dtbag 3.1.0

Data Tool Bag (dtbag) - A Python library for text processing, data cleaning, and similarity-based clustering of textual data

dtbag: Data Tools for Business, Analytics & General Tasks

Streamline your data preparation for Machine Learning and Business Analytics.

dtbag is a Python library that focuses on creating solutions to common problems, and what distinguishes it from others is that it adopts a "solution in one line" approach, meaning it combines all the necessary steps to work internally and returns the result in a single output. For instance "CatUnifier" function solves a common categorical data problem, inconsistent categorical data due to incorrect entries, spelling mistakes, different write/map data, etc... data might have only four real categories but the output can show two times or even many times that number, and this will affect any charts/ machine learning models etc.. and return wrong results.

Clean inconsistent data entries

• data = ["Pencil", "Parrise", "Pencle", "Pris", "PParis" "pencl", "pencyl", "Paris"]
• clean_data = CatUnifier(data, threshold=0.7)
• Result: ["Pencil", "Paris"]

Key Features

• Smart Text Unification : Intelligently groups and standardizes similar text entries (like names, addresses, product titles) even with typos, different cases, or accents.
• Built for Real Data : Handles common data issues like inconsistent capitalization ("New York", "NEW YORK"), diacritics ("Café", "Cafe"), and minor spelling variations.
• Multilingual Support : Works seamlessly across languages commonly found in business data, including Arabic, English, French, Spanish, German, and more.
• Zero Dependencies : Uses only Python's robust standard library, ensuring lightweight and conflict-free installation.
• Preserves Original Data : Returns the most frequent original version in each group, maintaining data integrity.

What's Inside:

• CatLists : Returns a list containing the corrected unique categories from the erroneous categories.
• CatUnifier : Returns the entire input list as a new list with fully corrected categories and in the same order as entered, so that it can be replaced directly.

Quick Start

Installation

pip install dtbag

Basic Usage: Cleaning Text Data

from dtbag import CatUnifier
from dtbag import CatLists

raw_customers = ["Café Marrakech", "CAFÉ MARRAKECH", 'Café Mrakech', 'Cafe Marakesh', 'Cafffé Marrakech', 'Cfé Markech', 'Sturbuks', 'Starbucks', "Starbucks", "starbucks"]
CatUnifier(raw_customers, threshold=0.7)
Output: ['Café Marrakech', 'Café Marrakech', 'Café Marrakech', 'Café Marrakech', 'Café Marrakech', 'Café Marrakech', 'Starbucks', 'Starbucks', 'Starbucks', 'Starbucks']

products = ["laptop-13inch", "Laptop 13 Inch", "Laptop 13\"", "mouse wirlss", "Mouse Wireless"]
CatUnifier(products, threshold=0.75)
Output:["Laptop 13 Inch", "Laptop 13 Inch", "Laptop 13 Inch", "Mouse Wireless", "Mouse Wireless"]

international = ["São Paulo", "Sao Paulo", "Saw Paullow", "München", "Muenchen", "Naïve", "Naive", "Nayvee"]
CatUnifier(international, threshold=0.7)
Output:["São Paulo", "São Paulo", "São Paulo", "München", "München", "Naïve", "Naïve", "Naïve"]

Tuning Precision

strict_cleaning = unify_similar_items(data, threshold=0.9)  # High precision
lenient_cleaning = unify_similar_items(data, threshold=0.6) # More grouping