polars-random 0.3.1

Polars plugin for generating random distributions

polars-random

Polars plugin for generating random distributions.

Description

polars-random is a Rust plugin for the Polars DataFrame library that provides functionality to generate random numbers through a new dataframe namespace called "random". It supports generating random numbers from various distributions such as uniform, normal, and binomial.

You can set seeds, and pass the parameters as polars expressions or column names (as strings).

Installation

To use polars-random, install it using your favourite tool:

uv add polars-random

poetry add polars-random

pip install polars-random

Usage

For every available distribution, parameters can be passed as polars expressions or native python objects (int, float, string...).

Here are some examples of how to use the polars-random plugin for generating uniform distributions:

import polars as pl
# This will automatically register .random
# in pl.DataFrame namespace
import polars_random

df: pl.DataFrame = ...

If we want to generate a new uniform column called rand based on some parameters:

(
    df
    .random.rand(
        low=1_000.,
        high=2_000.,
        name="rand",
    )
)

We can also add a seed and make the generation reproducible. In the following case, we are using default parameters (low=1. and high=1.) and generating a uniform distribution called rand_seed:

(
    df
    .random.rand(
        seed=42, 
        name="rand_seed",
    )

If we want custom parameters, we can use polars expressions. Let's say we have two columns called custom_low and custom_high. For generating a new column, we can use either the expression pl.col("custom_low") or a python string "custom_low":

(
    df
    .random.rand(
        low=pl.col("custom_low"),
        high=pl.col("custom_high"),
        name="rand_expr",
    )
    .random.rand(
        low="custom_low",
        high="custom_high",
        name="rand_str",
    )
)

Distributions

Uniform distribution

import polars as pl
import polars_random

df: pl.DataFrame = ...

random_series = (
    df
    .random.rand(low=1_000., high=2_000., name="rand")
    .random.rand(seed=42, name="rand_seed")
    .random.rand(
        low=pl.col("custom_low"),
        high=pl.col("custom_high"),
        name="rand_expr",
    )
    .random.rand(
        mean="custom_low",
        std="custom_high",
        name="rand_str",
    )
)

Normal Distribution

import polars as pl
import polars_random

df: pl.DataFrame = ...

random_series = (
    df
    .random.normal(mean=3., std=2., name="normal")
    .random.normal(seed=42, name="normal_seed")
    .random.normal(
        mean=pl.col("custom_mean"),
        std=pl.col("custom_std"),
        name="normal_expr",
    )
    .random.normal(
        mean="custom_mean",
        std="custom_std",
        name="normal_str",
    )
)

Binomial Distribution

import polars as pl
import polars_random

df: pl.DataFrame = ...

random_series = (
    df
    # Mandatory parameters n and p
    .random.binomial(n=100, p=.5, seed=42, name="binomial")
    .random.binomial(
        n=pl.col("custom_n"),
        p=pl.col("custom_p"),
        name="binomial_expr",
    )
    .random.binomial(
        n="n",
        p="p",
        name="binomial_str",
    )
)