socio4health
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
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Dask Dask is a flexible parallel computing library for analytics. |
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Pandas Pandas is a well-known open source data analysis and manipulation tool. |
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Geopandas Python tools for geographic data. |
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Numpy The fundamental package for scientific computing with Python. |
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Scrapy Framework for extracting the data you need from websites. |
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Matplotlib Library for creating static, animated, and interactive visualizations in Python. |
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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:
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Import the package in your Python script:
from socio4health import Extractor() from socio4health import Harmonizer
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Create an instance of the
Extractorclass:extractor = Extractor()
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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
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Authors / Contact information
Here is the contact information of authors/contributors in case users have questions or feedback.
1 maintainer
Release files for socio4health 1.0.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| socio4health-1.0.8.tar.gz | 38.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| socio4health-1.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 70.1 kB
Release files / socio4health-1.0.8.tar.gz
| Download URL | socio4health-1.0.8.tar.gz |
|---|---|
| Size | 38.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.12.13
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Release files / socio4health-1.0.8-py3-none-any.whl
| Download URL | socio4health-1.0.8-py3-none-any.whl |
|---|---|
| Size | 31.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.13
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