This project involves the development of a Python program designed to analyze electoral data from the first round of the 2024 elections, based on .csv files provided by the Superior Electoral Court (TSE). The goal of the program is to extract meaningful insights from the data, such as votes per party, city-level results, statistical analysis, and custom reports.
The main functionalities include:
- Data reading and processing: Load and analyze large .csv files.
- Statistical analysis: Calculation of mean, mode, standard deviation, and more.
- Data filtering: Generate custom reports based on user-defined criteria (e.g., top cities with the most votes for a specific party).
- Interactive user interface: Allows users to search and generate reports based on specific parameters.
Technologies Used:
- Python 3.x
- CSV module for data handling
- Basic statistical calculations (mean, mode, standard deviation)
The project was not 100% finalized but is fully functional in terms of its core features. It was thoroughly reviewed and analyzed by Professor Dr. Rafael de Moura Speroni as part of the Algorithms and Computer Programming II course. After the analysis, the project was awarded a grade of 10 for its technical execution and functionality.
- Ability to load and process electoral data from .csv files.
- Generate reports based on user-defined criteria (e.g., parties with the most votes, city-level results).
- Statistical analysis of the data, including mean, mode, and standard deviation.
- Save filtered data to new .csv files for further analysis.
To run this project on your local machine, follow these steps:
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Once you run the program, you will be presented with an interactive menu. From there, you can choose different operations, including:
- View the number of rows and column names of the original data file: This option allows you to get an overview of the data's structure.
- Generate custom reports based on user-defined filters: You can filter the data based on criteria like top cities with the highest votes for a specific party.
- Perform statistical analysis on the data: Calculate statistics such as mean, mode, standard deviation, and other relevant measures to better understand the dataset.
- Save filtered data into new .csv files: You can save your filtered or processed data for further analysis or to share with others.
To navigate through the features, follow the on-screen instructions and input the desired options.
Although the program covers most of the expected functionalities, it has not been fully optimized or finalized. Some additional features, error handling improvements, and performance optimizations are still needed. This version may not handle very large datasets or certain edge cases perfectly.