Yahoo Fantasy Basketball Positions

positions.py

#!/usr/bin/env python
# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "requests",
#     "pandas",
# ]
# ///

import pandas as pd
import requests

headers = {
    "authority": "pub-api-ro.fantasysports.yahoo.com",
    "accept": "*/*",
    "accept-language": "en-US,en;q=0.9,ca;q=0.8,da;q=0.7",
    "cache-control": "no-cache",
    "origin": "https://basketball.fantasysports.yahoo.com",
    "pragma": "no-cache",
    "referer": "https://basketball.fantasysports.yahoo.com/",
    "sec-ch-ua": '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
    "sec-ch-ua-mobile": "?0",
    "sec-ch-ua-platform": '"macOS"',
    "sec-fetch-dest": "empty",
    "sec-fetch-mode": "cors",
    "sec-fetch-site": "same-site",
    "user-agent": (
        """Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"""
        """(KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36")"""
    ),
}

params = {
    "format": "json_f",
}


def main():
    names = []
    positions = []
    for start in range(0, 600, 30):
        print(start)
        response = requests.get(
            (
                """https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/"""
                f"""428.l.public;out=settings/players;position=ALL;start={start};"""
                """count=30;sort=rank_season;search=;out=auction_values,ranks;ranks=season;"""
                """ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type="""
                """projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days"""
            ),
            params=params,
            headers=headers,
        )

        obj = response.json()
        for player in obj["fantasy_content"]["league"]["players"]:
            names.append(player["player"]["name"]["full"])
            positions.append(
                ", ".join(
                    [
                        p
                        for dp in player["player"]["eligible_positions"]
                        for p in dp.values()
                        if p != "Util"
                    ]
                )
            )

    df = pd.DataFrame({"name": names, "position": positions})
    df.to_csv("positions.csv", index=False)


if __name__ == "__main__":
    main()