Find And Replace Using Regex Advanced

This node finds and replaces text in a column containing string

Input

It accepts a DataFrame as input from the previous Node

Type

transform

Class

fire.nodes.etl.NodeFindAndReplaceUsingRegexMultiple

Fields

Name	Title	Description
inputCols	Input Columns	Columns on which to apply Regex
searchPatterns	Find	Enter Search Pattern
replacePatterns	Replace	Enter replacement Value

Details

This node finds and replaces text in a column containing string with another one.

String can be a single character or a combination of words.

In this node multiple find and replace conditions can be entered for one or multiple columns in one go.

Outgoing Dataframe would be created after processing of all conditions.

Examples

Incoming Dataframe has following rows:

EMP_CD    |    EMP_NAME    |    DEPT       |    AGE       |    DATE_OF_JOINING   |    SALARY       |    PERFORMANCE
---------------------------------------------------------------------------------------------------------------------------
E01       |    DAVID       |    HR         |    25        |    2021-01-01        |    12 000.00    |    GOOD
E02       |    JOHN        |    SALES      |    35        |    2019-05-04        |    11 000.00    |    VERY GOOD
E03       |    MARTIN      |    MARKETING  |    40        |    2018-06-07        |    34 000       |    AVERAGE
E04       |    TONY        |    MARKETING  |    45        |    2017-02-01        |    12 500.00    |    VERY VERY GOOD
E05       |    MARK        |    HR         |    25        |    2020-12-21        |    78 999.00    |    BAD

if FindAndReplaceUsingRegexMultiple node is configured as below:

INPUT COLUMNS    |   FIND    |    REPLACE
----------------------------------------------
DATE_OF_JOINING  |   \-      |    \/
SALARY           |   \s      |    ,
PERFORMANCE      |   BAD     |    NOT SO GOOD

then outgoing Dataframe would be created as below

after replacement of occurrence of [-] with [/] in [DATE_OF_JOINING] column

and replacement of occurrence of [SPACES] with [,] in [SALARY]

and replacement of occurrence of [BAD] with [NOT SO GOOD] in [PERFORMANCE] column:

EMP_CD    |    EMP_NAME    |    DEPT       |    AGE       |    DATE_OF_JOINING   |    SALARY       |    PERFORMANCE
---------------------------------------------------------------------------------------------------------------------------
E01       |    DAVID       |    HR         |    25        |    2021/01/01        |    12,000.00    |    GOOD
E02       |    JOHN        |    SALES      |    35        |    2019/05/04        |    11,000.00    |    VERY GOOD
E03       |    MARTIN      |    MARKETING  |    40        |    2018/06/07        |    34,000       |    AVERAGE
E04       |    TONY        |    MARKETING  |    45        |    2017/02/01        |    12,500.00    |    VERY VERY GOOD
E05       |    MARK        |    HR         |    25        |    2020/12/21        |    78,999.00    |    NOT SO GOOD