Quickstart

Installation

pip install lochan-eda

Requirements

Python ≥ 3.8 and scikit-learn >= 1.3.0 (needed for native TargetEncoder support). pandas, numpy, matplotlib, and seaborn install automatically as dependencies.

Your first pipeline

Always split your data into train/test before passing it through the pipeline — that ordering is what makes the leakage-free guarantee possible.

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 — learns the rules, saves them, and cleans X_train
X_train_clean = eda.run_pipeline(df=X_train, target=y_train, is_train=True)

# 4. TEST MODE — blindly applies the saved rules to X_test (no re-fitting)
X_test_clean = eda.run_pipeline(df=X_test, is_train=False)

That's it — X_train_clean and X_test_clean are fully numeric, NaN-free, and share identical columns, ready to feed into any scikit-learn model.

What just happened?

run_pipeline internally routes numeric columns through HandleNumerical and categorical/object/string columns through HandleCategorical, then concatenates the results and aligns the schema. Read Core Concepts next to understand exactly how each decision is made.