TidyKit Workspace Structure Explanation

Created

Nov 13, 2025 1:36 AM

Root Directory Structure

/Users/teslim/MyModules/
├── tidykit/                   # Main package directory
├── sample_data/               # Sample datasets for testing
├── tidykit_demo.ipynb         # Interactive demonstration notebook
├── setup_tidykit.sh           # Automated setup script
├── requirements.txt           # Python dependencies
├── README.md                  # Project overview
├── SETUP_GUIDE.md             # Installation instructions
├── QUICK_REFERENCE.md         # Function quick reference
├── LICENSE                    # MIT License
└── .gitignore                 # Git ignore rules

tidykit/ - Main Package Directory

This is the core Python package containing all data cleaning functionality.

Files in tidykit/:

‣

1. init.py - Package Interface

‣

2. core.py - Core Functionality

‣

3. setup.py - Package Configuration

‣

4. README.md - Package Documentation

‣

5. LICENSE - Legal

‣

6. .gitignore - Git Configuration

‣

Subdirectories in tidykit/:

Set Up

import sys 

print("🔍 Current Python Module Search Paths:")
for path in sys.path:
    print(path)
 
custom_module_path = r'/Users/teslim/MyModules'

if custom_module_path not in sys.path:
    sys.path.append(custom_module_path)
    print(f"\n✅ Custom module path added: {custom_module_path}")
else:
    print(f"\n⚠️ Path already exists: {custom_module_path}")
    

# import the cleaning packages 
import tidykit as tk

Function Categories:

Column Headers: clean_column_headers(), make_unique_columns()
Numeric Data: clean_numeric_column()
Duplicates: remove_duplicates()
Missing Values: fill_missing()
Outliers: remove_outliers_iqr(), remove_outliers_zscore(), detect_outliers_iqr(), cap_outliers()
Text Processing: clean_text_column(), standardize_text_values()
Date/Time: clean_date_column(), extract_date_features()
Data Types: convert_data_types(), validate_data_ranges()
Categorical: clean_categorical_column(), encode_categorical_variables()
Quality: get_data_summary(), check_data_consistency()
Convenience: quick_clean() - one-line cleaning pipeline
Information: info() - displays comprehensive function reference

Include Quick Check on data

‣

clean_column_headers( )

‣

Component	Primary Function
tidykit/	Core package with 20+ cleaning functions
init.py	Package interface & convenience classes
core.py	All data cleaning implementations
setup.py	Package installation configuration
sample_data/	Test datasets for demonstrations
tidykit_demo.ipynb	Interactive tutorial and examples
setup_tidykit.sh	One-command automated setup
requirements.txt	Python dependencies list
README.md	Main project documentation
SETUP_GUIDE.md	Installation instructions
QUICK_REFERENCE.md	Function quick reference
LICENSE	Legal permissions (MIT)

TidyKit Workspace Structure Explanation

Root Directory Structure

tidykit/ - Main Package Directory

Files in tidykit/:

1. init.py - Package Interface

2. core.py - Core Functionality

3. setup.py - Package Configuration

4. README.md - Package Documentation

5. LICENSE - Legal

6. .gitignore - Git Configuration

Subdirectories in tidykit/:

Set Up

clean_column_headers( )

clean_numeric_column( )