advprep 0.1.2

Advanced data imputation library

advprep: Advanced Data Preprocessing Toolkit

advprep is a Python package providing advanced tools for data preprocessing, including imputation, encoding, scaling, feature engineering, and an easy-to-use preprocessing pipeline. It helps you clean and prepare your datasets efficiently for machine learning or analysis.

Installation

Make sure you have these dependencies installed:

pip install pandas numpy scikit-learn category_encoders

Then, include the advprep package files in your project folder or install it as a local package.

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Modules and Usage

1. Imputation (imputation.py)

Handle missing data with different strategies like mean, median, mode, forward-fill, backward-fill, KNN, and iterative imputation.

from advprep.imputation import AdvancedImputer
import pandas as pd

df = pd.read_csv("data.csv")
imputer = AdvancedImputer(method="knn", knn_neighbors=3)
df_imputed = imputer.fit_transform(df)

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2. Encoding (encoding.py)

Encode categorical features using One-Hot, Ordinal, or Target encoding.

from advprep.encoding import AdvancedEncoder

encoder = AdvancedEncoder(method="onehot")
df_encoded = encoder.fit_transform(df, columns=["CategoryCol1", "CategoryCol2"])

For target encoding:

encoder = AdvancedEncoder(method="target")
df_encoded = encoder.fit_transform(df, columns=["CategoryCol"], target="TargetColumn")

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3. Scaling (scaling.py)

Scale numerical features using StandardScaler, MinMaxScaler, RobustScaler, or PowerTransformer.

from advprep.scaling import AdvancedScaler

scaler = AdvancedScaler(method="minmax")
df_scaled = scaler.fit_transform(df)

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4. Feature Engineering (feature_engineering.py)

Create new features like date parts or polynomial features.

Extract date/time features:

from advprep.feature_engineering import FeatureEngineer

df_features = FeatureEngineer.extract_date_features(df, date_column="OrderDate")

Generate polynomial features:

df_poly = FeatureEngineer.generate_polynomial_features(df, columns=["Feature1", "Feature2"], degree=3)

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5. Pipeline (pipeline.py)

Combine multiple preprocessing steps into a single pipeline for easy and repeatable workflows.

from advprep.imputation import AdvancedImputer
from advprep.encoding import AdvancedEncoder
from advprep.scaling import AdvancedScaler
from advprep.pipeline import PreprocessingPipeline

pipeline = PreprocessingPipeline(steps=[
    AdvancedImputer(method="median"),
    AdvancedEncoder(method="onehot"),
    AdvancedScaler(method="standard")
])

df_processed = pipeline.fit_transform(df)

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Notes

• For target encoding, always provide the target column name when fitting.
• Date columns must be parseable by pd.to_datetime for date feature extraction.
• By default, most methods operate on all suitable columns (numeric for imputation/scaling, categorical for encoding). You can specify columns to limit processing.
• The pipeline applies the steps sequentially to the DataFrame.
• The iterative imputer uses Bayesian Ridge by default and requires scikit-learn version supporting it.
• category_encoders is required