GreaterThan / Operator Layer

Compare if values in a column are greater than a constant or another column's values. Similar to pandas df['A'] > df['B'] or df['A'] > 5, numpy.greater(), or R's > operator. Returns a boolean column (true where left value exceeds right value).

Mathematical form: $y_{i} = {t r u e f a l se if x_{i} > v otherwise$

Common applications:

Threshold detection (temperature > limit)
Performance monitoring (speed > requirement)
Quality control (yield > minimum)
Risk assessment (exposure > safe_level)
Time series analysis (current > previous)
Financial alerts (price > target)

Note: Comparison follows standard numeric ordering rules.

Table

SelectLeft

column

The primary column for comparison. Forms the left side of the greater-than check (e.g., measured value, current reading, test result).

SelectRight

oneof

Integer

Value

i64

64-bit signed integer threshold. Range: -2^63 to 2^63-1. Use cases:

Age checks (age > 18)
Count thresholds (errors > 5)
Level monitoring (stock > reorder_point)
Score filtering (points > passing_grade)

UnsignedInteger

Value

u64

64-bit unsigned integer threshold. Range: 0 to 2^64-1. Examples:

Size limits (file_size > max_allowed)
Usage monitoring (requests > quota)
Capacity planning (demand > supply)
Performance metrics (throughput > baseline)

Float

Value

f64

64-bit floating-point threshold. Common scenarios:

Statistical significance (p_value > 0.05)
Price points (cost > budget)
Quality metrics (accuracy > 0.95)
Environmental monitoring (ph > neutral)

Column

Other

column

The comparison column for greater-than check. Common pairs:

actual > expected (overperformance)
current > previous (growth)
measured > baseline (deviation)
revenue > costs (profitability)

PropagateNull

bool

true

Controls null value handling:

true: propagates nulls (null > value returns null)
false: treats nulls as false

Default is true for standard SQL-like null behavior

AsColumn

name

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.