https://github.com/vitor-ace/sunspots-data-analysis
This is a Jupyter Notebook which works with Data Analysis logic and libraries implementation with Python.
https://github.com/vitor-ace/sunspots-data-analysis
data-analysis data-visualization debbuging error-handling file-handling matplotlib-pyplot numpy pandas python
Last synced: 2 months ago
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This is a Jupyter Notebook which works with Data Analysis logic and libraries implementation with Python.
- Host: GitHub
- URL: https://github.com/vitor-ace/sunspots-data-analysis
- Owner: VItor-Ace
- Created: 2025-09-09T17:25:56.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-09-09T17:50:22.000Z (2 months ago)
- Last Synced: 2025-09-09T21:04:28.407Z (2 months ago)
- Topics: data-analysis, data-visualization, debbuging, error-handling, file-handling, matplotlib-pyplot, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 425 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Sunspots-Data-Analysis
This is a Jupyter Notebook which works with Data Analysis logic and libraries implementation with Python. The code receives two .txt files containing sunspot information in two different categories.
The first one gathers daily sunspots data from 1888 up to 2025, while the other gathers monthly sunspots data from 1749 up to 2025. This project required a good knowledge on data libraries, such as Pandas, NumPy and Matplotlib (for visual output), error-handling, code optimization and data treatment.
## Menu
It is initially presented a menu showing all features and analysis visualization/information the code provides. The options were organized according to importance (oon my own opinion). However, each one of them is related with one or more specific functions, at the same time, a connsiderable amount of them are dependent of previous functions - specially the more complex ones - so, there was needed a efficient architecture and organization to make available a good interpretation of the code and its functionalities.
In this process, it was also required an error-handling knowledge, since it could be possible to have invalid type objects or invalid inputs regarding an specific analysis year range.
## Logical Strategies
In the Jupyter Notebook, there are some contents regarding the logical construction and problem solving strategy used to meet all demands on the project. Thus, there is a detailed logical construction used on this project.
# Conclusion
This project was a great real world example for the students to put on practice all the knowledge gathered throughout the course, working with files handling, basic structures, huge logic and debugging concepts, libraries usage - data work and graph plot -, and correlation between functions wrote in the code, which demonstrates facilities of a good work architecture.