This project was created to perform a data transformation and data analysis with python.
- 🐋Docker
- 💻VsCode
💡 Attention
This project employs Docker Dev Containers, ensuring that all operations occur within containers. Therefore, it is imperative to have Docker installed on your system.
It is highly recommended to utilize Visual Studio Code (VsCode), as the MS Dev Container plugin facilitates seamless integration with the development environment. Please ensure that you have Docker installed and consider installing the VsCode Dev Containers plugin for an enhanced experience.
Since Docker and Dev Containers are in use, there is no need to directly install Python on your local machine. The Dev Container for this project is preconfigured to install Python, Poetry, and all necessary dependencies internally.
Sit back and let the informagic work its magic.
The application has the following functionality:
The main.ipynb contains analyses that address the posed questions for this project.
Distributed under the MIT License. See LICENSE for more information.
- 1 - Make a descriptive analysis of the columns.
- 2 - Display the data types of each column.
- 3 - Check for empty values, if any, fill them with the mean.
- 4 - Answer: Which teams scored the most goals?
- 5 - Answer: Which teams had the most assists?
- 6 - Answer: Which teams had the highest total number of goal attempts?
- 7 - Present graphs for questions 4, 5, and 6 with a limit of 5 selections.