import pandas as pd
import zipfile
import os

# Unzipping the provided file
with zipfile.ZipFile("/mnt/data/f1db_csv.zip", 'r') as zip_ref:
    zip_ref.extractall("/mnt/data/f1db_csv")

# Checking the contents of the extracted folder
extracted_files = os.listdir("/mnt/data/f1db_csv")
extracted_files

['circuits.csv',
 'constructor_results.csv',
 'constructor_standings.csv',
 'constructors.csv',
 'driver_standings.csv',
 'drivers.csv',
 'lap_times.csv',
 'pit_stops.csv',
 'qualifying.csv',
 'races.csv',
 'results.csv',
 'seasons.csv',
 'sprint_results.csv',
 'status.csv']

# Loading the relevant files
drivers_df = pd.read_csv("/mnt/data/f1db_csv/drivers.csv")
results_df = pd.read_csv("/mnt/data/f1db_csv/results.csv")

# Merging drivers and results to get complete information
merged_df = results_df.merge(drivers_df, on='driverId')

# Counting victories for each driver
victory_counts = merged_df[merged_df['positionOrder'] == 1].groupby('driverId').size().sort_values(ascending=False)

# Getting the top 5 drivers with most victories
top_5_victories = victory_counts.head(5)
top_5_driver_ids = top_5_victories.index.tolist()

# Getting driver names for the top 5
top_5_drivers = drivers_df[drivers_df['driverId'].isin(top_5_driver_ids)][['driverId', 'forename', 'surname']]
top_5_drivers = top_5_drivers.set_index('driverId')
top_5_drivers['victories'] = top_5_drivers.index.map(top_5_victories)

# Formatting the result
top_5_drivers = top_5_drivers.sort_values(by='victories', ascending=False)
top_5_drivers_list = top_5_drivers.to_dict(orient='records')
top_5_drivers_list

[{'forename': 'Lewis', 'surname': 'Hamilton', 'victories': 103},
 {'forename': 'Michael', 'surname': 'Schumacher', 'victories': 91},
 {'forename': 'Sebastian', 'surname': 'Vettel', 'victories': 53},
 {'forename': 'Max', 'surname': 'Verstappen', 'victories': 52},
 {'forename': 'Alain', 'surname': 'Prost', 'victories': 51}]

Top 5 F1 Victories

Bot expert in F1 data analysis and race insights.