaN
50,2010,1,3,1,4,NaN
51,2010,1,3,2,4,NaN
52,2010,1,3,3,4,NaN
53,2010,1,3,4,4,NaN
54,2010,1,3,5,4,NaN
55,2010,1,3,6,4,NaN
56,2010,1,3,7,4,NaN
57,2010,1,3,8,4,NaN
58,2010,1,3,9,4,NaN
59,2010,1,3,10,4,NaN
60,2010,1,3,11,4,NaN
61,2010,1,3,12,4,NaN
62,2010,1,3,13,4,NaN
63,2010,1,3,14,4,NaN
64,2010,1,3,15,4,NaN
65,2010,1,3,16,4,NaN
66,2010,1,3,17,4,NaN
67,2010,1,3,18,4,NaN
68,2010,1,3,19,4,NaN
69,2010,1,3,20,4,NaN
70,2010,1,3,21,4,NaN
71,2010,1,3,22,4,NaN
72,2010,1,3,23,4,NaN
73,2010,1,4,0,4,NaN
74,2010,1,4,1,4,NaN
75,2010,1,4,2,4,NaN
76,2010,1,4,3,4,NaN
77,2010,1,4,4,4,NaN
78,2010,1,4,5,4,NaN
79,2010,1,4,6,4,NaN
80,2010,1,4,7,4,NaN
81,2010,1,4,8,4,NaN
82,2010,1,4,9,4,NaN
83,2010,1,4,10,4,NaN
84,2010,1,4,11,4,NaN
85,2010,1,4,12,4,NaN
86,2010,1,4,13,4,NaN
87,2010,1,4,14,4,NaN
88,2010,1,4,15,4,NaN
89,2010,1,4,16,4,NaN
90,2010,1,4,17,4,NaN
91,2010,1,4,18,4,NaN
92,2010,1,4,19,4,NaN
93,2010,1,4,20,4,NaN
94,2010,1,4,21,4,NaN
95,2010,1,4,22,4,NaN
96,2010,1,4,23,4,NaN
97,2010,1,5,0,4,NaN
98,2010,1,5,1,4,NaN
99,2010,1,5,2,4,NaN
100,2010,1,5,3,4,NaN
101,2010,1,5,4,4,NaN
102,2010,1,5,5,4,NaN
103,2010,1,5,6,4,NaN
104,2010,1,5,7,4,NaN
105,2010,1,5,8,4,NaN
106,2010,1,5,9,4,NaN
107,2010,1,5,10,4,NaN
108,2010,1,5,11,4,NaN
109,2010,1,5,12,4,NaN
110,2010,1,5,13,4,NaN
111,2010,1,5,14,4,NaN
112,2010,1,5,15,4,NaN
113,2010,1,5,16,4,NaN
114,2010,1,5,17,4,NaN
115,2010,1,5,18,4,NaN
116,2010,1,5,19,4,NaN
117,2010,1,5,20,4,NaN
118,2010,1,5,21,4,NaN
119,2010,1,5,22,4,NaN
120,2010,1,5,23,4,NaN
121,2010,1,6,0,4,NaN
122,2010,1,6,1,4,NaN
123,2010,1,6,2,4,NaN
124,2010,1,6,3,4,NaN
125,2010,1,6,4,4,NaN
126,2010,1,6,5,4,NaN
127,2010,1,6,6,4,NaN
128,2010,1,6,7,4,NaN
129,2010,1,6,8,4,NaN
130,2010,1,6,9,4,NaN
131,2010,1,6,10,4,NaN
132,2010,1,6,11,4,NaN
133,2010,1,6,12,4,NaN
134,2010,1,6,13,4,NaN
135,2010,1,6,14,4,NaN
136,2010,1,6,15,4,NaN
137,2010,1,6,16,4,NaN
138,2010,1,6,17,4,NaN
139,2010,1,6,18,4,NaN
140,2010,1,6,19,4,NaN
141,2010,1,6,20,4,NaN
142,2010,1,6,21,4,NaN
143,2010,1,6,22,4,NaN
144,2010,1,6,23,4,NaN
145,2010,1,7,0,4,NaN
146,2010,1,7,1,4,NaN
147,2010,1,7,2,4,NaN
148,2010,1,7,3,4,NaN
149,2010,1,7,4,4,NaN
150,2010,1,7,5,4,NaN
151,2010,1,7,6,4,NaN
152,2010,1,7,7,4,NaN
153,2010,1,7,8,4,NaN
154,2010,1,7,9,4,NaN
155,2010,1,7,10,4,NaN
156,2010,1,7,11,4,NaN
157,2010,1,7,12,4,NaN
158,2010,1,7,13,4,NaN
159,2010,1,7,14,4,NaN
160,2010,1,7,15,4,NaN
161,2010,1,7,16,4,NaN
162,2010,1,7,17,4,NaN
163,2010,1,7,18,4,NaN
164,2010,1,7,19,4,NaN
165,2010,1,7,20,4,NaN
166,2010,1,7,21,4,NaN
167,2010,1,7,22,4,NaN
168,2010,1,7,23,4,NaN
169,2010,1,8,0,4,NaN
170,2010,1,8,1,4,NaN
171,2010,1,8,2,4,NaN
172,2010,1,8,3,4,NaN
173,2010,1,8,4,4,NaN
174,2010,1,8,5,4,NaN
175,2010,1,8,6,4,NaN
176,2010,1,8,7,4,NaN
177,2010,1,8,8,4,NaN
178,2010,1,8,9,4,NaN
179,2010,1,8,10,4,NaN
180,2010,1,8,11,4,NaN
181,2010,1,8,12,4,NaN
182,2010,1,8,13,4,NaN
183,2010,1,8,14,4,NaN
184,2010,1,8,15,4,NaN
185,2010,1,8,16,4,NaN
186,2010,1,8,17,4,NaN
187,2010,1,8,18,4,NaN
188,2010,1,8,19,4,NaN
189,2010,1,8,20,4,NaN
190,2010,1,8,21,4,NaN
191,2010,1,8,22,4,NaN
192,2010,1,8,23,4,NaN
193,2010,1,9,0,4,NaN
194,2010,1,9,1,4,NaN
195,2010,1,9,2,4,NaN
196,2010,1,9,3,4,NaN
197,2010,1,9,4,4,NaN
198,2010,1,9,5,4,NaN
199,2010,1,9,6,4,NaN
```

（BeijingPM20100101_20151231.csv）

```
No,year,month,day,hour,season,PM_Dongsi,PM_Dongsihuan,PM_Nongzhanguan,PM_US_Post,DEWP,HUMI,PRES,TEMP,cbwd,Iws,precipitation,Iprec
1,2010,1,1,0,4,NA,NA,NA,NA,-21,43,1021,-11,NW,1.79,0,0
2,2010,1,1,1,4,NA,NA,NA,NA,-21,47,1020,-12,NW,4.92,0,0
3,2010,1,1,2,4,NA,NA,NA,NA,-21,43,1019,-11,NW,6.71,0,0
4,2010,1,1,3,4,NA,NA,NA,NA,-21,55,1019,-14,NW,9.84,0,0
5,2010,1,1,4,4,NA,NA,NA,NA,-20,51,1018,-12,NW,12.97,0,0
6,2010,1,1,5,4,NA,NA,NA,NA,-19,47,1017,-10,NW,16.1,0,0
7,2010,1,1,6,4,NA,NA,NA,NA,-19,44,1017,-9,NW,19.23,0,0
8,2010,1,1,7,4,NA,NA,NA,NA,-19,44,1017,-9,NW,21.02,0,0
9,2010,1,1,8,4,NA,NA,NA,NA,-19,44,1017,-9,NW,24.15,0,0
10,2010,1,1,9,4,NA,NA,NA,NA,-20,37,1017,-8,NW,27.28,0,0
11,2010,1,1,10,4,NA,NA,NA,NA,-19,37,1017,-7,NW,31.3,0,0
12,2010,1,1,11,4,NA,NA,NA,NA,-18,35,1017,-5,NW,34.43,0,0
13,2010,1,1,12,4,NA,NA,NA,NA,-19,32,1015,-5,NW,37.56,0,0
14,2010,1,1,13,4,NA,NA,NA,NA,-18,30,1015,-3,NW,40.69,0,0
15,2010,1,1,14,4,NA,NA,NA,NA,-18,28,1014,-2,NW,43.82,0,0
16,2010,1,1,15,4,NA,NA,NA,NA,-18,26,1014,-1,cv,0.89,0,0
17,2010,1,1,16,4,NA,NA,NA,NA,-19,25,1015,-2,NW,1.79,0,0
18,2010,1,1,17,4,NA,NA,NA,NA,-18,30,1015,-3,NW,2.68,0,0
19,2010,1,1,18,4,NA,NA,NA,NA,-18,35,1016,-5,NE,1.79,0,0
20,2010,1,1,19,4,NA,NA,NA,NA,-17,35,1017,-4,NW,1.79,0,0
21,2010,1,1,20,4,NA,NA,NA,NA,-17,38,1017,-5,cv,0.89,0,0
22,2010,1,1,21,4,NA,NA,NA,NA,-17,38,1018,-5,NW,1.79,0,0
23,2010,1,1,22,4,NA,NA,NA,NA,-17,38,1018,-5,NW,2.68,0,0
24,2010,1,1,23,4,NA,NA,NA,129,-17,41,1020,-5,cv,0.89,0,0
25,2010,1,2,0,4,NA,NA,NA,148,-16,38,1020,-4,SE,1.79,0,0
26,2010,1,2,1,4,NA,NA,NA,159,-15,42,1020,-4,SE,2.68,0,0
27,2010,1,2,2,4,NA,NA,NA,181,-11,63.5,1021,-5,SE,3.57,0,0
28,2010,1,2,3,4,NA,NA,NA,138,-7,85,1022,-5,SE,5.36,0,0
29,2010,1,2,4,4,NA,NA,NA,109,-7,85,1022,-5,SE,6.25,0,0
30,2010,1,2,5,4,NA,NA,NA,105,-7,92,1022,-6,SE,7.14,0,0
31,2010,1,2,6,4,NA,NA,NA,124,-7,92,1023,-6,SE,8.93,0,0
32,2010,1,2,7,4,NA,NA,NA,120,-7,85,1024,-5,SE,10.72,0,0
33,2010,1,2,8,4,NA,NA,NA,132,-8,85,1024,-6,SE,12.51,0,0
34,2010,1,2,9,4,NA,NA,NA,140,-7,85,1025,-5,SE,14.3,0,0
35,2010,1,2,10,4,NA,NA,NA,152,-7,85,1026,-5,SE,17.43,0,0
36,2010,1,2,11,4,NA,NA,NA,148,-8,79,1026,-5,SE,20.56,0,0
37,2010,1,2,12,4,NA,NA,NA,164,-8,79,1026,-5,SE,23.69,0,0
38,2010,1,2,13,4,NA,NA,NA,158,-8,79,1025,-5,SE,27.71,0,0
39,2010,1,2,14,4,NA,NA,NA,154,-9,73,1025,-5,SE,31.73,0,0
40,2010,1,2,15,4,NA,NA,NA,159,-9,73,1025,-5,SE,35.75,0,0
41,2010,1,2,16,4,NA,NA,NA,164,-9,73,1026,-5,SE,37.54,0,0
42,2010,1,2,17,4,NA,NA,NA,170,-8,79,1027,-5,SE,39.33,0,0
43,2010,1,2,18,4,NA,NA,NA,149,-8,79,1027,-5,SE,42.46,0,0
44,2010,1,2,19,4,NA,NA,NA,154,-8,79,1028,-5,SE,44.25,0,0
45,2010,1,2,20,4,NA,NA,NA,164,-7,85,1028,-5,SE,46.04,0,0
46,2010,1,2,21,4,NA,NA,NA,156,-7,85,1027,-5,SE,49.17,0,0
47,2010,1,2,22,4,NA,NA,NA,126,-8,85,1028,-6,SE,52.3,0,0
48,2010,1,2,23,4,NA,NA,NA,90,-8,85,1027,-6,SE,55.43,0,0
49,2010,1,3,0,4,NA,NA,NA,63,-7,92,1027,-6,SE,58.56,0.4,0.4
50,2010,1,3,1,4,NA,NA,NA,65,-8,85,1026,-6,SE,61.69,0.5,0.9
51,2010,1,3,2,4,NA,NA,NA,55,-8,92,1026,-7,SE,65.71,0.5,1.4
52,2010,1,3,3,4,NA,NA,NA,65,-8,92,1025,-7,SE,68.84,0.7,2.1
53,2010,1,3,4,4,NA,NA,NA,83,-8,92,1024,-7,SE,72.86,1.2,3.3
54,2010,1,3,5,4,NA,NA,NA,91,-9,92,1024,-8,SE,76.88,0.7,4
55,2010,1,3,6,4,NA,NA,NA,86,-10,85,1024,-8,SE,80.9,1,5
56,2010,1,3,7,4,NA,NA,NA,82,-10,92,1024,-9,SE,84.92,0.7,5.7
57,2010,1,3,8,4,NA,NA,NA,86,-10,92,1024,-9,SE,89.84,0.5,6.2
58,2010,1,3,9,4,NA,NA,NA,78,-11,85,1023,-9,SE,93.86,0.7,6.9
59,2010,1,3,10,4,NA,NA,NA,98,-11,85,1023,-9,SE,97.88,0.4,7.3
60,2010,1,3,11,4,NA,NA,NA,107,-11,85,1022,-9,SE,102.8,0.5,7.8
61,2010,1,3,12,4,NA,NA,NA,90,-11,85,1021,-9,SE,105.93,1.1,8.9
62,2010,1,3,13,4,NA,NA,NA,96,-11,85,1020,-9,SE,111.74,0.5,9.4
63,2010,1,3,14,4,NA,NA,NA,95,-11,85,1020,-9,SE,116.66,0.3,9.7
64,2010,1,3,15,4,NA,NA,NA,86,-11,85,1020,-9,SE,121.58,0.5,10.2
65,2010,1,3,16,4,NA,NA,NA,70,-11,85,1020,-9,SE,124.71,0.2,10.4
66,2010,1,3,17,4,NA,NA,NA,61,-11,85,1020,-9,SE,127.84,0.1,10.5
67,2010,1,3,18,4,NA,NA,NA,53,-11,85,1021,-9,cv,0.89,0.4,10.9
68,2010,1,3,19,4,NA,NA,NA,71,-11,85,1022,-9,cv,1.78,0.3,11.2
69,2010,1,3,20,4,NA,NA,NA,72,-10,92,1022,-9,NW,4.02,0,0
70,2010,1,3,21,4,NA,NA,NA,76,-11,92,1023,-10,NW,7.15,0,0
71,2010,1,3,22,4,NA,NA,NA,73,-11,85,1023,-9,NW,11.17,0,0
72,2010,1,3,23,4,NA,NA,NA,79,-12,92,1023,-11,NW,14.3,0,0
73,2010,1,4,0,4,NA,NA,NA,58,-14,85,1023,-12,NW,16.09,0,0
74,2010,1,4,1,4,NA,NA,NA,25,-16,56,1023,-9,NW,21.9,0,0
75,2010,1,4,2,4,NA,NA,NA,26,-17,56,1024,-10,NW,29.95,0,0
76,2010,1,4,3,4,NA,NA,NA,28,-18,56,1024,-11,NW,39.78,0,0
77,2010,1,4,4,4,NA,NA,NA,26,-19,51,1025,-11,NW,48.72,0,0
78,2010,1,4,5,4,NA,NA,NA,20,-20,51,1026,-12,NW,55.87,0,0
79,2010,1,4,6,4,NA,NA,NA,29,-21,47,1027,-12,NW,64.81,0,0
80,2010,1,4,7,4,NA,NA,NA,26,-21,51,1027,-13,NW,73.75,0,0
81,2010,1,4,8,4,NA,NA,NA,27,-22,46,1028,-13,NW,80.9,0,0
82,2010,1,4,9,4,NA,NA,NA,27,-22,46,1029,-13,NW,90.73,0,0
83,2010,1,4,10,4,NA,NA,NA,25,-22,43,1030,-12,NW,100.56,0,0
84,2010,1,4,11,4,NA,NA,NA,29,-23,39,1031,-12,NW,108.61,0,0
85,2010,1,4,12,4,NA,NA,NA,32,-21,43,1030,-11,NW,117.55,0,0
86,2010,1,4,13,4,NA,NA,NA,28,-20,43,1030,-10,NW,127.38,0,0
87,2010,1,4,14,4,NA,NA,NA,29,-21,40,1030,-10,NW,136.32,0,0
88,2010,1,4,15,4,NA,NA,NA,30,-21,37,1030,-9,NW,145.26,0,0
89,2010,1,4,16,4,NA,NA,NA,30,-21,37,1031,-9,NW,152.41,0,0
90,2010,1,4,17,4,NA,NA,NA,28,-20,47,1032,-11,NW,159.56,0,0
91,2010,1,4,18,4,NA,NA,NA,26,-23,36,1032,-11,NW,165.37,0,0
92,2010,1,4,19,4,NA,NA,NA,31,-21,47,1033,-12,NW,171.18,0,0
93,2010,1,4,20,4,NA,NA,NA,33,-24,36,1034,-12,NW,180.12,0,0
94,2010,1,4,21,4,NA,NA,NA,29,-24,39,1034,-13,NW,187.27,0,0
95,2010,1,4,22,4,NA,NA,NA,31,-24,39,1035,-13,NW,195.32,0,0
96,2010,1,4,23,4,NA,NA,NA,30,-26,38,1035,-15,NW,198.45,0,0
97,2010,1,5,0,4,NA,NA,NA,34,-26,45,1035,-17,NW,201.58,0,0
98,2010,1,5,1,4,NA,NA,NA,27,-26,49,1035,-18,NW,205.6,0,0
99,2010,1,5,2,4,NA,NA,NA,25,-26,53,1035,-19,NW,208.73,0,0
100,2010,1,5,3,4,NA,NA,NA,28,-27,45,1035,-18,NW,213.65,0,0
101,2010,1,5,4,4,NA,NA,NA,28,-27,49,1035,-19,NW,218.57,0,0
102,2010,1,5,5,4,NA,NA,NA,27,-27,38,1034,-16,NE,4.92,0,0
103,2010,1,5,6,4,NA,NA,NA,27,-26,41,1035,-16,NE,8.05,0,0
104,2010,1,5,7,4,NA,NA,NA,27,-27,38,1034,-16,NE,13.86,0,0
105,2010,1,5,8,4,NA,NA,NA,29,-26,41,1035,-16,NE,18.78,0,0
106,2010,1,5,9,4,NA,NA,NA,36,-26,38,1035,-15,NE,24.59,0,0
107,2010,1,5,10,4,NA,NA,NA,30,-25,38,1035,-14,NE,29.51,0,0
108,2010,1,5,11,4,NA,NA,NA,27,-25,35,1035,-13,NE,34.43,0,0
109,2010,1,5,12,4,NA,NA,NA,39,-25,33,1034,-12,NE,39.35,0,0
110,2010,1,5,13,4,NA,NA,NA,41,-24,33,1032,-11,NE,41.14,0,0
111,2010,1,5,14,4,NA,NA,NA,33,-22,39,1032,-11,cv,0.89,0,0
112,2010,1,5,15,4,NA,NA,NA,50,-23,36,1031,-11,NW,1.79,0,0
113,2010,1,5,16,4,NA,NA,NA,56,-24,33,1031,-11,NW,3.58,0,0
114,2010,1,5,17,4,NA,NA,NA,59,-23,36,1031,-11,NW,5.37,0,0
115,2010,1,5,18,4,NA,NA,NA,60,-23,36,1032,-11,NW,7.16,0,0
116,2010,1,5,19,4,NA,NA,NA,84,-22,46,1033,-13,NW,10.29,0,0
117,2010,1,5,20,4,NA,NA,NA,106,-22,43,1033,-12,NW,13.42,0,0
118,2010,1,5,21,4,NA,NA,NA,66,-24,59,1033,-18,NW,16.55,0,0
119,2010,1,5,22,4,NA,NA,NA,50,-22,46,1034,-13,NW,20.57,0,0
120,2010,1,5,23,4,NA,NA,NA,56,-22,59,1033,-16,NW,23.7,0,0
121,2010,1,6,0,4,NA,NA,NA,77,-25,49,1033,-17,NW,26.83,0,0
122,2010,1,6,1,4,NA,NA,NA,50,-25,38,1033,-14,NE,4.02,0,0
123,2010,1,6,2,4,NA,NA,NA,44,-26,35,1034,-14,NE,8.04,0,0
124,2010,1,6,3,4,NA,NA,NA,27,-26,35,1033,-14,NE,13.85,0,0
125,2010,1,6,4,4,NA,NA,NA,28,-26,35,1033,-14,NE,17.87,0,0
126,2010,1,6,5,4,NA,NA,NA,21,-26,35,1033,-14,NE,23.68,0,0
127,2010,1,6,6,4,NA,NA,NA,25,-26,35,1033,-14,NE,28.6,0,0
128,2010,1,6,7,4,NA,NA,NA,20,-26,38,1034,-15,NE,33.52,0,0
129,2010,1,6,8,4,NA,NA,NA,29,-26,35,1034,-14,NE,39.33,0,0
130,2010,1,6,9,4,NA,NA,NA,34,-25,35,1035,-13,NE,44.25,0,0
131,2010,1,6,10,4,NA,NA,NA,42,-25,33,1035,-12,NE,50.06,0,0
132,2010,1,6,11,4,NA,NA,NA,28,-24,33,1035,-11,NE,54.98,0,0
133,2010,1,6,12,4,NA,NA,NA,36,-24,30,1034,-10,NE,59,0,0
134,2010,1,6,13,4,NA,NA,NA,48,-22,36,1033,-10,NW,4.02,0,0
135,2010,1,6,14,4,NA,NA,NA,49,-22,34,1033,-9,NW,5.81,0,0
136,2010,1,6,15,4,NA,NA,NA,52,-22,31,1033,-8,cv,0.89,0,0
137,2010,1,6,16,4,NA,NA,NA,56,-22,31,1033,-8,NW,1.79,0,0
138,2010,1,6,17,4,NA,NA,NA,96,-21,37,1033,-9,NW,3.58,0,0
139,2010,1,6,18,4,NA,NA,NA,75,-22,34,1033,-9,NW,5.37,0,0
140,2010,1,6,19,4,NA,NA,NA,105,-22,50,1034,-14,cv,0.89,0,0
141,2010,1,6,20,4,NA,NA,NA,132,-22,43,1035,-12,NW,1.79,0,0
142,2010,1,6,21,4,NA,NA,NA,93,-21,55,1034,-14,NW,3.58,0,0
143,2010,1,6,22,4,NA,NA,NA,131,-22,59,1035,-16,NW,5.37,0,0
144,2010,1,6,23,4,NA,NA,NA,127,-21,65,1035,-16,NW,7.16,0,0
145,2010,1,7,0,4,NA,NA,NA,130,-21,65,1035,-16,NW,8.95,0,0
146,2010,1,7,1,4,NA,NA,NA,43,-21,65,1035,-16,cv,0.45,0,0
147,2010,1,7,2,4,NA,NA,NA,37,-22,70,1036,-18,cv,1.34,0,0
148,2010,1,7,3,4,NA,NA,NA,30,-23,50,1036,-15,NW,4.02,0,0
149,2010,1,7,4,4,NA,NA,NA,28,-24,50,1035,-16,NW,7.15,0,0
150,2010,1,7,5,4,NA,NA,NA,24,-25,42,1035,-15,NW,10.28,0,0
151,2010,1,7,6,4,NA,NA,NA,23,-24,46,1035,-15,NW,14.3,0,0
152,2010,1,7,7,4,NA,NA,NA,24,-25,35,1036,-13,NE,4.92,0,0
153,2010,1,7,8,4,NA,NA,NA,27,-24,42,1036,-14,NW,4.02,0,0
154,2010,1,7,9,4,NA,NA,NA,40,-23,39,1036,-12,NW,8.94,0,0
155,2010,1,7,10,4,NA,NA,NA,42,-22,39,1036,-11,NW,12.96,0,0
156,2010,1,7,11,4,NA,NA,NA,42,-20,43,1036,-10,NW,16.09,0,0
157,2010,1,7,12,4,NA,NA,NA,55,-21,37,1035,-9,NW,19.22,0,0
158,2010,1,7,13,4,NA,NA,NA,52,-21,34,1034,-8,NW,21.01,0,0
159,2010,1,7,14,4,NA,NA,NA,51,-20,34,1033,-7,NW,22.8,0,0
160,2010,1,7,15,4,NA,NA,NA,57,-20,34,1032,-7,NW,24.59,0,0
161,2010,1,7,16,4,NA,NA,NA,50,-18,44,1032,-8,NW,26.38,0,0
162,2010,1,7,17,4,NA,NA,NA,54,-19,44,1032,-9,NW,28.17,0,0
163,2010,1,7,18,4,NA,NA,NA,67,-19,51,1032,-11,NE,0.89,0,0
164,2010,1,7,19,4,NA,NA,NA,106,-18,56,1033,-11,NW,1.79,0,0
165,2010,1,7,20,4,NA,NA,NA,159,-19,71,1032,-15,NE,1.79,0,0
166,2010,1,7,21,4,NA,NA,NA,198,-19,65,1032,-14,cv,0.45,0,0
167,2010,1,7,22,4,NA,NA,NA,190,-21,55,1032,-14,cv,1.34,0,0
168,2010,1,7,23,4,NA,NA,NA,210,-21,65,1032,-16,cv,2.23,0,0
169,2010,1,8,0,4,NA,NA,NA,195,-21,71,1031,-17,NW,1.79,0,0
170,2010,1,8,1,4,NA,NA,NA,275,-19,77,1031,-16,NW,3.58,0,0
171,2010,1,8,2,4,NA,NA,NA,164,-20,71,1031,-16,NE,0.89,0,0
172,2010,1,8,3,4,NA,NA,NA,110,-19,71,1030,-15,SE,0.89,0,0
173,2010,1,8,4,4,NA,NA,NA,100,-18,77,1030,-15,cv,0.45,0,0
174,2010,1,8,5,4,NA,NA,NA,81,-18,77,1029,-15,cv,1.34,0,0
175,2010,1,8,6,4,NA,NA,NA,71,-18,77,1029,-15,NW,1.79,0,0
176,2010,1,8,7,4,NA,NA,NA,66,-16,78,1029,-13,NE,0.89,0,0
177,2010,1,8,8,4,NA,NA,NA,92,-16,72,1029,-12,SE,0.89,0,0
178,2010,1,8,9,4,NA,NA,NA,135,-16,72,1030,-12,SE,1.78,0,0
179,2010,1,8,10,4,NA,NA,NA,155,-17,56,1030,-10,NE,0.89,0,0
180,2010,1,8,11,4,NA,NA,NA,198,-16,61,1029,-10,cv,0.89,0,0
181,2010,1,8,12,4,NA,NA,NA,250,-16,56,1028,-9,SE,3.13,0,0
182,2010,1,8,13,4,NA,NA,NA,200,-15,57,1026,-8,SE,4.92,0,0
183,2010,1,8,14,4,NA,NA,NA,231,-16,52,1026,-8,cv,0.89,0,0
184,2010,1,8,15,4,NA,NA,NA,250,-16,52,1025,-8,cv,1.78,0,0
185,2010,1,8,16,4,NA,NA,NA,212,-16,52,1025,-8,cv,2.67,0,0
186,2010,1,8,17,4,NA,NA,NA,219,-17,48,1025,-8,SE,0.89,0,0
187,2010,1,8,18,4,NA,NA,NA,227,-17,52,1026,-9,cv,0.45,0,0
188,2010,1,8,19,4,NA,NA,NA,226,-17,61,1026,-11,cv,1.34,0,0
189,2010,1,8,20,4,NA,NA,NA,225,-17,66,1026,-12,cv,1.79,0,0
190,2010,1,8,21,4,NA,NA,NA,168,-18,71,1027,-14,NW,1.79,0,0
191,2010,1,8,22,4,NA,NA,NA,169,-16,56,1027,-9,NW,4.92,0,0
192,2010,1,8,23,4,NA,NA,NA,165,-16,66,1027,-11,NW,6.71,0,0
193,2010,1,9,0,4,NA,NA,NA,159,-17,71,1027,-13,cv,0.89,0,0
194,2010,1,9,1,4,NA,NA,NA,167,-17,71,1027,-13,NW,3.13,0,0
195,2010,1,9,2,4,NA,NA,NA,196,-17,77,1027,-14,NW,4.92,0,0
196,2010,1,9,3,4,NA,NA,NA,169,-17,84,1027,-15,NW,8.05,0,0
197,2010,1,9,4,4,NA,NA,NA,155,-17,71,1027,-13,NW,9.84,0,0
198,2010,1,9,5,4,NA,NA,NA,119,-19,77,1027,-16,NW,13.86,0,0
199,2010,1,9,6,4,NA,NA,NA,106,-18,77,1027,-15,NW,17.88,0,0
```

**代码：**

```scala
package com.spark.transformation

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types._
import org.junit.Test

class NullProcessor {

Logger.getLogger("org").setLevel(Level.ERROR)
  val spark: SparkSession = SparkSession.builder()
    .master("local[6]")
    .appName(this.getClass.getSimpleName)
    .getOrCreate()

@Test
  def nullAndNaN(): Unit = {

// 2. 导入数据集

// 3. 读取数据集
    //    1. 通过Saprk-csv自动的推断类型来读取, 推断数字的时候会将 NaN 推断为 字符串
    //    spark.read
    //      .option("header", true)
    //      .option("inferSchema", true)
    //      .csv(...)
    //    2. 直接读取字符串, 在后续的操作中使用 map 算子转类型
    //    spark.read.csv().map( row => row... )
    //    3. 指定 Schema, 不要自动推断
    val schema = StructType(
      List(
        StructField("id", LongType),
        StructField("year", IntegerType),
        StructField("month", IntegerType),
        StructField("day", IntegerType),
        StructField("hour", IntegerType),
        StructField("season", IntegerType),
        StructField("pm", DoubleType)
      )
    )

val sourceDF = spark.read
      .option("header", value = true)
      .schema(schema)
      .csv("E:\\Project\\Spark\\spark-sql\\input\\beijingpm_with_nan.csv")

sourceDF.show()

// 4. 丢弃
    // 2019, 12, 12, NaN
    // 规则:
    //      1. any, 只有有一个 NaN 就丢弃
    sourceDF.na.drop("any").show()
    sourceDF.na.drop().show()
    //      2. all, 所有数据都是 NaN 的行才丢弃
    sourceDF.na.drop("all").show()
    //      3. 某些列的规则
    sourceDF.na.drop("any", List("year", "month", "day", "hour")).show()

// 5. 填充
    // 规则:
    //     1. 针对所有列数据进行默认值填充
    sourceDF.na.fill(0).show()
    //     2. 针对特定列填充
    sourceDF.na.fill(0, List("year", "month")).show()
  }

@Test
  def strProcessor(): Unit = {
    // 读取数据集
    val sourceDF = spark.read
      .option("header", value = true)
      .option("inferSchema", value = true)
      .csv("E:\\Project\\Spark\\spark-sql\\input\\BeijingPM20100101_20151231.csv")

//    sourceDF.show()

// 1. 丢弃
    import spark.implicits._
    //    sourceDF.where('PM_Dongsi =!= "NA").show()

// 2. 替换
    import org.apache.spark.sql.functions._
    // select name, age, case
    // when ... then ...
    // when ... then ...
    // else
    sourceDF.select(
      'No as "id", 'year, 'month, 'day, 'hour, 'season,
      when('PM_Dongsi === "NA", Double.NaN)
        .otherwise('PM_Dongsi cast DoubleType)
        .as("pm")
    ).show()

// 原类型和转换过后的类型, 必须一致
    sourceDF.na.replace("PM_Dongsi", Map("NA" -> "NaN", "NULL" -> "null")).show()
  }

}
```

*附：原文链接地址*

https://blog.csdn.net/weixin_45417821/article/details/108472687

标签: Spark, Spark SQL

非特殊说明，本博所有文章均为博主原创。

如若转载，请注明出处：https://lilinchao.com/archives/1345.html

上一篇 05.【转载】Dataset (DataFrame) 的基础操作（一）

下一篇 07.RDD、DataFrame和DataSet对比与转换

06.【转载】Dataset (DataFrame) 的基础操作（二）

评论已关闭

栏目分类

标签云

友情链接申请

06.【转载】Dataset (DataFrame) 的基础操作（二）

评论已关闭

 栏目分类

标签云

友情链接申请

栏目分类

标签云

友情链接申请