suclepy 1.0.1

SUCLEPY — Smart Universal Cleaner Library for Python

🧹 SUCLEPY — Smart Universal Cleaner Library for Python

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✨ Overview

SUCLEPY v1.0.1 is a Smart Universal Cleaner Library for Python designed to make data cleaning automatic, fast, and reliable.
It handles missing values, duplicates, invalid emails, date formatting, string normalization, and generates detailed cleaning reports.
Perfect for data scientists, analysts, and developers working with messy datasets.

Compatible with Python 3.11+.

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🗂 Version History

v1.0.1

• String columns missing values filled with "abcd"
• Dates automatically formatted to DD-MM-YYYY
• Missing or invalid dates filled with "00-00-0000"
• Emails missing entirely filled with "[e-mail]"
• Emails with missing domain automatically corrected
• Minor improvements to cleaning report generation and performance

v1.0.0

• Automatic Cleaning: Dataset cleaned intelligently using default strategies
• Duplicate Removal: Repeated rows removed to avoid redundancy
• Missing Value Handling: Numeric missing values handled via mean/median; categorical via mode; rows can be dropped
• Email Validation: Detects invalid email addresses
• Date Parsing: Converts various date formats to standardized format
• Text Normalization: Capitalizes and strips unnecessary spaces
• CSV Export: Saves cleaned data easily
• Cleaning Report: Generates a clear and printable cleaning report

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⚙️ Installation

pip install suclepy

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🧩 Library Structure

SUCLEPY ke modules aur folder structure:

suclepy/
├── __init__.py
├── core/
│   ├── __init__.py
│   ├── cleaner.py
│   ├── analyzer.py
│   ├── role_infer.py
│   ├── validator.py
│   └── reporter.py
└── utils.py

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🧑‍💻 Usage Example

import pandas as pd
import suclepy as sp

# Create a sample dataset with 10 rows
df = pd.DataFrame({
    "Name": ["Subodh", None, "", "Amit", "Riya", None, "Riya", "Mohan", "", "Geeta"],
    "Age": [21, None, 22, 25, 20, 23, 20, None, 24, 22],
    "Join_Date": [
        "2024/05/10", None, "12-05-2024", "2024-05-11", "May 12, 2024",
        "13/05/2024", "", "2024-05-14", None, "2024/05/15"
    ],
    "Email": [
        "subodh@", None, "[e-mail]", "amit@", "", "[e-mail]",
        None, "mohan@", "[e-mail]", ""
    ],
    "City": [None, "Jaipur", "", "Delhi", None, "Mumbai", "Delhi", "", "Jaipur", "Pune"]
})

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

# Print summary and cleaned data
print("=== Cleaning Summary ===")
print(report.summary())

print("\n=== Cleaned Data ===")
print(report.head(10))  # Show all 10 rows

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

=== Cleaning Summary ===
SUCLEPY CLEANING REPORT
------------------------------
Total Rows (before): 10
Rows After Cleaning: 10
Duplicates Removed: 0
Missing Values Filled: 10
Invalid Emails Fixed: 7
Missing Strings Filled: 5
Dates Standardized: 7
Default Date Used: 3
Status: SUCCESS ✅

=== Cleaned Data ===
     Name   Age   Join_Date              Email    City
0  Subodh  21.0  05-10-2024   [e-mail]    abcd
1    abcd  22.0  00-00-0000      [e-mail]  Jaipur
2    abcd  22.0  12-05-2024   [e-mail]    abcd
3    Amit  25.0  05-11-2024     [e-mail]   Delhi
4    Riya  20.0  12-05-2024      [e-mail]    abcd
5    abcd  23.0  13-05-2024     [e-mail]  Mumbai
6    Riya  20.0  00-00-0000      [e-mail]   Delhi
7   Mohan  22.0  14-05-2024    [e-mail]    abcd
8    abcd  24.0  00-00-0000  [e-mail]  Jaipur
9   Geeta  22.0  15-05-2024      [e-mail]    Pune

File saved as cleaned_dataset.csv

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🔧 Configuration Options

import suclepy as sp

sp.config({
    "fill_missing_strategy": "mean",     # Strategy for numeric missing values
    "fill_string": "abcd",               # Fill string missing values
    "fill_date": "00-00-0000",           # Fill invalid/missing dates
    "validate_email": True,              # Validate emails
    "auto_correct_email": True,          # Auto-fix incomplete emails
    "drop_duplicates": True,             # Remove duplicate rows
    "date_format": "DD-MM-YYYY"          # Standard date format
})

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📝 Cleaning Rules / Behavior

• String Columns: missing (None, NaN, "") → "abcd"
• Dates: any format → standardized to DD-MM-YYYY, missing → "00-00-0000"
• Emails: missing → "[e-mail]", incomplete domains auto-corrected
• Numeric Columns: missing → mean or configured strategy
• Duplicates: automatically removed
• Text Normalization: capitalized and stripped extra spaces

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❓ FAQ / Notes

Q1: What happens if an email has no @ or domain?
A: Automatically corrected or filled with "[e-mail]"

Q2: How are missing dates handled?
A: Any invalid/missing date is replaced with "00-00-0000"

Q3: Can I customize fill values?
A: Yes, see Configuration Options above

Q4: Compatible Python versions?
A: Python 3.11+

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📚 Documentation & Resources

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

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📝 License

This project is licensed under the MIT License.