spongepy 0.1.2

get your data cleaned in seconds!

SpongePy - Data Processing Toolkit 🧽

A versatile Python CLI tool for cleaning, analyzing, and transforming structured data. Designed for data professionals who need quick data wrangling capabilities without spreadsheet software.

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Features 🌟

🔄 Data Cleaning

• Auto-clean phone numbers, IDs, and names
• Handle missing data with smart strategies:
  • Delete columns with >55% missing values
  • Fill numeric columns with mean/median
  • Multiple threshold configurations
• Text normalization (case conversion, special characters)

📊 Data Analysis

• Generate comprehensive reports:
  • Missing value analysis
  • Statistical summaries
  • Column type detection
  • Character distribution in text columns

📁 Multi-Format Support

• Input: CSV, Excel, JSON, Parquet, SQLite, Stata, Feather, Pickle
• Output: All input formats + HTML, XML (with consistent schema)

Installation 💻

pip install spongepy

Usage 🛠️

Basic Command Structure

spongepy --file <input> [OPTIONS]

Key Options:

Option	Description
-f/--file	Input file (required)
--clean	Enable auto-cleaning
-c/--config	Use custom cleaning config (JSON)
-s/--stats	Show summary statistics
-d/--details	Show detailed column analysis
-e/--export	Export cleaned data (specify filename)

Configuration Example 🧠

Generate config template:

spongepy -f data.csv --details  # Creates config.json
Sample config actions:

exemple config.json : 
{
  "missing-data": [
    ["Age", "4.2%", "mean"],
    ["CreditScore", "12.1%", "median"]
  ],
  "phone-number": "Contact",
  "name": ["FirstName", "LastName"],
  "text-columns": [
    {
      "column": "Notes",
      "special": "!?,.-",
      "numbers": ""  // Remove all digits if exists
    }
  ]
}

Common Workflows 🔄

Quick Clean & Export

spongepy -f dirty_data.xlsx --clean --export clean_data.parquet

Generate Data Health Report

spongepy -f customer_db.sqlite --details > report.txt

Custom Pipeline { }

Generate config:

spongepy -f raw.csv --details

Edit config.json

rules are specified in config.json under 'Guide'

Run targeted clean:

spongepy -f raw.csv --clean -c config.json -e cleaned.csv

Technical Specs ⚙️

Data Cleaning Logic Missing Data % Action 5%< Drop rows 5%-55% Mean/median imputation 80%< Column removal

Supported Text Operations

Case normalization

Special character whitelisting

Digit removal

Custom regex patterns

Support & Contribution 🤝

Found a bug? Want a new feature?

Open an Issue

Contribution Guidelines

License: MIT Version: 1.1.0 Compatibility: Python 3.8+