Here's a basic schema to get you started:
def insert_data(conn): cur = conn.cursor() # Inserting sample data cur.execute("INSERT INTO Teams (TeamID, TeamName, Country) VALUES (1, 'Team A', 'Country A')") cur.execute("INSERT INTO Teams (TeamID, TeamName, Country) VALUES (2, 'Team B', 'Country B')") cur.execute("INSERT INTO Players (PlayerID, FirstName, LastName, DateOfBirth, TeamID) VALUES (1, 'John', 'Doe', ?, 1)", (date(1990, 1, 1),)) cur.execute("INSERT INTO Matches (MatchID, MatchDate, Team1ID, Team2ID, Score1, Score2) VALUES (1, ?, 1, 2, 2, 1)", (date(2022, 1, 1),)) conn.commit() jfjelstul worldcup sqlite
for file in csv_files: # Read the CSV df = pd.read_csv(os.path.join(csv_folder, file)) Here's a basic schema to get you started:
In this post, we’ll explore what this dataset is, why it’s useful, and how to instantly load it into a local SQLite database to start querying match data in under five minutes. Country) VALUES (1
That is why the is a hidden gem for sports analysts and SQL practitioners.