SpeedTraps

import matplotlib.pyplot as plt
import pandas as pd
import fastf1 as ff1
import fastf1.plotting
import matplotlib as mpl
import matplotlib.ticker as tck
import numpy as np
from matplotlib.offsetbox import OffsetImage,AnnotationBbox
from timple.timedelta import strftimedelta
from fastf1.core import Laps

mpl.rcParams.update(mpl.rcParamsDefault)


fastf1.Cache.enable_cache('Cache')  # replace with your cache directory
fastf1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='none', misc_mpl_mods=False)
session_year = 2023
session_event = 'British Grand Prix'
session_name = 'R'
session = fastf1.get_session(session_year, session_event, session_name)
session.load()
drivers = pd.unique(session.laps['Driver'])

list_fastest_laps = list()
for drv in drivers:
    drvs_fastest_lap = session.laps.pick_driver(drv).pick_fastest()
    list_fastest_laps.append(drvs_fastest_lap)
fastest_laps = Laps(list_fastest_laps).sort_values(by='LapTime')

speedtrap_by_team = fastest_laps[['Team', 'SpeedST']].groupby(['Team']).max().sort_values('SpeedST', ascending=False).reset_index()
speedtrap_by_team['ShortName'] = speedtrap_by_team['Team'].str.replace(' Racing', '').str.replace(' F1 Team', '')
team_colors = list()

for index, team in speedtrap_by_team.iterrows():
    color = ff1.plotting.team_color(team['Team'])
    team_colors.append(color)
    
plt.style.use("ggplot") 
fig, ax = plt.subplots()

hbars = ax.bar(speedtrap_by_team['ShortName']
               , speedtrap_by_team['SpeedST']
               , color=team_colors
               , edgecolor='grey')
#set the upper and lower boundaries of the y axis
plt.ylim(round((speedtrap_by_team['SpeedST'].min() - 10)/10)*10
         , round((speedtrap_by_team['SpeedST'].max() + 10)/10)*10)

ax.set(xlabel='')
ax.set(ylabel='Speed (km/h)')
ax.set_axisbelow(True)
ax.yaxis.grid(True, which='major', linestyle='-')
ax.xaxis.grid(False)

plt.suptitle(f"{session.event['EventName']} {session.event.year} {session_name} \n"
             f"Max Speed Trap (Fastest Lap)")
plt.xticks(rotation=90)
ax.bar_label(hbars, fontsize=10, padding=3)
plt.show()