semvec/pypi-packages huggingface dataset:
data-harmonization-ai-dp 2.0.0
Create data quality rules and apply them to datasets.
Requires Python: >=3.8

# Data Harmonization Package

This package provides functionality for file merging and data harmonization using various algorithms and AI models.
It allows you to merge CSV files and harmonize data based on a sample-based approach using GPT-based models.

## Installation

You can install the package from PyPI using pip:

	pip install data-harmonization-ai


## Usage

### DataHarmonizer Class

The `DataHarmonizer` class provides the capability to merge CSV files based on different options.
It supports the following merge options:

- ChatGPT
- GPT4
- Fuzzy Wuzzy
- Rapidfuzz
- Jaro Winkler
- JW Layered with ChatGPT
- JW Layered with GPT4
- FW Layered with GPT4
- Recursive Data Harmonization

Example usage:

from utility import DataHarmonizer

# Create an instance of DataHarmonizer
key='openai-key'
harmonizer = DataHarmonizer(key,'file1.csv', 'file2.csv', 'ChatGPT')

# Merge the files based on the specified option
result = harmonizer.merge_files()

print(result)

### DataHarmonizationWithSuggestion Class

# The DataHarmonizationWithSuggestion class allows you to harmonize data using a sample-based approach.
 It takes a sample file and two data files as input.

Example usage:
from utility import DataHarmonizationWithSuggestion

# Create an instance of DataHarmonizationWithSuggestion

key = 'openai-key'
harmonizer = DataHarmonizationWithSuggestion(key, "sample_harmonized_data.csv", "file1.csv", "file2.csv")

# Harmonize the data based on the sample
	result = harmonizer.harmonize_data()

print(result)
config-yml 0.3.1
Utility modules for Class management
Requires Python: >=3.6

# Config management class
Configuration management package
- Yaml based
- Can use a config template embeded in your application distribution, and a local config (in /home/var/{application})
  customized for your particular installation (that could be edited manually, or by your ansible recipe).
- Local config will be merged with the template in your application overwritting its values, allowing you to upgrade 
  and distribute your application adding new fields with default values seamlessly.
- When merging two lists, if the items inside are dicts it will identify them as equal if the field 'name' matches.
- Provides functionality not only to read the configuration, but also to modify it in runtime