使用正则表达式查找字符串中的特定字符 [英] Regular expression to find Specific character in a string

问题描述

我有这些样本值

prm_2020 P02 United Kingdom London 2 for 2
prm_2020 P2 United Kingdom London 2 for 2
prm_2020 P10 United Kingdom London 2 for 2
prm_2020 P11 United Kingdom London 2 for 2

需要找到这样的P2，P02，P11，p06，p05，并尝试在数据块中使用Regexp_extract函数.努力寻找正确的表达方式.一旦找到P10，字符串中的p6，我需要将数字放在名为ID的新列中

Need to find P2, P02, P11,p06,p05 like this, trying to use Regexp_extract function in databricks. struggling to find the correct expression. Once i find P10, p6 from string i need to put numbers in new column called ID

select distinct
    promo_name
   ,regexp_extract(promo_name, '(?<=p\d+\s+)P\d+') as regexp_id
from stock
where promo_name is not null


select distinct
    promo_name
   ,regexp_extract(promo_name, 'P[0-9]+') as regexp_id
from stock
where promo_name is not null

两者都会产生错误

推荐答案

函数 regexp_extract 将使用3个参数.

• 列值
• 正则表达式模式
• 组索引

def regexp_extract(e: org.apache.spark.sql.Column,exp: String,groupIdx: Int): org.apache.spark.sql.Column

您缺少 regexp_extract 函数中的最后一个参数.

You are missing last parameter in regexp_extract function.

检查以下代码.

scala> df.show(truncate=False)
+------------------------------------------+
|data                                      |
+------------------------------------------+
|prm_2020 P02 United Kingdom London 2 for 2|
|prm_2020 P2 United Kingdom London 2 for 2 |
|prm_2020 P10 United Kingdom London 2 for 2|
|prm_2020 P11 United Kingdom London 2 for 2|
+------------------------------------------+

df
.withColumn("parsed_data",regexp_extract(col("data"),"(P[0-9]*)",0))
.show(truncate=False)
+------------------------------------------+-----------+
|data                                      |parsed_data|
+------------------------------------------+-----------+
|prm_2020 P02 United Kingdom London 2 for 2|P02        |
|prm_2020 P2 United Kingdom London 2 for 2 |P2         |
|prm_2020 P10 United Kingdom London 2 for 2|P10        |
|prm_2020 P11 United Kingdom London 2 for 2|P11        |
+------------------------------------------+-----------+

df.createTempView("tbl")
spark
.sql("select data,regexp_extract(data,'(P[0-9]*)',0) as parsed_data from tbl")
.show(truncate=False)
+------------------------------------------+-----------+
|data                                      |parsed_data|
+------------------------------------------+-----------+
|prm_2020 P02 United Kingdom London 2 for 2|P02        |
|prm_2020 P2 United Kingdom London 2 for 2 |P2         |
|prm_2020 P10 United Kingdom London 2 for 2|P10        |
|prm_2020 P11 United Kingdom London 2 for 2|P11        |
+------------------------------------------+-----------+

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