socio4health 0.1.4

Socio4health is a Python package for gathering and consolidating socio-demographic data.

socio4health

Overview

Package socio4health is an extraction, transformation, loading (ETL) and AI-assisted query and visualization (AI QV) tool designed to simplify the intricate process of collecting and merging data from multiple sources focusing in sociodemografic and census datasets from Colombia, Brasil and Peru, into a unified relational database structure and visualize or querying it using natural language.

• Seamlessly retrieve data from online data sources through web scraping, as well as from local files.
• Support for various data formats, including .csv, .xlsx, .xls, .txt, .sav, and compressed files, ensuring versatility in sourcing information.
• Consolidating extracted data into pandas DataFrame.
• Consolidating transformed data into a cohesive relational database.
• Conduct precise queries and apply transformations to meet specific criteria.
• Using natural language input to query data (Answers from values to subsets)
• Using natural language input to create simple visualizations of data

Dependencies

	Pandas Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.
	Numpy The fundamental package for scientific computing with Python.
	Scrapy Framework for extracting the data you need from websites.
	Pandasai Integrates generative artificial intelligence capabilities into pandas, making dataframes conversational.

• openpyxl
• py7zr
• pyreadstat
• tqdm
• requests

Installation

You can install the latest version of the package from GitHub using the remotes package:

# Install using pip
pip install nyctibius

How to Use it

To use the Nyctibius package, follow these steps:

1. Import the package in your Python script:

   from socio4health import Harmonizer
   

2. Create an instance of the Harmonizer class:

   harmonizer = Harmonizer()
   

3. Extract data from online sources and create a list of data information:

   url = 'https://www.example.com'
   depth = 0
   ext = 'csv'
   list_datainfo = harmonizer.extract(url=url, depth=depth, ext=ext)
   harmonizer = Harmonizer(list_datainfo)
   

4. Load the data from the list of data information and merge it into a relational database:

   results = harmonizer.load()
   

5. Import the modifier module and create an instance of the Modifier class:

   from socio4health.db.modifier import Modifier
   modifier = Modifier(db_path='../../data/output/nyctibius.db')
   

6. Perfom modifications:

   tables = modifier.get_tables()
   print(tables)
   

7. Import the querier module and create an instance of the Querier class:

   from socio4health.db.querier import Querier
   querier = Querier(db_path='data/output/socio4health.db')
   

8. Perform queries:

   df = querier.select(table="Estructura CHC_2017").execute()
   print(df)
   

Resources

Package Website

The socio4health website package website includes a function reference, a model outline, and case studies using the package. The site mainly concerns the release version, but you can also find documentation for the latest development version.

Organisation Website

Harmonize is an international develop cost-effective and reproducible digital tools for stakeholders in hotspots affected by a changing climate in Latin America & the Caribbean (LAC), including cities, small islands, highlands, and the Amazon rainforest.

The project consists of resources and tools developed in conjunction with different teams from Brazil, Colombia, Dominican Republic, Peru and Spain.

Organizations

Authors / Contact information

List the authors/contributors of the package and provide contact information if users have questions or feedback.

Diego Irreño (developer)
Erick Lozano (developer)