Filter by expression — filterExpression

Keep rows where a boolean expression evaluates to true. Unlike Filter rows (one column, one operator), this handles several conditions combined with and / or in a single expression.

Use cases

• Multi-column filters: amount > 100 and status == 'paid'.
• Ranges and combinations: score >= 0.8 or manual_review == True.
• Arithmetic conditions: revenue - cost > 0.

What it does

The expression is evaluated per row with pandas eval semantics — the same syntax as Calculated column — and only rows where it is true are kept. The result behaves identically on the pandas and polars engines. Column count is unchanged; the row index is reset.

Configuration

Reference columns by name; use and / or / not, comparisons (==, !=, >, >=, <, <=), and arithmetic (+ - * /).

Build condition helper

Above the expression textarea, the config panel also has an optional Build condition mini-form: pick a column, an operator, and a value, then click AND or OR to append the resulting condition onto whatever is already in the expression. It's purely a convenience for constructing the expression string one piece at a time — it doesn't add any config of its own, and you can ignore it and type the expression by hand instead.

Generated Python code

df_2 = df_1.query("amount > 100 and status == 'paid'").reset_index(drop=True)

Tips & common mistakes

• Use == for equality (not =), and quote text values: status == 'paid'.
• For a single simple condition, Filter rows is quicker.
• To produce a label from conditions instead of filtering, use Conditional column.

See also

• Filter rows · Calculated column · Conditional column