ConstNumeric / New Layer
Create a new column filled with a constant value, similar to numpy.full() or pandas.Series with a constant. Supports various numeric types from 8-bit to 64-bit integers and floating-point numbers. The operation lets you choose the precise numeric type through ColType and fills every row with the same specified Value.
ColType
oneofValue
f6464-bit double-precision floating point value (IEEE 754). Provides about 15-17 decimal digits of precision. Standard choice for scientific and financial calculations.
Value
f3232-bit single-precision floating point value (IEEE 754). Provides about 6-8 decimal digits of precision. Good balance of precision and memory efficiency.
Value
i6464-bit signed integer value ranging from -2^63 to 2^63-1 (-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807). Suitable for large numbers in scientific computing.
Value
i3232-bit signed integer value ranging from -2^31 to 2^31-1 (-2,147,483,648 to 2,147,483,647). Common in general-purpose computing.
Value
i1616-bit signed integer value ranging from -32,768 to 32,767. Useful for compact storage of moderate-range whole numbers.
Value
i88-bit signed integer value ranging from -128 to 127. Ideal for small numbers and memory-efficient storage.
Value
u6464-bit unsigned integer value ranging from 0 to 2^64-1 (0 to 18,446,744,073,709,551,615). Perfect for large positive numbers or identifiers.
Value
u3232-bit unsigned integer value ranging from 0 to 2^32-1 (0 to 4,294,967,295). Commonly used for positive numbers and indices.
Value
u1616-bit unsigned integer value ranging from 0 to 65,535. Suitable for small positive numbers and efficient memory usage.
AsColumn
nameName 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.