November 2021
Intermediate to advanced
340 pages
10h 46m
Chinese
Content preview from Spark快速大数据分析（第2版）
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134
｜
第
5
章
5.5.4
修改
另一种常用操作是对
DataFrame
进行修改。虽然
DataFrame
本身是不可变的，但我们依然
可以通过新创建另一个
DataFrame
来实现修改
，比如新
DataFrame
的列与原有
DataFrame
有
所不同。（前面的章节介绍过，底层的
RDD
是不可变的
，也就是说无法修改
RDD
，这是为
了确保
Spark
操作可以通过数据血缘恢复。
）我们从前面示例中的小型
DataFrame
开始。
// Scala代码或Python代码
foo.show()
+--------+-----+--------+------+-----------+
| date|delay|distance|origin|destination|
+--------+-----+--------+------+-----------+
|01010710| 31| 590|
SEA
|
SFO
|
|01010955| 104| 590|
SEA
|
SFO
|
|01010730| 5| 590|
SEA
|
SFO
|
+--------+-----+--------+------+-----------+
1.
添加新列
要想给
foo
这个
DataFrame
添加新列，应该使用
withColumn()
方法。
// Scala代码
import org.apache.spark.sql.functions.expr
val
foo2 = foo.withColumn(
"status",
expr("CASE ...
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