lochan-eda 0.1.1

An automated, leakage-free data preprocessing pipeline for machine learning.

Lochan-eda

An intelligent, stateful preprocessing pipeline that decides how to clean your data, prevents data leakage, and prepares raw datasets for production machine learning models.

Most EDA helpers apply the same fixed rule to every column. lochan-eda acts as an automated data engineer. It analyzes each column's statistical shape — missingness, skew, cardinality, sparsity, outlier ratio — and dynamically picks the appropriate imputation, outlier, and scaling strategy.

Crucially, v0.1.0 introduces Stateful Memory. It learns the rules on your training data, saves them, and blindly applies those exact rules to your testing data. Zero data leakage. Zero deployment crashes.

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Why lochan-eda?

A real preprocessing pass usually means writing the same boilerplate decision tree by hand, every time. Is this column highly skewed? Does it have a long tail? Is this a high-cardinality string?

lochan-eda automates that decision tree. It is not a black box: every rule it applies is documented below. You always know why a column was scaled with RobustScaler instead of StandardScaler, or why a category got grouped into "Other".

Features

• Leakage-Free Architecture (New in v0.1.0) — strictly separates .fit() learning from .transform() application using an is_train flag to prevent train-test contamination.
• Adaptive Imputation — deploys mean, median, or mode strategies dynamically based on column skewness and data behavior.
• Adaptive Outlier Handling — softly clips, winsorizes, or mathematically transforms tails depending on severity, without ever dropping rows (ensuring X and y shapes never mismatch).
• Adaptive Scaling — selects StandardScaler, RobustScaler, or MaxAbsScaler based on sparsity and outlier ratios.
• Smart Categorical Encoding — routes to binary mapping, one-hot encoding, frequency, or TargetEncoder based on unique cardinality.
• Rare-Category Grouping — collapses low-frequency categories into "Other" to prevent high-dimensional noise.

Installation

pip install lochan-eda

Note: Requires scikit-learn >= 1.3.0 for native TargetEncoder support.

Quickstart (Machine Learning Workflow)

To prevent data leakage, always split your data into Training and Testing sets before passing it through the pipeline.

import pandas as pd
from sklearn.model_selection import train_test_split
from lochan_eda import AutomatedEDA

df = pd.read_csv("your_data.csv")

# 1. Split your data first
X = df.drop(columns="Target_Column")
y = df["Target_Column"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. Initialize the pipeline engine
eda = AutomatedEDA()

# 3. TRAIN MODE: Pipeline analyzes distributions, saves the rules, and cleans X_train
X_train_clean = eda.run_pipeline(df=X_train, target=y_train, is_train=True)

# 4. TEST MODE: Pipeline blindly applies the saved rules to X_test (No Leakage)
X_test_clean = eda.run_pipeline(df=X_test, is_train=False)

How It Decides

Numerical Columns (HandleNumerical)

Step	Condition	Action
Imputation	>40% missing	Column dropped
	Uniqueness < 1% of rows	Mode imputation (treats as categorical)
	0–40% missing,	skew
	0–40% missing,	skew
Outliers	Outlier share ≤ 3%	Clipped strictly to IQR bounds
	Outlier share > 3%, spiked tail	Winsorized at 5th/95th percentile
	Outlier share > 3%, long tail, contains zeros	Square-root transform
	Outlier share > 3%, long tail, strict positive	Log1p transform
Scaling	Sparsity ≥ 50% zeros	MaxAbsScaler
	Skew > 1.0, non-negative	Log1p transform + StandardScaler
	Outlier ratio ≥ 5%	RobustScaler
	Otherwise	StandardScaler

Categorical Columns (HandleCategorical)

Step	Condition	Action
Imputation	>40% missing	Column dropped
	>10–40% missing	Filled with "Unknown"
	≤10% missing	Mode imputation
Rare Grouping	Frequency < threshold (default 5%)	Grouped into "Other"
Encoding	≤2 unique values	Binary integer mapping (0 and 1)
	3–10 unique values	One-hot encoding (aligns test columns perfectly)
	>10 unique values, target provided	Scikit-Learn TargetEncoder
	>10 unique values, no target	Frequency encoding

API Reference

Orchestration

Class / Method	Description
AutomatedEDA()	Initializes the persistent memory manager.
.run_pipeline(df, target, is_train)	Runs the end-to-end numerical and categorical pipeline. Set is_train=True for training data and is_train=False for testing/inference data.

Component Level

You can use the numerical or categorical engines individually. Ensure you instantiate the object once, then pass is_train=True and is_train=False to the processing methods.

from lochan_eda import HandleNumerical, HandleCategorical

# Initialize
num_handler = HandleNumerical(X_train)

# Learn & Clean Train Data
X_train_num = num_handler.full_handler(is_train=True, exclude="customer_id")

# Inject & Clean Test Data
num_handler.num_df = X_test.select_dtypes(include=["number"]).copy()
X_test_num = num_handler.full_handler(is_train=False, exclude="customer_id")

Testing

git clone [https://github.com/lochanjangid/lochan-eda.git](https://github.com/lochanjangid/lochan-eda.git)
cd lochan-eda
pip install -e .
pip install pytest
pytest -v

Contributing

Issues and PRs are heavily welcomed. Please ensure you run pytest before submitting a pull request to verify structural integrity across Train/Test splits.

License

MIT © Lochan Jangid