def crashrat():
raceratings = da.getraceratings()
results = da.results()
raceresults = pd.merge(raceratings, results, how='right', on="raceId")
raceresults = raceresults.dropna(subset=["rating"])
crashers = raceresults[(raceresults["statusId"] > 1) & (
(raceresults["statusId"] < 10) | (raceresults["statusId"] > 14))]
ccount = crashers.groupby("raceId").count()
rmean = raceresults.groupby("raceId").mean()
racecrash = raceresults.drop_duplicates(subset=["raceId"])
cols = np.delete(ccount.columns.to_numpy(), 24)
ccount = ccount.drop(columns=cols)
ccount.columns = ["crashcount"]
racecrash = pd.merge(racecrash, ccount, how="left", on="raceId")
cols = np.delete(rmean.columns.to_numpy(), 4)
rmean = rmean.drop(columns=cols)
rmean.columns = ["averagerating"]
racecrash = pd.merge(racecrash, rmean, how="left", on="raceId")
racecrash = racecrash.fillna(0)
cmp = makecmp(
raceratings,
raceresults.drop_duplicates(subset=["raceId"]).name.to_numpy())
plt.scatter(racecrash["crashcount"],
racecrash["averagerating"],
alpha=0.8,
color=cmp.colors)
plt.ylabel("Score")
plt.xlabel("DNF's")
plt.title("Rating (1-10) compared to DNF's per race")
plt.tight_layout()
#plt.show()
plt.savefig("D:\Semester3\ADS-A\Challenge\Charts\CrashRatRace.png")
def getdata():
raceratings = da.getraceratings()
raceratings = raceratings.drop(
columns=['year', 'round', 'circuitId', 'date', 'time', 'race'])
results = da.results()
raceresults = pd.merge(raceratings, results, how='right', on="raceId")
crashers = raceresults[(raceresults["statusId"] > 1) & (
(raceresults["statusId"] < 10) | (raceresults["statusId"] > 14))]
ccount = crashers.groupby("raceId").count()
cols = np.delete(ccount.columns.to_numpy(), 18)
ccount = ccount.drop(columns=cols)
ccount.columns = ["crashcount"]
ccount = ccount.fillna(0)
raceratings = pd.merge(raceratings, ccount, how="right", on="raceId")
raceratings["crashcount"] = raceratings["crashcount"].fillna(0)
stops = da.pitstops()
racestops = pd.merge(raceratings, stops, how='left', on="raceId")
racestopsraceavg = racestops.groupby(["raceId", "driverId"
]).count().groupby("raceId").mean()
racestopsraceavg = racestopsraceavg.drop(columns=[
'name', 'rating', 'crashcount', 'lap', 'time', 'duration',
'milliseconds'
])
racestopsraceavg.columns = ["averagestops"]
raceratings = pd.merge(raceratings,
racestopsraceavg,
how="right",
on="raceId")
laptimes = da.laptimes()
racelapratings = pd.merge(raceratings, laptimes, how="right", on="raceId")
racelapratings = racelapratings.dropna(subset=[
"rating",
])
avglaptimerace = racelapratings.groupby(
["raceId", "driverId"]).mean().groupby("raceId").mean()
avgbestlaptime = racelapratings.groupby(["raceId", "driverId"
]).min().groupby("raceId").mean()
avgdeltarace = (avglaptimerace["milliseconds"] -
avgbestlaptime["milliseconds"])
raceratings = pd.merge(raceratings, avgdeltarace, how="left", on="raceId")
raceratings.columns = [
'raceId', 'name', 'rating', 'crashcount', 'averagestops', 'delta'
]
raceratings = raceratings.drop(columns=['raceId'])
return raceratings.dropna()
