dataforgex 0.1.2

DataForgeX: Prep • Plot • Evaluate • Detect

🚀 DataForgeX – Code Reduction Examples

This document shows how DataForgeX (DFX) reduces long and repetitive data science code into simple and readable one-liners.

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1. Handling Missing Values

Without DataForgeX

import pandas as pd

for col in df.columns:
    if df[col].dtype == "object":
        df[col] = df[col].fillna(df[col].mode()[0])
    else:
        df[col] = df[col].fillna(df[col].median())

With DataForgeX

from dfx import handle_missing_values
df = handle_missing_values(df)

Code reduced: ~10 lines → 1 line

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2. Fixing Data Types Automatically

Without DataForgeX

df["age"] = pd.to_numeric(df["age"], errors="coerce")
df["date"] = pd.to_datetime(df["date"], errors="coerce")
df["flag"] = df["flag"].map({"yes": 1, "no": 0})

With DataForgeX

from dfx import auto_fix_dtypes
df = auto_fix_dtypes(df)

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4. Outlier Handling

Without DataForgeX

Q1 = df["chol"].quantile(0.25)
Q3 = df["chol"].quantile(0.75)
IQR = Q3 - Q1

df = df[
    (df["chol"] >= Q1 - 1.5 * IQR) &
    (df["chol"] <= Q3 + 1.5 * IQR)
]

With DataForgeX

from dfx import remove_outliers
df = remove_outliers(df)

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5. Encoding Categorical Features

Without DataForgeX

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
df["sex"] = le.fit_transform(df["sex"])
df = pd.get_dummies(df, columns=["cp"], drop_first=True)

With DataForgeX

from dfx import auto_encode
df = auto_encode(df)

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6. Feature Scaling

Without DataForgeX

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df[cols] = scaler.fit_transform(df[cols])

With DataForgeX

from dfx import scale_data
df = scale_data(df)

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7. Model Evaluation

Without DataForgeX

from sklearn.metrics import accuracy_score, classification_report

y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

With DataForgeX

from dfx import evaluate_classification
evaluate_classification(model, X_test, y_test)

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8. Full Workflow Comparison

Without DataForgeX

df = fix_types(df)
df = fill_missing(df)
plot_all(df)
df = encode(df)
df = scale(df)
evaluate(model)

With DataForgeX

from dfx import auto_fix_dtypes, handle_missing_values, quick_eda
from dfx import auto_encode, scale_data, evaluate_model

df = auto_fix_dtypes(df)
df = handle_missing_values(df)
quick_eda(df)
df = auto_encode(df)
df = scale_data(df)
evaluate_model(model, X_test, y_test)

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📦 Installation

Install the latest stable version from PyPI:

pip install dfx

Install with optional ML features:

pip install dfx[ml]

Install development tools:

pip install dfx[dev]

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🚀 Quick Start

import pandas as pd
from dfx import (
    auto_fix_dtypes,
    handle_missing_values,
    auto_encode,
    scale_data,
    evaluate_classification
)

# Load data
df = pd.read_csv("data.csv")

# Preprocess
df = auto_fix_dtypes(df)
df = handle_missing_values(df)
df = auto_encode(df)
df = scale_data(df)

# Train model
from sklearn.ensemble import RandomForestClassifier

X = df.drop("target", axis=1)
y = df["target"]

model = RandomForestClassifier().fit(X, y)

# Evaluate
evaluate_classification(model, X, y)

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🧠 Core Features

Feature	Description
Automatic dtype fixing	Detects numeric, date, and boolean columns
Missing value handling	Smart median/mode imputation
Auto encoding	Label + one-hot encoding
Scaling	Standard/robust scaling
Outlier removal	IQR-based filtering
Quick EDA	Summary + plots
Model evaluation	Metrics + reports

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🏗️ Library Design

DataForgeX follows three design principles:

• Automation — minimal user code
• Consistency — standardized preprocessing
• Compatibility — works with pandas & sklearn

It integrates directly into existing ML pipelines without replacing them.

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📊 Typical Workflow

Raw Data
   ↓
DataForgeX Preprocessing
   ↓
Clean Dataset
   ↓
Model Training
   ↓
Evaluation

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📁 Project Structure

DataForgeX/
│
├── pyproject.toml
├── README.md
├── LICENSE
│
└── src/
    └── dfx/
        ├── preprocessing.py
        ├── encoding.py
        ├── scaling.py
        ├── outliers.py
        ├── eda.py
        └── evaluation.py

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🎯 Use Cases

DataForgeX is useful for:

• Machine learning preprocessing
• Kaggle competitions
• Research experiments
• Student projects
• Rapid prototyping
• Data cleaning automation

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🤝 Contributing

Contributions are welcome.

1. Fork repository
2. Create feature branch
3. Commit changes
4. Submit pull request

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Final Summary

• Less code
• Better readability
• Faster development
• Reusable functions

DataForgeX lets you focus on logic, not boilerplate.

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Authors and Contributors

Author and Lead Developer

Prince Saxena
Creator and primary developer of DataForgeX.
Responsible for the design, architecture, and implementation of the library.

Contributors

The development of DataForgeX benefited from valuable collaboration and input from:

• Mohd Saad Sherwani — Conceptual discussions and feature ideation
• Hammad Rafeeque — Testing, feedback, and usability evaluation

Their support and insights helped refine the direction and usability of the project.

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📜 License

MIT License © 2026 Prince Saxena