File size: 1,115 Bytes
7fd5387 62740e0 7fd5387 13801e8 62740e0 7fd5387 62740e0 7fd5387 13801e8 7fd5387 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
import pandas as pd
from domain.playoffs import SCHEDULE_WEEK_TO_PLAYOFF_WEEK
def get_full_schedule(season_int: str | int) -> pd.DataFrame:
url = f"https://www.pro-football-reference.com/years/{season_int}/games.htm#games"
df = pd.read_html(url)[0]
# remove extra header rows in table
df = df[df.Week != "Week"]
return df
def get_week_team_time_map(df_schedule: pd.DataFrame) -> dict[int, dict[str, pd.Timestamp]]:
week_team_time_map: dict[int, dict[str, pd.Timestamp]] = {k: {} for k in SCHEDULE_WEEK_TO_PLAYOFF_WEEK.values()}
for _, row in df_schedule.iterrows():
if mapped_week := SCHEDULE_WEEK_TO_PLAYOFF_WEEK.get(row.Week):
game_time = pd.to_datetime(row.Date + " " + row.Time, yearfirst=True)
week_team_time_map[mapped_week][row["Winner/tie"]] = game_time
week_team_time_map[mapped_week][row["Loser/tie"]] = game_time
return week_team_time_map
def get_season_time_map(season_int: str | int) -> dict[int, dict[str, pd.Timestamp]]:
df_schedule = get_full_schedule(season_int)
return get_week_team_time_map(df_schedule)
|