Col

Hi Members,

Please let me know if there is a way to filter column by using both manual selection and wildcard/regex selection together. If so, how to do it?
or if there is a possibility to use multiple wildcard selection?

Thanks in advance

a simple example

$…my column…$ MATCHES “[^?]+” => TRUE

filtering cells null

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wish you help

This is for only row filtering right?

@armingrudd can you please help?

Hi,

As far as I know you can pick only one of the options to use manual selection or wildcard/regex selection.
But if you want to use multiple patterns to filter columns, I suggest you to use Regex. You can define multiple patterns within a single regular expression. For example you can enter:
(int.*)|(text.*)
This regex will select columns that their headers start with “int” or “text”.
If you don’t know about regex, let me know the patterns you look for and I will send the regex for you.
Meanwhile this website is a great source to learn regex:
https://www.regular-expressions.info/

Best,
Armin

Sales.xlsx (9.0 KB)

Attached the file
I want to have output file with columns Sales Person, Region, and all columns with Revenue Target. Although I can select the columns manually using column filter node but the revenue target columns are dynamic in nature as April Revenue Target in April will be April Sales Achieved in May. That is why I thought of using * Revenue Target wildcard and manual selection of Sales Person and Region column. But I am unable to use both simultaneously to get the output.

Well.
Use this expression as shown in the image as well:
(Salesperson)|(Region)|(.*Revenue.*)

Best,
Armin

P.S. Would you please do me a favor and mark solutions in your topics?
You can choose any reply that could resolve your issue.


This will be helpful for the community in the future. Thanks.

3 Likes

You are the best!
Can you please refer where can I learn more about Knime usage?

This is how I learned KNIME:
https://blog.statinfer.com/a-public-knime-school-that-no-one-should-disregard

Best,
Armin

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Thank you so much! Appreciated.

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