suclepy 1.0.0

SUCLEPY — Smart Universal Cleaner Library for Python

🧹 SUCLEPY — Smart Universal Cleaner Library for Python

---

✨ Overview

SUCLEPY is your one-stop Python library for smart data cleaning!
Clean data, fill missing values, remove duplicates, validate emails, normalize text, parse dates, and generate beautiful cleaning reports — all in seconds.

---

🚀 Features

• 🧹 Automatic Data Cleaning
• 🔁 Duplicate Detection & Removal
• 💧 Missing Value Handling
• 📧 Email Validation
• 📅 Date Parsing & Standardization
• ✍️ Text Normalization
• 📊 Detailed Cleaning Report
• 💾 Export Cleaned Data to CSV

---

⚙️ Installation

pip install suclepy

---

🧑‍💻 Usage Example

import suclepy as sp
import pandas as pd

# Create a dirty dataset
df = pd.DataFrame({
    "Name": ["Subodh", "Amit", "Amit", "Riya", None],
    "Age": [21, None, 22, 20, 21],
    "Join_Date": ["2024/05/10", "10-05-2024", None, "2024-05-11", "May 12, 2024"],
    "Email": ["subodh@", "[e-mail]", "[e-mail]", None, "[e-mail]"]
})

# Clean the dataset automatically
report = sp.auto_clean(df)

# View summary
print(report.summary())

# View cleaned data
print(report.head())

# Save cleaned data
report.to_csv("cleaned_dataset.csv")

---

📝 Cleaning Report Example

SUCLEPY CLEANING REPORT

Total Rows (Before): 5  
Rows After Cleaning: 5  
Duplicates Removed: 1  
Missing Values Filled: 1  
Invalid Emails Found: 1  
Standardized Columns: 4  
Status: SUCCESS ✅

---

📁 Features in Detail

Feature	Description
Automatic Cleaning	Cleans the dataset intelligently with default strategies.
Duplicate Removal	Removes repeated rows to avoid redundancy.
Missing Value Handling	Fills missing numeric data with mean/median, categorical with mode, or drops rows.
Email Validation	Detects invalid email addresses.
Date Parsing	Converts dates to a standard YYYY-MM-DD format.
Text Normalization	Capitalizes and strips unnecessary spaces.
CSV Export	Saves your cleaned data easily.
Cleaning Report	Generates a clear and printable cleaning report.

---

💡 Why SUCLEPY?

• 🧠 Friendly and easy-to-use API
• ⚡ Minimal coding required to clean messy data
• 📂 Works with any CSV or pandas DataFrame
• 📈 Generates actionable insights and visual reports

---

🔧 Configuration

You can configure global cleaning rules easily:

import suclepy as sp

sp.config({
    "fill_missing_strategy": "mean",
    "validate_email": True,
    "drop_duplicates": True
})

---

📚 Documentation & Resources

• GitHub Repository: https://github.com/subodhkryadav
• LinkedIn: Subodh Kumar Yadav

---

📝 License

This project is licensed under the MIT License.