database-operations

What problem does it solve?

This Skill simplifies PostgreSQL data interaction by providing a unified DBManager interface for common operations, reducing ad-hoc queries and promoting consistent data access patterns across the betting analytics stack.

Core Features & Use Cases

• Fetch data as a DataFrame using the default connection (e.g., unified_games across sports).
• Execute updates and transactional changes (e.g., marking bets as won/lost) via parameterized queries.
• Upsert and insert data from DataFrames into key tables like unified_games, game_odds, placed_bets, elo_ratings, and portfolio_snapshots.
• Use Case: Data analysts fetch historical game data, perform analytics, and feed dashboards or models with consistent data.

Quick Start

Run a quick fetch with the default DB connection: df = default_db.fetch_df("SELECT * FROM unified_games WHERE sport = 'nba'") print(df.head())