Perfil Politico by Okfn Brasil - A Django Template

Overview

The platform designed for profiling candidates in the Brazilian 2022 General Election is an impressive and innovative project that leverages open data to provide transparency and accessibility to the electoral process. By utilizing Docker and Docker Compose, this application allows users to easily set up a local server that showcases crucial data about electoral candidates. With functionalities that range from database creation to candidate details retrieval, this tool is a significant asset for those interested in understanding political candidates and their backgrounds.

Running the platform is straightforward, and the use of APIs to access various pieces of information makes it user-friendly. Whether you're a developer looking to experiment with open data or a citizen aiming to educate yourself about the election candidates, this project is an excellent resource.

Features

Docker Integration: Easily create and manage your application container with Docker and Docker Compose, simplifying the setup process.
Comprehensive API Access: The well-structured API allows users to retrieve information on candidates based on year, state, and post, ensuring a wide range of data is accessible.
Electoral Income History: Users can delve into a candidate's electoral income history, providing insights into their financial partnerships and economic bonds.
National Statistics: Get extensive statistics on characteristics such as age, education, and ethnicity of elected officials, offering an in-depth view of the demography of elected posts.
Data Initialization and Updating: Easily initialize your database with a few commands or update existing records, all while making sure your modifications are clear and manageable.
Mapped Data Directory: The local data directory is efficiently linked with the container, making data handling straightforward and intuitive.
Custom Django Commands: The inclusion of custom commands allows users to generate extra data manually, which can enhance their analysis and research.
User Caution Alerts: Clear warnings about potential changes to primary keys and existing data help prevent issues, especially when running the project in a production environment.