Hello,
my data ended at Record # 1084573, but it seems that my output inserted additional blank rows. are there any tools that allows me to remove the blank rows? from row # 1084574 onwards?
thank you!
Lance
Solved! Go to Solution.
I would bring in the sample tool at the start of the worflow to filter out anything at the start.
It could help with performance.
1. You can use sample tool to restrict the no. of rows but if more rows comes then there will be loss of data..so prevent this data loss you can simply filter at one of you key field for not null.
2. And if you still want to restrict the no. of rows you can put "record limit" in input tool configuration itself.
Thanks
Vishwa
You could use the Filter tool to find records with IsNull([Field]) -- or IsEmpty([Field])...
I tried your approach using a column. however there is a possibility that the column selected for the isnull() or isempty() filter itself is not available in the data reaching there.
Is there any other approach we can think of ?
It is quite interesting that Alteryx does not provide a simple tool to remove empty rows. My best guess is we can use R or Python to achieve this but not sure about the performance.
Easiest is to use a "Custom Filter". Add all column names in a custom filter like below. Let's take an example:
Assume your data has 4 columns - Column 1, Column 2, Column 3 and Column 4
Then use below in custom filter formula:
IsNull([Column 1]) AND IsNull([Column 2]) AND IsNull([Column 3]) AND IsNull([Column 4])
so, if none of the column has data, it would mean the row is blank. Hence, everything in "True" output will be blank rows and you can just use the filtered out results under "False" further in your flow.
Hope this helps.
Tip: If you have a lot of columns, just list columns in excel and use basic concat formula to create the filter string above.
I see it has been a while since this question was asked however this is for anyone else referring to this article for an answer.
It looks like under Data Cleansing there are options to 'remove null columns' and 'remove null rows'. Works a treat!