Dragon Imputation

A Polars plugin for performing imputation on missing values in DataFrames.

MICE imputation uses a decision tree model to predict missing values based on the observed values in the DataFrame. The algorithm iteratively fills in missing values by modeling each variable with missing data as a function of other variables in a round-robin fashion.

Installation

pip install dragon-imputation

uv add dragon-imputation

Usage

import polars as pl
from dragon_imputation import mice_impute

# Create a sample DataFrame with missing values
df = pl.DataFrame({
    "A": [1, 2, None, 4],
    "B": [None, 2, 3, 4],
    "C": [1, None, 3, 4]
})

# Perform MICE imputation with 5 iterations
imputed_df = mice_impute(df, 5)

Arguments

mice_impute() function takes the following arguments:

df: A Polars DataFrame containing missing values.
max_iterations: The maximum number of iterations for the MICE algorithm (default is 10).
max_depth: The maximum depth for the decision tree model used in imputation (default is None for no depth limit).

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dragon-imputation 0.1.2

Dragon Imputation

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