def competitions(fmt="dataframe", creds: dict = DEFAULT_CREDS):
if api_client.has_auth(creds) is True:
competitions = api_client.competitions(creds)
else:
competitions = public.competitions()
if fmt == "dataframe":
if isinstance(competitions, dict):
competitions = competitions.values()
competitions = pd.DataFrame(competitions)
return competitions
def lineups(match_id, fmt="dataframe", creds: dict = DEFAULT_CREDS):
if api_client.has_auth(creds) is True:
lineups = api_client.lineups(match_id, creds)
else:
lineups = public.lineups(match_id)
if fmt == "dataframe":
lineups_ = {}
for l in lineups.values():
lineup = pd.DataFrame(l["lineup"])
lineup["country"] = lineup.country.apply(lambda c: c["name"])
lineups_[l["team_name"]] = lineup
lineups = lineups_
return lineups
def events(
match_id: int,
split: bool = False,
filters: dict = {},
fmt: str = "dataframe",
flatten: bool = False,
creds: dict = DEFAULT_CREDS,
) -> (pd.DataFrame, dict):
if api_client.has_auth(creds) is True:
events = api_client.events(match_id, creds)
else:
events = public.events(match_id)
events = filter_and_group_events(events, filters, fmt, flatten)
if fmt == "dataframe":
for ev_type, evs in events.items():
events[ev_type] = pd.DataFrame(evs)
if split is False:
events = pd.concat([*events.values()], axis=0, ignore_index=True, sort=True)
return events
def matches(
competition_id: int, season_id: int, fmt="dataframe", creds: dict = DEFAULT_CREDS
):
if api_client.has_auth(creds) is True:
matches = api_client.matches(competition_id, season_id, creds)
else:
matches = public.matches(competition_id, season_id)
if fmt == "dataframe":
matches = pd.DataFrame(matches.values())
matches["competition"] = matches.competition.apply(
lambda c: f"{c['country_name']} - {c['competition_name']}"
)
for col in ["season", "home_team", "away_team"]:
matches[col] = matches[col].apply(lambda c: c[f"{col}_name"])
for col in ["competition_stage", "stadium", "referee"]:
matches[col] = matches[col].apply(
lambda x: x["name"] if not pd.isna(x) else x
)
metadata = matches.pop("metadata")
for k in ["data_version", "shot_fidelity_version", "xy_fidelity_version"]:
matches[k] = metadata.apply(lambda x: x.get(k))
return matches