Assert expression — assertExpression

Verify that a boolean expression evaluates to true for every row.

The node is pass-through: the dataframe leaves unchanged regardless of the outcome. The violation is recorded in the run's per-node result and either fails the run (error mode) or logs a warning and continues (warn mode).

Use cases

• Assert a business invariant: revenue >= cost on every row.
• Validate a cross-column relationship: end_date >= start_date.
• Catch unexpected nulls in a derived column: score.notnull().
• Any condition expressible as a pandas column expression.

Configuration

The expression is evaluated via df.eval(expression) which supports arithmetic operators (+, -, *, /), comparisons (==, !=, >, <, >=, <=), boolean operators (and, or, not), and column name references.

Behavior

The per-node result in the run detail always includes assertion_passed, assertion_violation_count, and a sample of up to 5 violating rows.

Generated Python code

_expr_mask = ~df_1.eval('revenue >= cost').astype(bool)
if _expr_mask.any():
    raise ValueError(f"assertExpression: {_expr_mask.sum()} row(s) violate 'revenue >= cost'")

In warn mode the raise is replaced by warnings.warn(...) and execution continues.

Tips & common mistakes

• Use pandas eval syntax. Column names with spaces need backtick quoting: `my column` > 0.
• Expressions must return a boolean series. An expression like price * 2 does not evaluate to booleans and will error; use price * 2 > 0 instead.
• NaN comparisons return False, so a null in any compared column will appear as a violation. Precede this node with Fill nulls or Drop nulls if needed.
• For a simpler range check on a single column, use Assert value range — it has a dedicated UI and clearer error messages.

See also

• Assert value range · Assert not null
• Calculated column · Filter rows