RandomListNumeric / New Layer

Create a new column where each cell contains a list of random numbers. Similar to creating lists of numpy.random values. Each row gets a randomly-sized list filled with values from the specified probability distribution. Useful for:

Simulating multiple measurements or readings per observation
Generating synthetic sensor data with varying sample sizes
Creating test datasets for array/list operations
Monte Carlo simulations with multiple trials per unit

Table

ElementType

enum

Float64

Float64 ~

64-bit double-precision floating point (IEEE 754). Best for statistical computations where high precision is needed. Provides about 16 decimal digits of precision.

Float32 ~

32-bit single-precision floating point (IEEE 754). Good for basic statistical work where memory efficiency is important. Provides about 7 decimal digits of precision.

MinElements

u32

Minimum number of elements in each generated list. Must be <= MaxElements. Controls the smallest possible list size in the output.

MaxElements

u32

Maximum number of elements in each generated list. Must be >= MinElements. Controls the largest possible list size in the output.

Distribution

oneof

StandardNormal

Generate random numbers following the standard normal distribution (also known as Z-distribution or bell curve). This is the most commonly used probability distribution in statistics and machine learning.

Mathematical form: $f (x) = \frac{1}{2 π} e^{- \frac{x ^{2}}{2}}$ Key properties:

Center (mean) at zero
Spread (standard deviation) of 1
About 68% of values fall between -1 and +1
About 95% of values fall between -2 and +2

Normal

Generate random numbers following a normal distribution with customizable center and spread. Useful for simulating natural phenomena, measurement errors, or any quantities that cluster around a central value.

Mathematical form: $f (x) = \frac{1}{σ 2 π} e^{- \frac{( x - μ ) ^{2}}{2 σ ^{2}}}$

Mean

f64

The center point (Mean, $μ$ ) where values are most likely to occur. In real-world examples, this could be: average measurement, expected value, or central tendency.

StdDev

f64

The spread or variability (StdDev, $σ$ ) around the center. Larger values create more scattered results. Must be positive. Controls how far values typically deviate from the mean.

Uniform

Generate random numbers that are equally likely within a specified range. Like rolling a fair die or sampling from a range with no bias. Useful for simulations, random sampling, and generating test data where any value in the range should be equally probable.

Low

f64

-1

Lower bound (Low) of the range. All generated values will be greater than or equal to this value. Sets the minimum possible value in the distribution.

High

f64

Upper bound (High) of the range. All generated values will be less than or equal to this value. Sets the maximum possible value in the distribution.

Seed

oneof

Random

Use system-provided randomization. Each execution produces different selections. Suitable for security-sensitive applications, simulation scenarios, or cases where true randomness is required.

Fixed

Use seeded random generation for reproducible results. Like random.seed() in Python or set.seed() in R. Essential for testing and reproducible experiments. Note: Not suitable for security-sensitive applications where predictability could be a vulnerability.

Value

u64

Seed value for the random number generator. Same Value guarantees identical sequence of selections from Values. Should not be used for security-critical operations.

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.