import pandas as pd
import zipfile
import os

# File path
zip_file_path = '/mnt/data/f1db_csv.zip'

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

# Directory where files are extracted
extracted_dir_path = '/mnt/data/f1db_csv'

# Checking the extracted files
extracted_files = os.listdir(extracted_dir_path)
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']

# Load the necessary data files
drivers_df = pd.read_csv(f'{extracted_dir_path}/drivers.csv')
driver_standings_df = pd.read_csv(f'{extracted_dir_path}/driver_standings.csv')
races_df = pd.read_csv(f'{extracted_dir_path}/races.csv')

# Filter for the 2023 season
races_2023_df = races_df[races_df['year'] == 2023]

# Join driver standings with races to get race information for 2023
driver_standings_2023_df = pd.merge(driver_standings_df, races_2023_df, on='raceId')

# Filter for Alonso and Norris
alonso_id = drivers_df[drivers_df['driverRef'].str.lower() == 'alonso']['driverId'].iloc[0]
norris_id = drivers_df[drivers_df['driverRef'].str.lower() == 'norris']['driverId'].iloc[0]

alonso_standings_2023 = driver_standings_2023_df[driver_standings_2023_df['driverId'] == alonso_id]
norris_standings_2023 = driver_standings_2023_df[driver_standings_2023_df['driverId'] == norris_id]

# Selecting relevant columns for the graph
alonso_points = alonso_standings_2023[['name', 'points']]
norris_points = norris_standings_2023[['name', 'points']]

alonso_points, norris_points

(                         name  points
 17         Bahrain Grand Prix    15.0
 35   Saudi Arabian Grand Prix    30.0
 42      Australian Grand Prix    45.0
 62      Azerbaijan Grand Prix    60.0
 82           Miami Grand Prix    75.0
 102         Monaco Grand Prix    93.0
 122        Spanish Grand Prix    99.0
 155       Canadian Grand Prix   117.0
 174       Austrian Grand Prix   131.0
 182        British Grand Prix   137.0
 202      Hungarian Grand Prix   139.0
 223        Belgian Grand Prix   149.0
 244          Dutch Grand Prix   168.0
 280        Italian Grand Prix   170.0
 302      Singapore Grand Prix   170.0
 310       Japanese Grand Prix   174.0
 351          Qatar Grand Prix   183.0
 373  United States Grand Prix   183.0
 376    Mexico City Grand Prix   183.0
 398      São Paulo Grand Prix   198.0,
                          name  points
 3          Bahrain Grand Prix     0.0
 32   Saudi Arabian Grand Prix     0.0
 56      Australian Grand Prix     8.0
 76      Azerbaijan Grand Prix    10.0
 96           Miami Grand Prix    10.0
 116         Monaco Grand Prix    12.0
 136        Spanish Grand Prix    12.0
 141       Canadian Grand Prix    12.0
 163       Austrian Grand Prix    24.0
 196        British Grand Prix    42.0
 216      Hungarian Grand Prix    60.0
 237        Belgian Grand Prix    69.0
 258          Dutch Grand Prix    75.0
 266        Italian Grand Prix    79.0
 288      Singapore Grand Prix    97.0
 324       Japanese Grand Prix   115.0
 334          Qatar Grand Prix   136.0
 357  United States Grand Prix   159.0
 390    Mexico City Grand Prix   169.0
 412      São Paulo Grand Prix   195.0)

import matplotlib.pyplot as plt

# Plotting the data
plt.figure(figsize=(12, 6))

# Plot for Alonso
plt.plot(alonso_points['name'], alonso_points['points'], label='Alonso', marker='o')

# Plot for Norris
plt.plot(norris_points['name'], norris_points['points'], label='Norris', marker='o')

plt.xticks(rotation=45, ha='right')
plt.xlabel('Race')
plt.ylabel('Points')
plt.title('Driver Standing Points for Alonso and Norris in 2023 Season')
plt.legend()
plt.tight_layout()
plt.show()

Norris VS Alonso

Bot expert in F1 data analysis and race insights.