Find And Replace Using Regex
===========

This node finds and replaces text in a column with another

Input
--------------
It accepts a DataFrame as input from the previous Node

Type
--------- 

transform

Class
--------- 

fire.nodes.etl.NodeFindAndReplaceUsingRegex

Fields
--------- 

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

      * - Name
        - Title
        - Description
      * - inputCols
        - Input Columns
        - Columns on which to apply Regex
      * - searchPattern
        - Find
        - Enter Search Pattern
      * - replacePattern
        - Replace
        - Enter replacement Value


Details
-------
Find and Replace Details
+++++++++++++++


This node allows the user to find and replace patterns of text within the data. This node will only search the columns selected in the Input Columns option. 

The Find pattern must be in Regex format. This node will only find exact matches for the search pattern.


Examples
-------
Find and Replace Examples
+++++++++++++++


Incoming Dataframe has the 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 FindAndReplaceUsingRegex node is configured to find and replace [-] character in [DATE_OF_JOINING] column with [/]
+++++++++++++++

then outgoing Dataframe would be created as below after replacement of character:


::

    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 FindAndReplaceUsingRegex node is configured to find and replace [^VERY GOOD$] string in [PERFORMANCE] column with [EXCELLENT]
+++++++++++++++

where [^] denotes start of string and [$] denotes end of string

then outgoing Dataframe would be created as below after replacement of exact entry of [VERY GOOD] with [EXCELLENT] and not other entries:


::

    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    |    EXCELLENT
    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


Note: [VERY VERY GOOD] is not replaced as it is not an exact match.