Filter rows

Filter rows — `filterRows`

Keep rows matching a condition.

Use cases

Keep only positive amounts, a date range, or a set of categories.
Drop rows where a key column is null (isnull/notnull).
Text matching: contains, startswith, endswith.

What it does

Rows that match the condition are kept; everything else is dropped. The number of columns does not change.

Before

order_id	region	amount
1001	North	120.5
1002	South	-5
1003	North	89
1004	South	0

4 rows · 3 cols

Filter rows (amount > 0)

After

order_id	region	amount
1001	North	120.5
1003	North	89

2 rows · 3 cols

Configuration

Config key	Type	Required	Description
`column`	string	Yes	Column to test
`operator`	string	Yes	See operators below
`value`	any	Conditional	Required except for `isnull` / `notnull`
`value2`	any	Conditional	Upper bound, required for `between`

Operators: ==, !=, >, >=, <, <=, isnull, notnull, between (needs value2), in (comma-separated or a list), contains, startswith, endswith.

Numeric columns accept numeric values; text columns accept strings — the editor keeps the value type aligned to the column.

Generated Python code

df_2 = df_1.loc[lambda _d: _d['amount'] > 0]

Tips & common mistakes

between needs an upper bound (value2); the form blocks saving without it. It's inclusive on both ends.
in takes a comma-separated list (x, z) or an actual list.
For multi-condition AND/OR logic that produces a label (not just a filter), use Conditional column.