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Package socio4health is an extraction, transformation and loading (ETL) classification tool designed to simplify the intricate process of collecting and merging data from multiple sources, focusing on sociodemographic and census datasets from Colombia, Brazil, and Peru, into a harmonized dataset.

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socio4health

Lifecycle: maturing MIT license GitHub contributors commits

Overview

Package socio4health is an extraction, transformation and loading (ETL) classification tool designed to simplify the intricate process of collecting and merging data from multiple sources, focusing on sociodemographic and census datasets from Colombia, Brazil, and Peru, into a harmonized dataset.

  • 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, fixed-width files and geospatial files, ensuring versatility in sourcing information.
  • Consolidating extracted data into a pandas (or dask) DataFrame.

Dependencies

pandas logo Dask
Dask is a flexible parallel computing library for analytics.
pandas logo Pandas
Pandas is a well-known open source data analysis and manipulation tool.
pandas logo Geopandas
Python tools for geographic data.
numpy logo Numpy
The fundamental package for scientific computing with Python.
scrapy logo Scrapy
Framework for extracting the data you need from websites.
scrapy logo Matplotlib
Library for creating static, animated, and interactive visualizations in Python.
scrapy logo Torch
Python package for tensor computation and deep neural networks.

Installation

socio4health can be installed via pip from PyPI.

# Install using pip
pip install socio4health

How to Use it

To use the socio4health package, follow these steps:

  1. Import the package in your Python script:

    from socio4health import Extractor()
    from socio4health import Harmonizer
  2. Create an instance of the Extractor class:

    extractor = Extractor()
  3. Extract data from online sources and create a list of data information:

    url = 'https://www.example.com'
    depth = 0
    ext = 'csv'
    list_datainfo = extractor.s4h_extract(url=url, depth=depth, ext=ext)
    harmonizer = Harmonizer()

For more detailed examples and use cases, please refer to the socio4health documentation.

Resources

Package Website

The socio4health website package website includes API reference, user guide, and examples. The site mainly concerns the release version, but you can also find documentation for the latest development version.

Organisation Website

Harmonize is an international project that develops cost-effective and reproducible digital tools for stakeholders in Latin America and the Caribbean (LAC) affected by a changing climate. These stakeholders include 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

bsc logo uniandes logo

Authors / Contact information

Here is the contact information of authors/contributors in case users have questions or feedback.

Diego Irreño (developer)
Erick Lozano (developer)
Juan Montenegro (developer)
Ingrid Mora (documentation)

About

Package socio4health is an extraction, transformation and loading (ETL) classification tool designed to simplify the intricate process of collecting and merging data from multiple sources, focusing on sociodemographic and census datasets from Colombia, Brazil, and Peru, into a harmonized dataset.

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