在 Apache Spark 中，我有一个数据框，其中有一列包含字符串（它的日期），但月份和日期中缺少前导零

Posted 2023-03-28

【中文标题】在 Apache Spark 中，我有一个数据框，其中有一列包含字符串（它的日期），但月份和日期中缺少前导零

【英文标题】：In Apache Spark, I have a dataframe with one column which has string (its a date) but leading zero is missing from month and day

【发布时间】：2020-08-02 05:47:34

【问题描述】：

import org.apache.spark.sql.functions.regexp_replace
val df = spark.createDataFrame(Seq(
  (1, "9/11/2020"),
  (2, "10/11/2020"),
  (3, "1/1/2020"),  
  (4, "12/7/2020"))).toDF("Id", "x4")
val newDf = df
            .withColumn("x4New", regexp_replace(df("x4"), "(?:(\\d2))/(?:(\\d1))/(?:(\\d4))", "$1/0$2/$3"))
val newDf1 = newDf
            .withColumn("x4New1", regexp_replace(df("x4"), "(?:(\\d1))/(?:(\\d1))/(?:(\\d4))", "0$1/0$2/$3"))
            .withColumn("x4New2", regexp_replace(df("x4"), "(?:(\\d1))/(?:(\\d2))/(?:(\\d4))", "0$1/$2/$3"))

newDf1.show

Output now

+---+----------+----------+-----------+-----------+
| Id|        x4|     x4New|     x4New1|     x4New2|
+---+----------+----------+-----------+-----------+
|  1| 9/11/2020| 9/11/2020|  9/11/2020| 09/11/2020|
|  2|10/11/2020|10/11/2020| 10/11/2020|100/11/2020|
|  3|  1/1/2020|  1/1/2020| 01/01/2020|   1/1/2020|
|  4| 12/7/2020|12/07/2020|102/07/2020|  12/7/2020|
+---+----------+----------+-----------+-----------+

`所需的输出，在日或月前面添加前导零是个位数'出于性能原因不想使用 UDF

+---+----------+----------+
| Id|        x4|     date | 
+---+----------+----------+
|  1| 9/11/2020|09/11/2020|
|  2|10/11/2020|10/11/2020|
|  3|  1/1/2020|01/01/2020|
|  4| 12/7/2020|12/07/2020|
+---+----------+----------+-----------+-----------+

【问题讨论】：

在正则表达式的两边使用单词边界，\b（转义反斜杠，"\\b"）。你也可以删除1

这也不起作用 - .withColumn("x4New", regexp_replace(df("x4"), "(?:(\\b\\d2))/(?: (\\d))/(?:(\\d4)\\b)", "$1/0$2/$3"))

使用 spark 3.0 预览版，仍然是 NULL，但是当我使用 Databricks 社区版时，结果如您所说，没有个位数问题。不知道是什么问题。

【参考方案1】：

使用 from_unixtime,unix_timestamp（或）date_format,to_timestamp,(or) to_date 在内置函数中。

Example:(In Spark-2.4)

import org.apache.spark.sql.functions._

//sample data
val df = spark.createDataFrame(Seq((1, "9/11/2020"),(2, "10/11/2020"),(3, "1/1/2020"),  (4, "12/7/2020"))).toDF("Id", "x4")

//using from_unixtime
df.withColumn("date",from_unixtime(unix_timestamp(col("x4"),"MM/dd/yyyy"),"MM/dd/yyyy")).show()

//using date_format
df.withColumn("date",date_format(to_timestamp(col("x4"),"MM/dd/yyyy"),"MM/dd/yyyy")).show()
df.withColumn("date",date_format(to_date(col("x4"),"MM/dd/yyyy"),"MM/dd/yyyy")).show()
//+---+----------+----------+
//| Id|        x4|      date|
//+---+----------+----------+
//|  1| 9/11/2020|09/11/2020|
//|  2|10/11/2020|10/11/2020|
//|  3|  1/1/2020|01/01/2020|
//|  4| 12/7/2020|12/07/2020|
//+---+----------+----------+

【讨论】：

上面的代码没有提供想要的输出。为单个数字的月份和日期抛出空值。

@yogesh,无法重现该场景，您使用的是哪个版本的spark？正如您在我的example 代码中看到的那样，即使我们有 month and day with single digit!!

在我的笔记本电脑上使用 Apache Spark Ver 3.0 预览版，当我使用 databricks 社区版时，您的示例代码可以工作。不知道发生了什么。

【参考方案2】：

`找到了解决方法，看看有没有更好的解决方案，使用一个数据框，不使用UDF'

import org.apache.spark.sql.functions.regexp_replace
val df = spark.createDataFrame(Seq(
  (1, "9/11/2020"),
  (2, "10/11/2020"),
  (3, "1/1/2020"),  
  (4, "12/7/2020"))).toDF("Id", "x4")

val newDf = df.withColumn("x4New", regexp_replace(df("x4"), "(?:(\\b\\d2))/(?:(\\d))/(?:(\\d4)\\b)", "$1/0$2/$3"))
val newDf1 = newDf.withColumn("x4New1", regexp_replace(newDf("x4New"), "(?:(\\b\\d1))/(?:(\\d))/(?:(\\d4)\\b)", "0$1/$2/$3"))
val newDf2 = newDf1.withColumn("x4New2", regexp_replace(newDf1("x4New1"), "(?:(\\b\\d1))/(?:(\\d2))/(?:(\\d4)\\b)", "0$1/$2/$3"))
val newDf3 = newDf2.withColumn("date", to_date(regexp_replace(newDf2("x4New2"), "(?:(\\b\\d2))/(?:(\\d1))/(?:(\\d4)\\b)", "$1/0$2/$3"),"MM/dd/yyyy"))
val formatedDataDf = newDf3
                    .drop("x4New")
                    .drop("x4New1")
                    .drop("x4New2")

formatedDataDf.printSchema
formatedDataDf.show

Output looks like as follows

root
 |-- Id: integer (nullable = false)
 |-- x4: string (nullable = true)
 |-- date: date (nullable = true)

+---+----------+----------+
| Id|        x4|      date|
+---+----------+----------+
|  1| 9/11/2020|2020-09-11|
|  2|10/11/2020|2020-10-11|
|  3|  1/1/2020|2020-01-01|
|  4| 12/7/2020|2020-12-07|
+---+----------+----------+