### Fetch Entry Gameweek Picks - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets the squad selection (picks) and captain information for a specific team across multiple gameweeks. Uses the `get_entry_gws_data` function, requiring team ID, number of gameweeks, and start gameweek. Output includes points and captain details per gameweek.

```python
from getters import get_entry_gws_data

# Fetch gameweek picks for team 4582 from GW1 to GW10
team_id = 4582
num_gws = 10
start_gw = 1

gw_data = get_entry_gws_data(team_id, num_gws, start_gw)

# Examine picks for each gameweek
for gw in gw_data:
    event = gw['entry_history']['event']
    total_points = gw['entry_history']['points']
    picks = gw['picks']

    print(f"\nGW{event}: {total_points} points")
    print(f"Captain: Player ID {[p for p in picks if p['is_captain']][0]['element']}")
    print(f"Players: {len(picks)}")
```

--------------------------------

### Fetch Entry Gameweek Picks

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets the squad selection and transfers for a specific team across multiple gameweeks. This provides insight into a manager's strategic decisions.

```APIDOC
## Fetch Entry Gameweek Picks

### Description
Gets the squad selection and transfers for a specific team across multiple gameweeks.

### Method
GET

### Endpoint
/api/entry/{entry_id}/event/{event_id}/picks/

### Parameters
#### Path Parameters
- **entry_id** (integer) - Required - The unique identifier for the FPL team/entry.
- **event_id** (integer) - Required - The gameweek ID for which to fetch picks.

### Request Example
```python
from getters import get_entry_gws_data

# Fetch gameweek picks for team 4582 from GW1 to GW10
team_id = 4582
num_gws = 10
start_gw = 1

gw_data = get_entry_gws_data(team_id, num_gws, start_gw)

# Examine picks for each gameweek
for gw in gw_data:
    event = gw['entry_history']['event']
    total_points = gw['entry_history']['points']
    picks = gw['picks']

    print(f"\nGW{event}: {total_points} points")
    print(f"Captain: Player ID {[p for p in picks if p['is_captain']][0]['element']}")
    print(f"Players: {len(picks)}")
```

### Response
#### Success Response (200)
- **picks** (array) - An array of objects representing the selected players for the gameweek.
- **entry_history** (object) - Historical performance data for the entry in that gameweek.

#### Response Example
```json
[
  {
    "picks": [
      {
        "element": 10,
        "is_captain": true,
        "is_vice_captain": false
      }
    ],
    "entry_history": {
      "event": 1,
      "points": 60,
      "overall_rank": 5000
    }
  }
]
```
```

--------------------------------

### Fetch Fixtures Data

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets fixture schedule and results for all Premier League matches. This endpoint provides information on upcoming and past fixtures.

```APIDOC
## Fetch Fixtures Data

### Description
Gets fixture schedule and results for all Premier League matches.

### Method
GET

### Endpoint
/api/fixtures/

### Parameters
This endpoint does not require any specific parameters.

### Request Example
```python
from getters import get_fixtures_data

fixtures = get_fixtures_data()

for fixture in fixtures[:5]:  # First 5 fixtures
    print(f"GW{fixture['event']}: Team {fixture['team_h']} vs Team {fixture['team_a']}")
    if fixture['finished']:
        print(f"  Score: {fixture['team_h_score']}-{fixture['team_a_score']}")
    else:
        print(f"  Kickoff: {fixture['kickoff_time']}")
```

### Response
#### Success Response (200)
- **fixtures** (array) - An array of objects, each representing a fixture.

#### Response Example
```json
[
  {
    "event": 1,
    "team_h": 1,
    "team_a": 2,
    "team_h_score": 2,
    "team_a_score": 1,
    "finished": true,
    "kickoff_time": "2023-08-11T19:00:00Z"
  }
]
```
```

--------------------------------

### Scrape Complete Team Data - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Downloads all historical data for a specific FPL team/manager for a given season, starting from a specified gameweek. It creates a dedicated output folder containing various CSV files like gws.csv, chips.csv, and more.

```python
from teams_scraper import store_data
import os

# Scrape data for team 4582, season 24_25, starting from GW1
team_id = 4582
season = '24_25'
start_gw = 1
output_folder = f'team_{team_id}_data{season}'

if not os.path.exists(output_folder):
    os.makedirs(output_folder)

store_data(team_id, output_folder, start_gw)
# Creates:
#   team_4582_data24_25/gws.csv          - GW-by-GW performance
#   team_4582_data24_25/chips.csv        - Chips used
#   team_4582_data24_25/history.csv      - Past seasons
#   team_4582_data24_25/classic_leagues.csv
#   team_4582_data24_25/h2h_leagues.csv
#   team_4582_data24_25/transfers.csv
#   team_4582_data24_25/picks_*.csv      - Team selection for each GW
```

--------------------------------

### Fetch Fixtures Data - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets the fixture schedule and results for all Premier League matches. Uses the `get_fixtures_data` function. The output includes gameweek, participating teams, and scores if the match has finished, or kickoff time if not.

```python
from getters import get_fixtures_data

fixtures = get_fixtures_data()

for fixture in fixtures[:5]:  # First 5 fixtures
    print(f"GW{fixture['event']}: Team {fixture['team_h']} vs Team {fixture['team_a']}")
    if fixture['finished']:
        print(f"  Score: {fixture['team_h_score']}-{fixture['team_a_score']}")
    else:
        print(f"  Kickoff: {fixture['kickoff_time']}")
```

--------------------------------

### Access FPL Data with Pandas in Python

Source: https://github.com/vaastav/fantasy-premier-league/blob/master/README.md

This snippet demonstrates how to access FPL data directly from the repository using Python and the pandas library. It reads a specified CSV file (e.g., merged_gw.csv) into a pandas DataFrame. Ensure you have pandas installed (`pip install pandas`).

```python
import pandas as pd

# URL of the CSV file (example)
url = "https://raw.githubusercontent.com/vaastav/Fantasy-Premier-League/master/data/2023-24/gws/merged_gw.csv"

# Read the CSV file into a pandas DataFrame
df = pd.read_csv(url)
```

--------------------------------

### Fetch Individual Player Detailed Data

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets gameweek-by-gameweek history and fixture data for a specific player. This is useful for in-depth analysis of a single player's performance and upcoming matches.

```APIDOC
## Fetch Individual Player Detailed Data

### Description
Gets gameweek-by-gameweek history and fixture data for a specific player.

### Method
GET

### Endpoint
/api/element-summary/{player_id}/

### Parameters
#### Path Parameters
- **player_id** (integer) - Required - The unique identifier for the player.

### Request Example
```python
from getters import get_individual_player_data

# Fetch data for player ID 389 (e.g., Erling Haaland)
player_data = get_individual_player_data(389)

# Access player gameweek history
gw_history = player_data['history']
for gw in gw_history:
    print(f"GW{gw['round']}: {gw['total_points']} points, {gw['goals_scored']} goals")

# Access player's past season history
past_seasons = player_data['history_past']
for season in past_seasons:
    print(f"{season['season_name']}: {season['total_points']} points")

# Access upcoming fixtures
fixtures = player_data['fixtures']
```

### Response
#### Success Response (200)
- **history** (array) - An array of objects, each representing a gameweek's performance for the player.
- **history_past** (array) - An array of objects, each representing the player's performance in past seasons.
- **fixtures** (array) - An array of objects, representing upcoming fixtures for the player's team.

#### Response Example
```json
{
  "history": [
    {
      "round": 1,
      "total_points": 10,
      "goals_scored": 2
    }
  ],
  "history_past": [
    {
      "season_name": "2022/23",
      "total_points": 300
    }
  ],
  "fixtures": [
    {
      "opponent_team": 5,
      "team_h_score": null,
      "team_a_score": null
    }
  ]
}
```
```

--------------------------------

### Get Recent Gameweek ID - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves the ID of the most recently completed gameweek. It depends on the 'gameweek' module and returns an integer representing the gameweek ID.

```python
from gameweek import get_recent_gameweek_id

# Get the most recent completed gameweek
recent_gw = get_recent_gameweek_id()
print(f"Most recent gameweek: {recent_gw}")
```

--------------------------------

### Fetch Individual Player History - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Gets gameweek-by-gameweek history, past season performance, and upcoming fixture data for a specific player ID. Uses the `get_individual_player_data` function from the `getters` module. Provides insights into player's performance and schedule.

```python
from getters import get_individual_player_data

# Fetch data for player ID 389 (e.g., Erling Haaland)
player_data = get_individual_player_data(389)

# Access player gameweek history
gw_history = player_data['history']
for gw in gw_history:
    print(f"GW{gw['round']}: {gw['total_points']} points, {gw['goals_scored']} goals")

# Access player's past season history
past_seasons = player_data['history_past']
for season in past_seasons:
    print(f"{season['season_name']}: {season['total_points']} points")

# Access upcoming fixtures
fixtures = player_data['fixtures']
```

--------------------------------

### Download Team Data using teams_scraper.py

Source: https://github.com/vaastav/fantasy-premier-league/blob/master/README.md

This command-line script, `teams_scraper.py`, allows users to download specific data for their FPL team. It requires a team ID as an argument and generates a folder containing individual data files for the team.

```bash
python teams_scraper.py <team_id>
#Eg: python teams_scraper.py 4582
```

--------------------------------

### Collect Gameweek Data - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Aggregates player performance data for a specific gameweek, including team and opponent information. It requires the gameweek number, player directory, and output directory, producing a 'gwX.csv' file.

```python
from collector import collect_gw

# Collect data for gameweek 10
gw_number = 10
players_dir = 'data/2024-25/players'
output_dir = 'data/2024-25/gws'
root_dir = 'data/2024-25'

collect_gw(gw_number, players_dir, output_dir, root_dir)
# Creates: data/2024-25/gws/gw10.csv

# The output includes: name, position, team, xP (expected points),
# plus all performance stats (minutes, goals, assists, bonus, etc.)
```

--------------------------------

### Fetch All Player Data

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves the complete FPL bootstrap-static dataset, which includes all players, teams, gameweeks, and season information. This data is essential for a general overview of the FPL landscape.

```APIDOC
## Fetch All Player Data

### Description
Retrieves the complete FPL bootstrap-static dataset containing all players, teams, gameweeks, and season information.

### Method
GET

### Endpoint
/api/bootstrap-static/

### Parameters
This endpoint does not require any specific parameters.

### Request Example
```python
from getters import get_data
import json

# Fetch complete FPL data
data = get_data()

# Save to file
with open('raw.json', 'w') as outf:
    json.dump(data, outf)

# Access different data sections
players = data['elements']  # All player data
teams = data['teams']  # All team data
gameweeks = data['events']  # All gameweek data

# Example: Print first player's name and total points
print(f"{players[0]['first_name']} {players[0]['second_name']}: {players[0]['total_points']} points")
```

### Response
#### Success Response (200)
- **data** (object) - The complete FPL bootstrap-static dataset, containing keys like 'elements', 'teams', 'events', etc.

#### Response Example
```json
{
  "elements": [
    {
      "id": 1,
      "first_name": "Mesut",
      "second_name": "Özil",
      "total_points": 130,
      "team_id": 1
    }
  ],
  "teams": [
    {
      "id": 1,
      "name": "Arsenal"
    }
  ],
  "events": [
    {
      "id": 1,
      "name": "Gameweek 1",
      "deadline_time": "2023-08-11T18:30:00Z"
    }
  ]
}
```
```

--------------------------------

### Create Player ID Mapping - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Generates a CSV file that maps player names to their FPL IDs and can also load this mapping into a Python dictionary. It requires the raw players CSV file and an output directory.

```python
from cleaners import id_players, get_player_ids

# Create player ID list
players_file = 'data/2024-25/players_raw.csv'
output_dir = 'data/2024-25/'

id_players(players_file, output_dir)
# Creates: data/2024-25/player_idlist.csv

# Load player IDs into a dictionary
player_ids = get_player_ids('data/2024-25/')
# Returns: {4: 'Bukayo_Saka', 17: 'Gabriel_Martinelli', ...}

print(f"Player ID 4: {player_ids[4]}")
```

--------------------------------

### Fetch Team/Entry Data

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves historical performance data for a specific FPL team/manager, including current season performance and past season summaries.

```APIDOC
## Fetch Team/Entry Data

### Description
Retrieves historical performance data for a specific FPL team/manager.

### Method
GET

### Endpoint
/api/entry/{entry_id}/

### Parameters
#### Path Parameters
- **entry_id** (integer) - Required - The unique identifier for the FPL team/entry.

### Request Example
```python
from getters import get_entry_data, get_entry_personal_data

# Fetch data for team ID 4582
team_id = 4582
entry_data = get_entry_data(team_id)
personal_data = get_entry_personal_data(team_id)

# Current season gameweek-by-gameweek data
current_season = entry_data['current']
for gw in current_season:
    print(f"GW{gw['event']}: {gw['points']} points, Rank: {gw['overall_rank']}")

# Past seasons summary
past_seasons = entry_data['past']
for season in past_seasons:
    print(f"{season['season_name']}: {season['total_points']} points, Rank: {season['rank']}")

# Team leagues
classic_leagues = personal_data['leagues']['classic']
print(f"Number of leagues: {len(classic_leagues)}")
```

### Response
#### Success Response (200)
- **current** (array) - Gameweek-by-gameweek performance for the current season.
- **past** (array) - Summary of performance for past seasons.
- **leagues** (object) - Information about leagues the entry participates in.

#### Response Example
```json
{
  "current": [
    {
      "event": 1,
      "points": 50,
      "overall_rank": 10000
    }
  ],
  "past": [
    {
      "season_name": "2022/23",
      "total_points": 2200,
      "rank": 5000
    }
  ],
  "leagues": {
    "classic": [
      {
        "id": 123,
        "name": "My Classic League"
      }
    ]
  }
}
```
```

--------------------------------

### Parse Fixtures to CSV - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Converts FPL fixture data from JSON format to a structured CSV file. It fetches fixture data using 'getters' and parses it using 'parsers', creating a 'fixtures.csv' file.

```python
from getters import get_fixtures_data
from parsers import parse_fixtures

# Fetch and parse fixtures
fixtures = get_fixtures_data()
parse_fixtures(fixtures, 'data/2024-25/')
# Creates: data/2024-25/fixtures.csv

# Columns include: id, event (gameweek), team_h, team_a, team_h_score,
# team_a_score, kickoff_time, finished, stats (detailed match events)
```

--------------------------------

### Parse Individual Player History - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Saves individual player gameweek and season history to separate CSV files. It requires player IDs, player names, and output directories, utilizing data from 'getters' and 'parsers' modules.

```python
from getters import get_individual_player_data
from parsers import parse_player_history, parse_player_gw_history
from cleaners import get_player_ids

# Get player name mapping
player_ids = get_player_ids('data/2024-25/')
player_id = 389
player_name = player_ids[player_id]

# Fetch individual player data
player_data = get_individual_player_data(player_id)

# Parse season history
parse_player_history(
    player_data['history_past'],
    'data/2024-25/players/',
    player_name,
    player_id
)
# Creates: data/2024-25/players/{player_name}_{id}/history.csv

# Parse gameweek history
parse_player_gw_history(
    player_data['history'],
    'data/2024-25/players/',
    player_name,
    player_id
)
# Creates: data/2024-25/players/{player_name}_{id}/gw.csv
```

--------------------------------

### Fetch All Player Data - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves the complete FPL bootstrap-static dataset, including all players, teams, gameweeks, and season information. The data is fetched using the `get_data` function and can be saved to a JSON file. It allows access to player, team, and gameweek data sections.

```python
from getters import get_data
import json

# Fetch complete FPL data
data = get_data()

# Save to file
with open('raw.json', 'w') as outf:
    json.dump(data, outf)

# Access different data sections
players = data['elements']  # All player data
teams = data['teams']  # All team data
gameweeks = data['events']  # All gameweek data

# Example: Print first player's name and total points
print(f"{players[0]['first_name']} {players[0]['second_name']}: {players[0]['total_points']} points")
```

--------------------------------

### Import Multi-Season Merged FPL Data (Python)

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Loads and combines merged gameweek data across multiple FPL seasons ('2020-21' to '2023-24'). It cleans player names, filters for players active in the latest season, adds opponent team names, and exports the consolidated data to 'data/cleaned_merged_seasons.csv'.

```python
from mergers import import_merged_gw, clean_players_name_string
from mergers import filter_players_exist_latest, get_opponent_team_name
from mergers import export_cleaned_data
import pandas as pd

# Import data for multiple seasons
seasons = ['2020-21', '2021-22', '2022-23', '2023-24']
dfs = []

for season in seasons:
    season_path = import_merged_gw(season)
    df = pd.read_csv(season_path)
    df['season'] = season
    dfs.append(df)

# Merge all seasons
merged_df = pd.concat(dfs, ignore_index=True)

# Clean player names
merged_df = clean_players_name_string(merged_df, col='name')

# Filter to players existing in latest season
merged_df = filter_players_exist_latest(merged_df, col='position')

# Add opponent team names
merged_df = get_opponent_team_name(merged_df)

# Export cleaned merged data
export_cleaned_data(merged_df)
# Creates: data/cleaned_merged_seasons.csv
```

--------------------------------

### Fetch Team/Entry Performance Data - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves historical performance data for a specific FPL team/manager ID. Uses `get_entry_data` for current and past season gameweek performance and `get_entry_personal_data` for league information. Outputs include gameweek points, overall rank, and league counts.

```python
from getters import get_entry_data, get_entry_personal_data

# Fetch data for team ID 4582
team_id = 4582
entry_data = get_entry_data(team_id)
personal_data = get_entry_personal_data(team_id)

# Current season gameweek-by-gameweek data
current_season = entry_data['current']
for gw in current_season:
    print(f"GW{gw['event']}: {gw['points']} points, Rank: {gw['overall_rank']}")

# Past seasons summary
past_seasons = entry_data['past']
for season in past_seasons:
    print(f"{season['season_name']}: {season['total_points']} points, Rank: {season['rank']}")

# Team leagues
classic_leagues = personal_data['leagues']['classic']
print(f"Number of leagues: {len(classic_leagues)}")
```

--------------------------------

### Access FPL Data via URL (Python/Pandas)

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Reads FPL gameweek data directly from a GitHub URL for the '2024-25' season using Pandas. Demonstrates analysis of top scorers by summing total points and filtering data for a specific gameweek.

```python
import pandas as pd

# Read merged gameweek data for 2024-25 season
url = "https://raw.githubusercontent.com/vaastav/Fantasy-Premier-League/master/data/2024-25/gws/merged_gw.csv"
df = pd.read_csv(url)

# Analyze top scorers
top_scorers = df.groupby('name')['total_points'].sum().sort_values(ascending=False).head(10)
print("Top 10 scorers:")
print(top_scorers)

# Filter specific gameweek
gw5_data = df[df['GW'] == 5]
print(f"\nGameweek 5 top scorer: {gw5_data.loc[gw5_data['total_points'].idxmax(), 'name']}")
```

--------------------------------

### Fetch Transfer History - Python FPL API

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves all transfers made by a specific team throughout the season. Uses the `get_entry_transfers_data` function with the team ID. The output details each transfer, including gameweek, players out/in, and cost.

```python
from getters import get_entry_transfers_data

# Get all transfers for team 4582
transfers = get_entry_transfers_data(4582)

for transfer in transfers:
    print(f"GW{transfer['event']}: OUT {transfer['element_out']} -> IN {transfer['element_in']} (Cost: {transfer['element_in_cost']/10}m)")
```

--------------------------------

### Clean Player Data - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Cleans raw player data from a CSV file, calculating value per million and essential performance metrics. It requires input and output file paths and generates a 'cleaned_players.csv' file.

```python
from cleaners import clean_players

# Clean raw player data
input_file = 'data/2024-25/players_raw.csv'
output_dir = 'data/2024-25/'

clean_players(input_file, output_dir)
# Creates: data/2024-25/cleaned_players.csv

# The output includes these columns:
# first_name, second_name, goals_scored, assists, total_points, minutes,
# goals_conceded, creativity, influence, threat, bonus, bps, ict_index,
# clean_sheets, red_cards, yellow_cards, selected_by_percent, now_cost,
# element_type (GK/DEF/MID/FWD), value_per_m
```

--------------------------------

### Fetch Transfer History

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Retrieves all transfers made by a specific team throughout the season, detailing player ins and outs along with any cost implications.

```APIDOC
## Fetch Transfer History

### Description
Retrieves all transfers made by a specific team throughout the season.

### Method
GET

### Endpoint
/api/entry/{entry_id}/transfers/

### Parameters
#### Path Parameters
- **entry_id** (integer) - Required - The unique identifier for the FPL team/entry.

### Request Example
```python
from getters import get_entry_transfers_data

# Get all transfers for team 4582
transfers = get_entry_transfers_data(4582)

for transfer in transfers:
    print(f"GW{transfer['event']}: OUT {transfer['element_out']} -> IN {transfer['element_in']} (Cost: {transfer['element_in_cost']/10}m)")
```

### Response
#### Success Response (200)
- **transfers** (array) - An array of objects, each representing a transfer made.

#### Response Example
```json
[
  {
    "event": 5,
    "element_out": 150,
    "element_in": 200,
    "element_in_cost": 55
  }
]
```
```

--------------------------------

### Parse Raw Player Data to CSV - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Fetches raw player and team data from the FPL API and parses it into structured CSV files. It depends on 'getters' and 'parsers' modules and creates 'players_raw.csv' and 'teams.csv'.

```python
from getters import get_data
from parsers import parse_players, parse_team_data

# Fetch and parse all players
data = get_data()
parse_players(data['elements'], 'data/2024-25/')
# Creates: data/2024-25/players_raw.csv

# Parse team data
parse_team_data(data['teams'], 'data/2024-25/')
# Creates: data/2024-25/teams.csv
```

--------------------------------

### Merge Gameweek CSVs - Python

Source: https://context7.com/vaastav/fantasy-premier-league/llms.txt

Combines individual gameweek CSV files into a single merged file or merges a specific gameweek's data. It requires the number of gameweeks or a specific gameweek number and the directory containing the gameweek CSVs.

```python
from collector import merge_gw, merge_all_gws

# Merge all gameweeks from 1 to 38
num_gws = 38
gw_directory = 'data/2024-25/gws'

merge_all_gws(num_gws, gw_directory)
# Creates: data/2024-25/gws/merged_gw.csv

# Or merge a single gameweek
merge_gw(10, gw_directory)
```

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