Skip to main content
The Premier League library provides flexible export options across all modules. This guide shows you how to export data in various formats for analysis, reporting, and sharing.

​
Export Formats Overview

All modules support multiple export formats:
ModuleCSVJSONPDFDictionary
MatchStatistics✅❌❌❌
RankingTable✅✅✅✅
PlayerSeasonLeaders✅✅✅❌
Transfers✅✅❌❌

​
CSV Export

CSV exports are supported across all modules and are ideal for data analysis in Excel, pandas, or R.

​
Match Statistics (ML Dataset)

Export match data formatted for machine learning: 
from premier_league import MatchStatistics

stats = MatchStatistics()

# Export full dataset
stats.create_dataset(
    output_path="ml_data/matches.csv",
    lag=10,
    weights="exp",
    params=0.9
)
Output format: 
game_id,date,season,match_week,home_team_id,away_team_id,home_team,away_team,home_xG,home_shots_total_FW,away_xG,away_shots_total_FW,...,home_goals,away_goals
ab123cd,2024-01-15,2023-2024,20,team1,team2,Arsenal,Liverpool,2.3,15,1.8,12,...,2,1
The create_dataset() method automatically places target columns (home_goals, away_goals) at the end for ML convenience.

​
Ranking Tables

Export league standings to CSV: 
from premier_league import RankingTable

# Export current Premier League standings
ranking = RankingTable(league="Premier League")
ranking.get_ranking_csv(
    file_name="premier_league_standings",
    header="Premier League 2023-2024 Standings"
)
# Creates: premier_league_standings.csv
Output format: 
Premier League 2023-2024 Standings
Pos,Team,Pld,W,D,L,GF,GA,GD,Pts
1,Arsenal,38,28,5,5,88,43,+45,89
2,Manchester City,38,27,5,6,96,34,+62,86

​
Player Statistics

Export top scorers or assist leaders: 
from premier_league import PlayerSeasonLeaders

# Export top 20 goal scorers
scorers = PlayerSeasonLeaders(
    stat_type="G",
    target_season="2023-2024",
    league="Premier League"
)

scorers.get_top_stats_csv(
    file_name="top_scorers_2024",
    header="Premier League 2023-2024 Top Scorers",
    limit=20  # Top 20 players only
)
Parameters:
  • file_name (str): Filename without extension
  • header (str, optional): Header row for the CSV
  • limit (int, optional): Number of players to include

​
Transfer Data

Export team transfers: 
from premier_league import Transfers

transfers = Transfers(target_season="2023-2024")

# Export both in and out transfers
transfers.transfer_csv(
    team="Chelsea",
    file_name="chelsea_transfers_2024",
    transfer_type="both"  # or "in" or "out"
)
Transfer types:
  • "both": Creates file with both incoming and outgoing transfers
  • "in": Only incoming transfers
  • "out": Only outgoing transfers
Output format: 
Chelsea 2023-2024 Transfers In
Date,Name,Position,Club
08/15,Moises Caicedo,Midfielder,Brighton & Hove Albion
08/20,Cole Palmer,Forward,Manchester City

Chelsea 2023-2024 Transfers Out
Date,Name,Position,Club
07/02,Kai Havertz,Forward,Arsenal
07/15,Mason Mount,Midfielder,Manchester United

​
JSON Export

JSON exports are perfect for web applications, APIs, and JavaScript-based analysis.

​
Ranking Tables as JSON

ranking = RankingTable(league="La Liga", target_season="2023-2024")

ranking.get_ranking_json(
    file_name="la_liga_standings",
    header="La Liga Standings"
)
Output format: 
{
  "La Liga Standings": [
    ["Pos", "Team", "Pld", "W", "D", "L", "GF", "GA", "GD", "Pts"],
    ["1", "Real Madrid", "38", "29", "8", "1", "87", "26", "+61", "95"],
    ["2", "Barcelona", "38", "27", "7", "4", "79", "36", "+43", "88"]
  ]
}

​
Player Statistics as JSON

assist_leaders = PlayerSeasonLeaders(
    stat_type="A",
    league="Serie A"
)

assist_leaders.get_top_stats_json(
    file_name="serie_a_assists",
    header="Serie A Assist Leaders",
    limit=10
)

​
Transfer Data as JSON

transfers = Transfers(
    target_season="2023-2024",
    league="Bundesliga"
)

transfers.transfer_json(
    team="Bayern Munich",
    file_name="bayern_transfers",
    transfer_type="in"
)

​
Dictionary Export

Get data as Python dictionaries for in-memory processing: 
ranking = RankingTable(league="Premier League")

# Get as dictionary
standing_dict = ranking.get_ranking_dict(
    header="Current Standings"
)

# Use in your application
for row in standing_dict["Current Standings"]:
    print(f"{row[1]} has {row[8]} points")  # Team name and points
Use cases:
  • Building web APIs
  • Creating custom visualizations
  • Feeding data to other Python libraries
  • Real-time data processing

​
PDF Export

PDF exports create beautifully formatted reports with color-coding and professional styling.
PDF export requires the optional reportlab dependency. Install with:
pip install premier_league[pdf]

​
Ranking Tables as PDF

ranking = RankingTable(
    league="Premier League",
    target_season="2023-2024"
)

ranking.get_ranking_pdf(
    file_name="premier_league_2024",
    dir="reports"  # Output directory
)
# Creates: reports/premier_league_2024.pdf
PDF Features:
  • Color-coded rows:
    • Light green: Champions League spots (top 4)
    • Yellow-green: Europa League spots
    • Green: Europa Conference League spot
    • Red: Relegation zone (bottom 3)
  • Centered title: League name and season
  • Formatted table: Clean grid with proper alignment
  • A3 page size: Fits all columns comfortably

​
Player Statistics as PDF

scorers = PlayerSeasonLeaders(
    stat_type="G",
    target_season="2023-2024"
)

scorers.get_top_stats_pdf(
    file_name="top_scorers",
    path="reports/players"
)
PDF Features for Players:
  • Top 20 players only
  • Gold highlighting for #1 scorer
  • Includes player name, country, club, and statistics
  • Professional formatting for presentations

​
Export Workflows

​
Complete Season Analysis Export

1

Initialize all modules

from premier_league import MatchStatistics, RankingTable, PlayerSeasonLeaders, Transfers

season = "2023-2024"
league = "Premier League"
2

Create output directory

import os

output_dir = f"exports/{season.replace('-', '_')}"
os.makedirs(output_dir, exist_ok=True)
os.makedirs(f"{output_dir}/csv", exist_ok=True)
os.makedirs(f"{output_dir}/json", exist_ok=True)
os.makedirs(f"{output_dir}/pdf", exist_ok=True)
3

Export ML dataset

stats = MatchStatistics()
stats.create_dataset(
    output_path=f"{output_dir}/csv/ml_dataset.csv",
    lag=10,
    weights="exp",
    params=0.9
)
4

Export rankings

ranking = RankingTable(league=league, target_season=season)
ranking.get_ranking_csv(f"{output_dir}/csv/standings", header=f"{league} {season}")
ranking.get_ranking_json(f"{output_dir}/json/standings", header=f"{league} {season}")
ranking.get_ranking_pdf("standings", dir=f"{output_dir}/pdf")
5

Export player statistics

# Goal scorers
scorers = PlayerSeasonLeaders(stat_type="G", target_season=season, league=league)
scorers.get_top_stats_csv(f"{output_dir}/csv/top_scorers", limit=20)
scorers.get_top_stats_json(f"{output_dir}/json/top_scorers", limit=20)
scorers.get_top_stats_pdf("top_scorers", path=f"{output_dir}/pdf")

# Assist leaders
assists = PlayerSeasonLeaders(stat_type="A", target_season=season, league=league)
assists.get_top_stats_csv(f"{output_dir}/csv/top_assists", limit=20)
assists.get_top_stats_json(f"{output_dir}/json/top_assists", limit=20)
assists.get_top_stats_pdf("top_assists", path=f"{output_dir}/pdf")

​
Multi-League Comparison Export

leagues = ["Premier League", "La Liga", "Serie A", "Bundesliga", "Ligue 1"]
season = "2023-2024"

for league in leagues:
    # Export rankings for each league
    ranking = RankingTable(league=league, target_season=season)
    
    league_slug = league.lower().replace(" ", "_")
    ranking.get_ranking_csv(
        file_name=f"exports/comparison/{league_slug}_standings",
        header=f"{league} {season}"
    )
    
    # Export top scorers
    scorers = PlayerSeasonLeaders(stat_type="G", league=league, target_season=season)
    scorers.get_top_stats_csv(
        file_name=f"exports/comparison/{league_slug}_scorers",
        limit=10
    )

print("Multi-league export completed!")

​
Team-Specific Report

def export_team_report(team_name, season, output_dir):
    """Generate complete report for a specific team"""
    import os
    
    # Create team directory
    team_dir = f"{output_dir}/{team_name.replace(' ', '_').lower()}"
    os.makedirs(team_dir, exist_ok=True)
    
    # Export transfers
    transfers = Transfers(target_season=season)
    transfers.transfer_csv(
        team=team_name,
        file_name=f"{team_dir}/transfers",
        transfer_type="both"
    )
    transfers.transfer_json(
        team=team_name,
        file_name=f"{team_dir}/transfers",
        transfer_type="both"
    )
    
    # Export team's match history
    stats = MatchStatistics()
    team_games = stats.get_team_games(team_name)
    
    # Save to JSON
    import json
    with open(f"{team_dir}/match_history.json", "w") as f:
        json.dump(team_games, f, indent=2)
    
    print(f"Report generated for {team_name} in {team_dir}")

# Usage
export_team_report("Liverpool", "2023-2024", "team_reports")

​
Best Practices

Include league, season, and data type in filenames:
# Good
"premier_league_2023_2024_standings.csv"
"serie_a_top_scorers_2024.json"

# Avoid
"data.csv"
"export1.json"
Create timestamped directories for historical tracking:
from datetime import datetime

timestamp = datetime.now().strftime("%Y%m%d")
export_dir = f"exports/{timestamp}"
os.makedirs(export_dir, exist_ok=True)
Always check that files were created successfully:
import os

export_path = "data/standings.csv"
ranking.get_ranking_csv("data/standings")

if os.path.exists(f"{export_path}"):
    file_size = os.path.getsize(f"{export_path}")
    print(f"Export successful: {file_size} bytes")
else:
    print("Export failed!")
Wrap exports in try-except blocks:
try:
    ranking.get_ranking_pdf("standings", dir="reports")
    print("PDF generated successfully")
except ImportError:
    print("reportlab not installed. Use: pip install premier_league[pdf]")
except Exception as e:
    print(f"Export failed: {e}")

​
Export Format Comparison

​
When to Use Each Format

CSV: Best for…
  • Data analysis in Excel, pandas, R
  • Importing into databases
  • Maximum compatibility
  • Large datasets
JSON: Best for…
  • Web applications and APIs
  • JavaScript-based visualization
  • Nested/hierarchical data
  • Modern app integration
PDF: Best for…
  • Presentations and reports
  • Sharing with non-technical users
  • Print-ready documents
  • Professional formatting
Dictionary: Best for…
  • In-memory processing
  • Python-only workflows
  • Real-time applications
  • Custom data transformations

​
Troubleshooting

​
PDF Export Fails

Error: ModuleNotFoundError: No module named 'reportlab' Solution: 
pip install premier_league[pdf]
# or
pip install reportlab

​
Empty CSV Files

Cause: No data available for the specified parameters Solution: 
# Check data exists first
data = ranking.get_ranking_list()
if len(data) > 1:  # More than just header
    ranking.get_ranking_csv("standings")
else:
    print("No data to export")

​
File Permission Errors

Error: PermissionError: [Errno 13] Permission denied Solution:
  • Ensure the output directory exists and is writable
  • Close any files that might be open in other applications
  • Check file system permissions
import os

output_dir = "exports"
if not os.path.exists(output_dir):
    os.makedirs(output_dir, mode=0o755)
You now know how to export Premier League data in all supported formats!

Build docs developers (and LLMs) love

Get started for freeTalk to us