To get started you need to have Python installed
Clone the repository
git clone https://github.com/DanielDls-exe/mid-project-euro2020.git
Use the command in the box below to install the project dependencies It is recommended to have 3 separate environments, one for the data, one for the API, and the last one for the streamlit, each folder has its own requirements.txt
cd midproject/data
pip install -r requirements.txt
cd midproject/data
pip install -r requirements.txt
cd midproject/data
pip install -r requirements.txt
Now install Jupyter-notebook
cd midproject
conda install -c conda-forge jupyterlab
#Run it locally. Go to the "data" folder
cd data
jupyter notebook
We run all the cells to do the cleaning and data extraction, you can also upload to a database
Execute the API
cd midproject/api
uvicorn main:app --reload
Executes the Streamlit
cd midproject/streamlit
streamlit run main.py
/players --> shows us the data of all the players of Euro 2020
/player/most --> Returns the player with the highest stats, you have to pass a web parameter stats = [goals, assist]
/player/most/cards --> Returns the player with the highest cards color, you have to pass a web parameter color = [red, yellow]
/player/{name} --> The data of a specific player is obtained
/players/name/all --> All players names
/player/{name}/goals --> The goals of a specific player are obtained
/player/{name}/asssit --> The assistance of a specific player are obtained
/player/{name}/cards --> Returns the cards of a specific player, you have to pass a web parameter color = [red, yellow]
/teams --> shows us the data of all the teams of Euro 2020
/team/most --> Returns the team with the highest stats, you have to pass a web parameter stats = [goalscored, goalown, possession, penaltys, shots]
/team/{team} --> The data of a specific team is obtained
/team/{team}/shots --> shots by a specific team
/team/{team}/possession --> possession scored by a specific team
/team/{team}/goals/scored --> Goals scored by a specific team
/team/{team}/goals/received --> Goals received by a specific team
/team/{team}/goals/penaltys --> Goals scored (penaltys) by a specific team
/team/name/all --> All teams names
Python 3.9, Jupyter-notebook, pandas, numpy, matplotlib.
- Daniel Alvarado - danieldls-exe
This project is under the MIT License.
⌨️ with ❤️ by danieldls-exe 😊