datafiller 0.2.3

A python package for easy data imputation

DataFiller

DataFiller is a Python library for imputing missing values in datasets. It provides a flexible and powerful way to handle missing data in both numerical arrays and time series data.

Why DataFiller

DataFiller is a pragmatic imputation tool: it is unlikely to match the absolute performance of large deep learning approaches on complex masking patterns, but it is much simpler to fit, easier to adapt, and more flexible to plug into existing workflows. It is also significantly faster than scikit-learn's IterativeImputer, which makes it a good choice when you need strong results with tight iteration cycles.

Key Features

Key features include model-based imputation with lightweight models, mixed data support with one-hot encoding and label recovery, a dedicated TimeSeriesImputer with lag/lead features, performance-critical sections accelerated by Numba, smart feature selection for training subsets, and scikit-learn compatibility.

Installation

Install DataFiller using pip or conda:

pip install datafiller

conda install -c conda-forge datafiller

Basic Usage

Imputing a NumPy Array

The MultivariateImputer can be used to fill missing values (NaN) in a 2D NumPy array.

import numpy as np
from datafiller import MultivariateImputer

# Create a matrix with missing values
X = np.array([
    [1.0, 2.0, 3.0, 4.0],
    [5.0, np.nan, 7.0, 8.0],
    [9.0, 10.0, 11.0, np.nan],
    [13.0, 14.0, 15.0, 16.0],
])

# Initialize the imputer and fill the missing values
imputer = MultivariateImputer()
X_imputed = imputer(X)

print("Original Matrix:")
print(X)
print("\nImputed Matrix:")
print(X_imputed)

Imputing a Time Series DataFrame

The TimeSeriesImputer is designed to work with pandas DataFrames that have a DatetimeIndex. It automatically creates autoregressive features (lags and leads) to improve imputation accuracy.

import pandas as pd
import numpy as np
from datafiller import TimeSeriesImputer

# Create a time series DataFrame with missing values
rng = pd.date_range('2023-01-01', periods=10, freq='D')
data = {
    'feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature2': [10, 9, np.nan, 7, 6, 5, np.nan, 3, 2, 1],
}
df = pd.DataFrame(data, index=rng)

# Initialize the imputer with lags and leads
# Use t-1 and t+1 to impute missing values
ts_imputer = TimeSeriesImputer(lags=[1, -1])
df_imputed = ts_imputer(df)

print("Original DataFrame:")
print(df)
print("\nImputed DataFrame:")
print(df_imputed)

Imputing a Mixed DataFrame with Categorical Features

Categorical columns are one-hot encoded and used as predictors for other columns, while missing categorical values are imputed with a classifier and mapped back to labels.

from datafiller.datasets import load_titanic
from datafiller import MultivariateImputer, ExtremeLearningMachine

df = load_titanic()
imputer = MultivariateImputer(regressor=ExtremeLearningMachine())
df_imputed = imputer(df)

How It Works

DataFiller uses a model-based imputation strategy. For each column containing missing values, it trains a model using the other columns as features. Categorical, boolean, and string columns are one-hot encoded for feature construction, so they can drive the imputation of numerical targets, and are imputed with a classifier before being mapped back to the original labels. The rows used for training are carefully selected to be the largest, most complete rectangular subset of the data, which is found using the optimask algorithm. This ensures that the training data is of the highest possible quality, leading to more accurate imputations.

For more details, see the documentation.