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
--------- 

.. list-table::
      :widths: 10 5 10
      :header-rows: 1

      * - 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