The Filter rows step keeps rows that satisfy a set of rules. You can use this step to create simple filters with one rule, or complex filters with many rules.

Filter rows

Input/output

Let's say we want to filter the "Product Name" column by setting a rule to include rows where the value is contains the word "Road".
We'll use the "Product Name" column from the input data.



After using Filter rows step, we will have the filtered rows where the "Product Name" column contains the word "Road".



Settings

Pick the dataset. By default, the selected option is keep, this means that if a row satisfies the selected rule, that row will be kept. You can reversing the filtering logic by setting the option to remove, this means that if a row satisfies the selected rule, that row will be removed.
Now it's time to set up filters for rows. by selecting a column, a filtering condition, and a matching value.

Available filtering conditions:

is blank
is not blank
is equal to
is not equal to
contains
do not contain
is greater than
is greater than or equal to
is less than
is less than or equal to



Helpful tips

Filtering dates: As you can see from the example above, the Filter rows step can generally handle filtering for dates that match a certain criteria without any additional date formatting required. However, if you're experiencing any issues with filtering dates, we recommend trying to convert your dates into one of the following formats: Unix format or Lexicographical format. To update your date formats, use a Format dates step before sending your data to the Filter rows step. For Unix format, use capital X for precision to the second and lowercase x for precision to the millisecond. For Lexicographical format, use YYYY-MM-DD HH:mm:ss.

Reversing the filtering logic: If you set up a filter with multiple rules and wish to instead see the rows that are being removed by the filter, you can reverse your filtering by changing keep rows where to remove rows where.

Filtering symmetrically: If you need to split your table to create separate branches that will eventually be merged back together to recreate the complete data set, you will use a few Filter rows step while making sure you're not duplicating or losing any rows in that process. For example, let's say you have data that spans three months (October, November, and December) and you need to process each month differently. You want one Filter rows step per month.

You'll need to use three separate Filter rows step with the following rules:
Date is contains 2019-10.
Date is contains 2019-11.
Date is contains 2019-12.
A good way to make sure you successfully filtered symmetrically is to use a Stack tables step at the end of your separate branches to merge the data back together. The resulting row count should match the row count of your starting data before the Filter rows steps.
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