Spark的Dataset操作(四)-其他单表操作

还有些杂七杂八的小用法没有提到，比如添加列，删除列，NA值处理之类的，就在这里大概列一下吧。

数据集还是之前的那个吧：

scala> val df = spark.createDataset(Seq(  ("aaa",1,2),("bbb",3,4),("ccc",3,5),("bbb",4, 6))   ).toDF("key1","key2","key3")df: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]scala> df.printSchemaroot |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false)scala> df.show+----+----+----+|key1|key2|key3|+----+----+----+| aaa|   1|   2|| bbb|   3|   4|| ccc|   3|   5|| bbb|   4|   6|+----+----+----+

下面来添加一列，可以是字符串类型，整型；可以是常量或者是对当前已有的某列的变换，都行：

/* 新增字符串类型的列key_4，都初始化为new_str_col，注意这里的lit()函数 */scala> val df_1 = df.withColumn("key4", lit("new_str_col"))df_1: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 2 more fields]scala> df_1.printSchemaroot |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false)scala> df_1.show+----+----+----+-----------+|key1|key2|key3|       key4|+----+----+----+-----------+| aaa|   1|   2|new_str_col|| bbb|   3|   4|new_str_col|| ccc|   3|   5|new_str_col|| bbb|   4|   6|new_str_col|+----+----+----+-----------+/* 同样的，新增Int类型的列key5，都初始化为1024 */scala> val df_2 = df_1.withColumn("key5", lit(1024))df_2: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 3 more fields]scala> df_2.printSchemaroot |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key5: integer (nullable = false)scala> df_2.show+----+----+----+-----------+-----+|key1|key2|key3|       key4|key5|+----+----+----+-----------+-----+| aaa|   1|   2|new_str_col| 1024|| bbb|   3|   4|new_str_col| 1024|| ccc|   3|   5|new_str_col| 1024|| bbb|   4|   6|new_str_col| 1024|+----+----+----+-----------+-----+/*再来个不是常量的新增列key6 = key5 * 2*/scala> val df_3 = df_2.withColumn("key6", $"key5"*2)df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]scala> df_3.show+----+----+----+-----------+----+----+|key1|key2|key3|       key4|key5|key6|+----+----+----+-----------+----+----+| aaa|   1|   2|new_str_col|1024|2048|| bbb|   3|   4|new_str_col|1024|2048|| ccc|   3|   5|new_str_col|1024|2048|| bbb|   4|   6|new_str_col|1024|2048|+----+----+----+-----------+----+----+/*这次是用的expr()函数*/scala> val df_4 = df_2.withColumn("key6", expr("key5 * 4"))df_4: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields]scala> df_4.show+----+----+----+-----------+----+----+|key1|key2|key3|       key4|key5|key6|+----+----+----+-----------+----+----+| aaa|   1|   2|new_str_col|1024|4096|| bbb|   3|   4|new_str_col|1024|4096|| ccc|   3|   5|new_str_col|1024|4096|| bbb|   4|   6|new_str_col|1024|4096|+----+----+----+-----------+----+----+

删除列就比较简单了，指定列名就好了

/*删除列key5*/scala> val df_5 = df_4.drop("key5")df_5: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 3 more fields]scala> df_4.printSchemaroot |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key5: integer (nullable = false) |-- key6: integer (nullable = false)scala> df_5.printSchemaroot |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key6: integer (nullable = false)scala> df_5.show+----+----+----+-----------+----+|key1|key2|key3|       key4|key6|+----+----+----+-----------+----+| aaa|   1|   2|new_str_col|4096|| bbb|   3|   4|new_str_col|4096|| ccc|   3|   5|new_str_col|4096|| bbb|   4|   6|new_str_col|4096|+----+----+----+-----------+----+/*可以一次删除多列key4和key6*/scala> val df_6 = df_5.drop("key4", "key6")df_6: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]/* 这里的columns函数以数组形式返回所有列名 */scala> df_6.columnsres23: Array[String] = Array(key1, key2, key3)scala> df_6.show+----+----+----+|key1|key2|key3|+----+----+----+| aaa|   1|   2|| bbb|   3|   4|| ccc|   3|   5|| bbb|   4|   6|+----+----+----+

再写几个null值等无效数据的一些处理吧
这次得换个数据集，null值的表用个csv文件导入，代码如下：

/*csv文件内容如下：key1,key2,key3,key4,key5aaa,1,2,t1,4bbb,5,3,t2,8ccc,2,2,,7,7,3,t1,bbb,1,5,t3,0,4,,t1,8 */scala> val df = spark.read.option("header","true").csv("natest.csv")df: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7||null|   7|   3|  t1|null|| bbb|   1|   5|  t3|   0|| null|   4|null|  t1|   8|+----+----+----+----+----+/*把key1列中所有的null值替换成'xxx' */scala> val df_2 = df.na.fill("xxx",Seq("key1"))df_2: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df_2.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7|| xxx|   7|   3|  t1|null|| bbb|   1|   5|  t3|   0|| xxx|   4|null|  t1|   8|+----+----+----+----+----+/*一次修改相同类型的多个列的示例。这里是把key3,key5列中所有的null值替换成1024。csv导入时默认是string，如果是整型，写法是一样的，有各个类型的重载。*/scala> val df_3 = df.na.fill("1024",Seq("key3","key5"))df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df_3.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7||null|   7|   3|  t1|1024|| bbb|   1|   5|  t3|   0||null|   4|1024|  t1|   8|+----+----+----+----+----+/*一次修改不同类型的多个列的示例。csv导入时默认是string，如果是整型，写法是一样的，有各个类型的重载。*/scala> val df_3 = df.na.fill(Map(("key1"->"yyy"),("key3","1024"),("key4","t88"),("key5","4096")))df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df_3.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2| t88|   7|| yyy|   7|   3|  t1|4096|| bbb|   1|   5|  t3|   0|| yyy|   4|1024|  t1|   8|+----+----+----+----+----+/*不修改，只是过滤掉含有null值的行。这里是过滤掉key3,key5列中含有null的行*/scala> val df_4 = df.na.drop(Seq("key3","key5"))df_4: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df_4.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7|| bbb|   1|   5|  t3|   0|+----+----+----+----+----+/*过滤掉指定的若干列中，有效值少于n列的行这里是过滤掉key1,key2,key3这3列中有效值小于2列的行。最后一行中，这3列有2列都是null，所以被过滤掉了。*/scala> val df_5 = df.na.drop(2,Seq("key1","key2","key3"))df_5: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7||null|   7|   3|  t1|null|| bbb|   1|   5|  t3|   0||null|   4|null|  t1|   8|+----+----+----+----+----+scala> df_5.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7||null|   7|   3|  t1|null|| bbb|   1|   5|  t3|   0|+----+----+----+----+----+/*同上，如果不指定列名列表，则默认列名列表就是所有列*/scala> val df_6 = df.na.drop(4)df_6: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]scala> df_6.show+----+----+----+----+----+|key1|key2|key3|key4|key5|+----+----+----+----+----+| aaa|   1|   2|  t1|   4|| bbb|   5|   3|  t2|   8|| ccc|   2|   2|null|   7|| bbb|   1|   5|  t3|   0|+----+----+----+----+----+

ok，就到这吧，下次再写多表的部分了~~