Read in the data file f1-data.csv concerning recent Formula One Grands Prix seasons, and rank (a) the drivers by their number of wins; (b) the constructors by their number of wins; and (c) the circuits by their average fastest lap per race.
The following code uses groupby to determine the necessary rankings.
import pandas as pd
df = pd.read_csv('f1-data.csv')
# Create a DataFrame of race winners.
winners = df[df['Position']==1]
# Group by Driver and count wins.
g = winners.groupby('Driver')
print('Drivers by number of wins')
print(g['Driver'].count().sort_values(ascending=False))
# Group by Constructor and count wins.
g = winners.groupby('Constructor')
print('Constructors by number of wins')
print(g['Constructor'].count().sort_values(ascending=False))
# Ensure the 'Fastest Lap' column is a datetime, and convert to ms.
df['Fastest Lap'] = pd.to_datetime(df['Fastest Lap'], format='%M:%S.%f')
df['Fastest Lap (ms)'] = (df['Fastest Lap'].dt.minute * 60000
+ df['Fastest Lap'].dt.second * 1000
+ df['Fastest Lap'].dt.microsecond / 1000)
# Group Fastest Lap by Circuit and calculate mean.
g = df[df['Fastest Lap (ms)'].notna()].groupby('Circuit')
tdf = g['Fastest Lap (ms)'].mean().sort_values()
def to_time_str(time_ms):
"""Convert from ms to string in form MM:SS.[MS]"""
mins, time_ms = divmod(time_ms, 60000)
secs = time_ms / 1000
return '{:02d}:{:6.3f}'.format(int(mins), secs)
print('Mean fastest lap times by circuit')
for circuit, time in tdf.iteritems():
print(to_time_str(time), circuit)