Filter Rows

Transform

Remove rows that don’t match your conditions. Define one or more condition groups — rows pass if they match ALL conditions in ANY group (AND within groups, OR across groups). Use it to clean data, extract subsets, or focus on specific segments before further processing.

Local processing

This transform runs entirely in your browser. No data is sent to external servers.

How it works

Filter Rows evaluates each row against your conditions and keeps only the rows that pass. It processes data one row at a time (streaming), so it works efficiently even on large files.

Conditions are organized into groups. Within a group, all conditions must be true (AND logic). Across groups, any group passing is enough (OR logic). This lets you express rules like “keep rows where status is ‘active’ AND region is ‘US’” or more complex filters like “(status is ‘active’ AND region is ‘US’) OR (status is ‘pending’ AND amount > 1000)”.

If no conditions are defined, all rows pass through unchanged.

Input: One tabular data connection. Output: The same columns, with non-matching rows removed.

Options

Condition Groups

Each group contains one or more conditions. Add multiple groups to create OR logic.

Conditions

Each condition specifies a column, an operator, and (for most operators) a comparison value.

Operator	Description	Requires Value
equals	Exact string match	Yes
not equals	Passes when the cell does not match the value	Yes
contains	Substring match (case-sensitive)	Yes
not contains	Passes when the cell does not contain the value	Yes
greater than	Numeric comparison: cell > value	Yes
less than	Numeric comparison: cell < value	Yes
greater than or equals	Numeric comparison: cell ≥ value	Yes
less than or equals	Numeric comparison: cell ≤ value	Yes
is empty	Passes when the cell is empty, null, or undefined	No
is not empty	Passes when the cell has any value	No

Examples

Keep only active users

You have a user list and want to extract rows where the status column is “active”.

Before:

name	email	status
Alice	[email protected]	active
Bob	[email protected]	inactive
Carol	[email protected]	active
Dave	[email protected]	pending

Configuration: One group with one condition — column: status, operator: equals, value: active.

After:

name	email	status
Alice	[email protected]	active
Carol	[email protected]	active

Remove rows with missing data

You want to exclude rows where the email column is empty.

Before:

name	email	role
Alice	[email protected]	admin
Bob		user
Carol	[email protected]	user

Configuration: One group with one condition — column: email, operator: is not empty.

After:

name	email	role
Alice	[email protected]	admin
Carol	[email protected]	user

OR logic: keep high-value or VIP rows

You want to keep orders above $500 OR orders from VIP customers, regardless of amount.

Before:

customer	amount	tier
Acme Corp	250	standard
Beta Inc	750	standard
Gamma LLC	100	vip
Delta Co	50	standard

Configuration: Two groups:

• Group 1: column amount, operator greater than, value 500
• Group 2: column tier, operator equals, value vip

After:

customer	amount	tier
Beta Inc	750	standard
Gamma LLC	100	vip

Tips and Edge Cases

• All comparisons are string-based except the numeric operators (greater than, less than, greater than or equals, less than or equals), which parse both the cell and the comparison value as numbers. If either side can’t be parsed as a number, the condition fails for that row.
• contains is case-sensitive. “Active” does not match a condition value of “active”. To do case-insensitive matching, add a Computed Column that lowercases the field first, then filter on that.
• Empty string vs. null. The is empty operator matches empty strings, null, and undefined. The equals operator with an empty value matches only empty strings, not null.
• No conditions = passthrough. If you add a Filter Rows node but don’t define any conditions, all rows pass through unchanged. This is intentional — it lets you set up the node and configure it later without breaking the flow.

Related Transforms

• Select Columns — remove unwanted columns (Filter Rows removes unwanted rows)
• Remove Duplicates — remove duplicate rows based on key columns
• Sort Rows — reorder rows without removing any