lmeulen

from nltk import tokenize, word_tokenize

with open("stopwords.txt"), "r", encoding="utf-8") as f:
     text = " ".join(f.readlines())
STOP_WORDS = set(text.split())

lmeulen

year = 2022
rnd = 13
pgl = stats.get_race_results(year)
pgl = pgl[pgl['round'] == rnd][['driver', 'grid', 'position']]
pgl['change'] = pgl['grid'] - pgl['position']

def _color_red_or_green(val):
    color = 'orangered' if val < 0 else 'springgreen'
    return 'background-color: %s' % color
pgl.style.applymap(_color_red_or_green, subset=['change'])

lmeulen

fig, ax = plt.subplots(figsize=(8,3.5))

driver_clrs = [stats.get_driver_color(dr[0]), stats.get_driver_color(dr[1]) ]

ys    = ['Points', 'Points', 'Wins', 'Wins', 'Podiums', 'Podiums', 
         'Quali', 'Quali', 'Race', 'Race']
xmaxs = [-pnts[0]/max(pnts), pnts[1]/max(pnts), \
         -wins[0]/max(wins), wins[1]/max(wins), \
         -podia[0]/max(podia), podia[1]/max(podia), \
         -quali[0]/max(quali), quali[1]/max(quali), \

lmeulen

year = 2022
driver1 = 'VER'
driver2 = 'PER'

race_results = stats.get_race_results(year)
dr = [ race_results[race_results.code == driver1]['driverId'].values[0],
       race_results[race_results.code == driver2]['driverId'].values[0] ]
  
race_results['position'] = race_results['position'].replace(0, 30)
race=[0, 0, 10]

lmeulen

year=2022

nw = stats.get_wcc_standing(year)
rnd = nw['round'].max()
nw = nw[nw['round'] == rnd][['name', 'points']].sort_values('points', ascending=False)

pv = stats.get_wcc_standing(year-1)
pv = pv[pv['round'] == rnd][['name', 'points']].sort_values('points', ascending=False)
pv = pv.rename(columns={"points": "prev_year"})

lmeulen

year=2022
standings = stats.get_wdc_standing(year)

stats.horizontal_driver_lines_plot(df=standings, xcolumn='round', ycolumn='position', 
                                   invert_yaxis=True,
                                   title='Championships standings ({})'.format(year),
                                   xlabel='Round', ylabel='Position')

lmeulen

results = stats.get_race_results()
current_driver_ids = results[results['year'] == 2022]['driverId']

current_drivers = results[results['driverId'].isin(pd.unique(current_driver_ids))]
current_drivers = current_drivers[['driverId', 'driver', 'race']]. \
                      groupby(['driverId', 'driver']).count().reset_index()

stats.horizontal_barplot(df=current_drivers, rowcount=30, sort_value='race', 
                         sort_ascending=False, invert_yaxis=True, 
                         xcolumn='driver', ycolumn='race', 

lmeulen

df = stats.get_race_results().merge(stats.get_table('races'))
df = df.merge(stats.get_table('drivers'), on='driverId')
df['age'] = (pd.to_datetime(df['date']).dt.date - df['dob'])
df['age'] = df['age'] / np.timedelta64(1, 'Y')
df = df[['year', 'age']].groupby('year').mean()

fig, ax = plt.subplots(figsize=(15,8))
df.plot(ax=ax)
ax.set_ylim(25,40)
ax.set_title('Average age per year')

lmeulen

winners = stats.get_winners()
wins_per_driver = winners[['driverId', 'year']].groupby('driverId').count(). \
                      reset_index().rename(columns={"year": "podiums"})

champs = pd.unique(stats.get_wdc_champions()['driverId'])
not_champ = wins_per_driver[~wins_per_driver.driverId.isin(champs)]
not_champ = not_champ.merge(stats.get_table('drivers'))

stats.horizontal_barplot(df=not_champ, rowcount=10, sort_value='podiums', 
                         sort_ascending=False, 

lmeulen

wins = winners[['constructor', 'race']].groupby('constructor').count().reset_index()

stats.horizontal_barplot(df=wins, rowcount=10, sort_value='race', 
                         sort_ascending=False, invert_yaxis=True,
                         xcolumn='constructor', ycolumn='race', 
                         title='Wins per constructor', 
                         xlabel='wins', ylabel='')