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将 pyspark 中的括号替换为 replace_regex

[英]Replace parentheses in pyspark with replace_regex

+---+------------+
|  A|           B|
+---+------------+
| x1|        [s1]|
| x2|   [s2 (A2)]|
| x3|   [s3 (A3)]|
| x4|   [s4 (A4)]|
| x5|   [s5 (A5)]|
| x6|   [s6 (A6)]|
+---+------------+

The desired result:期望的结果:

+---+------------+-------+
|A  |B           |value  |
+---+------------+-------+
|x1 |[s1]        |[s1]   |
|x2 |[s2 (A2)]   |[s2]   |
|x3 |[s3 (A3)]   |[s3]   |
|x4 |[s4 (A4)]   |[s4]   |
|x5 |[s5 (A5)]   |[s5]   |
|x6 |[s6 (A6)]   |[s6]   |
+---+------------+-------+

When I applied each of the codes below, the parentheses and the whitespace before them were not replaced:当我应用下面的每个代码时,它们之前的括号和空格没有被替换:

from pyspark.sql.functions import expr
df.withColumn("C",
               expr('''transform(B, x-> regexp_replace(x, ' \\(A.\\)', ''))''')).show(truncate=False)

or或者

df.withColumn("C",
               expr('''transform(B, x-> regexp_replace(x, ' \(A.\)', ''))''')).show(truncate=False)

The obtained result:得到的结果:

+---+------------+------------+
|A  |B           |value       |
+---+------------+------------+
|x1 |[s1]        |[s1]        |
|x2 |[s2 (A2)]   |[s2 ()]     |
|x3 |[s3 (A3)]   |[s3 ()]     |
|x4 |[s4 (A4)]   |[s4 ()]     |
|x5 |[s5 (A5)]   |[s5 ()]     |
|x6 |[s6 (A6)]   |[s6 ()]     |
+---+------------+------------+

You can create a UDF that removes all elements from the array that match the regular expression r"\(.*\)" .您可以创建一个 UDF,从数组中删除与正则表达式r"\(.*\)"匹配的所有元素。 If necessary, you can change the regexp to match r"\(A.\)" if that is required.如有必要,您可以更改正则表达式以匹配r"\(A.\)"如果需要)。

import re
replaced = F.udf(lambda arr: [s for s in arr if not re.compile(r"\(.*\)").match(s)], \
                 T.ArrayType(T.StringType()))
df.withColumn("value", replaced("B")).show()

You can split the array value and get only the first index from the array.您可以拆分数组值并仅从数组中获取第first index

  • (or) using regexp_replace function. (或)使用regexp_replace function。

Example:

df.show()
#+---+---------+
#|  A|        B|
#+---+---------+
#| x1|     [s1]|
#| x2|[s2 (A2)]|
#+---+---------+

df.printSchema()
#root
# |-- A: string (nullable = true)
# |-- B: array (nullable = true)
# |    |-- element: string (containsNull = true)

df.withColumn("C",expr('''transform(B,x -> split(x,"\\\s+")[0])''')).show()

#using regexp_replace function
df.withColumn("C",expr('''transform(B,x -> regexp_replace(x,"(\\\s+.*)",""))''')).show()
df.withColumn("C",expr('''transform(B,x -> regexp_replace(x,"(\\\s+\\\((?i)A.+\\\))",""))''')).show()
#+---+---------+----+
#|  A|        B|   C|
#+---+---------+----+
#| x1|     [s1]|[s1]|
#| x2|[s2 (A2)]|[s2]|
#+---+---------+----+

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