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Get up and running with the Premier League library in under 5 minutes.

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Installation

Install the library using pip: 
pip install premier_league
The library automatically initializes a local SQLite database on first use.

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Your first queries

1

Fetch league standings

Get the current Premier League table:
from premier_league import RankingTable

# Get current season standings
ranking = RankingTable()
standings = ranking.get_ranking_list()

# Display top 5 teams
print(standings[0])  # Header row
for team in standings[1:6]:
    print(team)
Output:
['Position', 'Team', 'MP', 'W', 'D', 'L', 'GF', 'GA', 'GD', 'Points']
['1', 'Manchester City', '38', '32', '4', '2', '102', '31', '71', '100']
['2', 'Arsenal', '38', '28', '5', '5', '88', '43', '45', '89']
...
2

Query player statistics

Get the top goalscorers:
from premier_league import PlayerSeasonLeaders

# Get top 10 goalscorers
scorers = PlayerSeasonLeaders(stat_type='G')
top_scorers = scorers.get_top_stats_list(limit=10)

# Display results
for player in top_scorers[1:]:  # Skip header
    print(f"{player[0]} ({player[2]}): {player[3]} goals")
Output:
Erling Haaland (Manchester City): 36 goals
Harry Kane (Tottenham Hotspur): 30 goals
Ivan Toney (Brentford): 20 goals
...
3

Access match statistics

Get detailed match data for analysis:
from premier_league import MatchStatistics
from datetime import datetime

# Initialize match statistics
stats = MatchStatistics()

# Get Arsenal's recent games
arsenal_games = stats.get_team_games("Arsenal")

# Get games before a specific date
recent_games = stats.get_games_before_date(
    date=datetime(2024, 2, 1),
    limit=5,
    team="Liverpool"
)

print(f"Found {len(arsenal_games)} Arsenal games")
4

Check transfer activity

View player transfers for a team:
from premier_league import Transfers

# Initialize transfers for current season
transfers = Transfers()

# Get Chelsea's incoming transfers
chelsea_ins = transfers.transfer_in_table("Chelsea")

# Display transfers
print(chelsea_ins[0])  # Header
for transfer in chelsea_ins[1:]:
    print(transfer)

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Multi-league support

The library supports data from multiple European leagues: 
from premier_league import RankingTable

ranking = RankingTable(league="Premier League")
standings = ranking.get_ranking_list()

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Export data

All modules support exporting data in multiple formats: 
from premier_league import RankingTable, PlayerSeasonLeaders

# Export standings to CSV
ranking = RankingTable()
ranking.get_ranking_csv("standings_2024", header="Premier League 2023-24")

# Export top scorers to JSON
scorers = PlayerSeasonLeaders(stat_type='G')
scorers.get_top_stats_json("top_scorers", header="Goals_2024")

# Export to PDF (requires premier_league[pdf])
ranking.get_ranking_pdf("standings_2024")

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Update data

Keep your local database current with the latest matches: 
from premier_league import MatchStatistics

stats = MatchStatistics()

# Update with latest match data
stats.update_data_set()
The update_data_set() method takes considerable time due to rate limiting (4 seconds between requests) to respect data source restrictions.

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Next steps

Core modules

Explore MatchStatistics, RankingTable, PlayerSeasonLeaders, and Transfers

Machine learning

Create ML-ready datasets with lag-based features

Flask API

Deploy as a REST API with rate limiting and caching

Data export

Export data to CSV, JSON, and PDF formats