https://github.com/alper-sayin/nba-database
NBA Database - A Flask-based Web Application
https://github.com/alper-sayin/nba-database
api database-migrations flask html-css-javascript nba-api orm plotly python sqlalchemy sqlite
Last synced: 7 months ago
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NBA Database - A Flask-based Web Application
- Host: GitHub
- URL: https://github.com/alper-sayin/nba-database
- Owner: alper-sayin
- Created: 2024-07-12T22:27:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-18T12:53:11.000Z (about 1 year ago)
- Last Synced: 2025-01-24T07:29:45.361Z (9 months ago)
- Topics: api, database-migrations, flask, html-css-javascript, nba-api, orm, plotly, python, sqlalchemy, sqlite
- Language: HTML
- Homepage:
- Size: 1.48 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# NBA Database
NBA Database- A Flask-based Web Application
## Table of Contents
1. [Introduction](#introduction)
2. [Key Features](#key-features)
3. [Technologies Used](#technologies-used)
4. [Project Structure](#project-structure)
5. [Installation & Setup](#installation--setup)
6. [Usage & Screenshots](#usage--screenshots)
7. [Database Structure](#database-structure)
8. [API Endpoints](#api-endpoints)
## Introduction
NBA Database is a mini framework that fetches information from different endpoints of nba_api, storing information in a database and visualizing these information in different sections of webpage.
## Key Features
- Real-time data fetching from NBA API
- Efficient data storage using SQLAlchemy (Object Relational Mapping-ORM)
- Interactive data visualizations with Plotly
- Responsive front-end design using HTML, CSS, and JavaScript
## Technologies Used
Python, Flask, SQLAlchemy, Plotly, NBA API, HTML, CSS, JavaScript, SQLite. (You can browse [requirements.txt](requirements.txt) for details)
## Project Structure
- Models: SQL Alchemy Database Models - databasemodels.py
- Views: HTML files in templates folder
- Controllers: Flask Routes - Between lines of 109-236 in app.py
- CLI Commands: Custom Flask CLI commands for database operations and data fetching - Between lines of 27-104 in app.py
## Installation & Setup
After obtaining the files in repository or [cloning the repository](git clone https://github.com/alper-sayin/NBA-Database.git) to desired folder, setting up virtual environment and satisfying the requirements [requirements.txt](requirements.txt), you should run flask CLI commands in your terminal to create then update the database (Database will be automatically created in your project folder directory)
After opening the terminal in your IDE, you should apply:
- `flask db_create` for creating the database
- `flask db_drop` for dropping the database
- `flask db_seed_teams` for seeding the database with teams
- `flask db_seed_players` for seeding the database with players
- `flask db_extend_player_info` for updating the players table with country, jersey, weight, height and position information
- `flask db_add_career_stats` for updating the players table with career stats.
- After flask CLI commands, you can run the app.py file and experience all the functionality which is explained in details in usage (screenshots of application in action) by going to http://127.0.0.1:5000 address in your internet browser
***I separated flask CLI commands for seeding the players table on purpose. So you can arbitrarily add the desired information.
Also you may want to add more columns of the current players or teams tables after the initial database creation. In this case, you should update database models in code snippet and then you should create other flask CLI commands for seeding the database.
But between seeding the database and updating database models, you should apply:
- `flask db init`
- `flask db migrate`
- `flask db upgrade`
to update the current database. That's why I used flask-migrate package in this project. But you don't need to use these flask CLI commands in final code in order to test out current functionality. If you want to add more columns or add more tables to current database **___after creation___** then you should apply these flask CLI commands.
## Usage & Screenshots
Homepage
Teams Section
Players section. Players are grouped by surnames.
Switching radio button to filter only active players
Search bar is searching for the word both in name and surname
Player details section
Career stats section
You can use “toggle graph” if you want to view some essential stats in graph
Download database section. Functionality to fetch database in both excel, csv or json(API) formats
Json(API)
Excel(.xlsx)
CSV(Pycharm view)
## Database Structure
Database structure and tables in SQLite
Teams table in SQLite
Players table in SQLite-1
Players table in SQLite-2
## API Endpoints
- nba_api.stats.static/players
- nba_api.stats.static/teams
- nba_api.stats.endpoints/commonplayerinfo*
- nba_api.stats.endpoints/playercareerstats*
*It takes a while fetching information from these endpoints for all players. So I saved as json files (nba_players.json, career_stats_of_all_players.json), added to repository and implemented the code in this way.