创建具有空/空字段值的新数据框 [英] Create new Dataframe with empty/null field values

问题描述

我正在从现有数据帧创建一个新数据帧，但需要在此新 DF 中添加新列(下面代码中的field1").我该怎么做?工作示例代码示例将不胜感激.

I am creating a new Dataframe from an existing dataframe, but need to add new column ("field1" in below code) in this new DF. How do I do so? Working sample code example will be appreciated.

val edwDf = omniDataFrame 
  .withColumn("field1", callUDF((value: String) => None)) 
  .withColumn("field2",
    callUdf("devicetypeUDF", (omniDataFrame.col("some_field_in_old_df")))) 

edwDf
  .select("field1", "field2")
  .save("odsoutdatafldr", "com.databricks.spark.csv"); 

推荐答案

可以使用lit(null):

import org.apache.spark.sql.functions.{lit, udf}

case class Record(foo: Int, bar: String)
val df = Seq(Record(1, "foo"), Record(2, "bar")).toDF

val dfWithFoobar = df.withColumn("foobar", lit(null: String))

这里的一个问题是列类型是null:

One problem here is that the column type is null:

scala> dfWithFoobar.printSchema
root
 |-- foo: integer (nullable = false)
 |-- bar: string (nullable = true)
 |-- foobar: null (nullable = true)

并且它不会被 csv 编写器保留.如果这是一个硬性要求，您可以使用 DataType

and it is not retained by the csv writer. If it is a hard requirement you can cast column to the specific type (lets say String), with either DataType

import org.apache.spark.sql.types.StringType

df.withColumn("foobar", lit(null).cast(StringType))

或字符串描述

df.withColumn("foobar", lit(null).cast("string"))

或者像这样使用 UDF:

or use an UDF like this:

val getNull = udf(() => None: Option[String]) // Or some other type

df.withColumn("foobar", getNull()).printSchema
root
 |-- foo: integer (nullable = false)
 |-- bar: string (nullable = true)
 |-- foobar: string (nullable = true)

可在此处找到等效的 Python:添加空列以触发 DataFrame

A Python equivalent can be found here: Add an empty column to spark DataFrame

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