Считать данные из файла и вывести автомобили, не старше 5 лет

with open('car.txt', 'r', encoding='utf-8') as f:
    arr = f.read().splitlines()
 
d = []
for x in arr:
    match a := x.split(','):
        case title, cost, year, fuel:
            d.append({'title': title, 'cost': int(cost.strip('$')), 'year': int(year), 'fuel': fuel})
        case title, cost, year if '$' in x:
            fuel = None
            d.append({'title': title, 'cost': int(cost.strip('$')), 'year': int(year), 'fuel': fuel})
        case title, year, fuel if '$' not in x:
            cost = None
            d.append({'title': title, 'cost': cost, 'year': int(year), 'fuel': fuel})
 
on_year = list(filter(lambda x: x.get('year') is not None and 2022 - x.get('year') <= 5, d))
print(*on_year, sep='\n')

from io import StringIO
import pandas as pd 
import numpy as np
import datetime
 
table = """
Nissan Skyline GTR R-32,70000$,1989,14.3L/100km
Mercedes-Benz W211 E63 AMG,67000$,2007,14.5L/100km
Nissan GTR R-35,80000$,2017,15L/100km
Dacia Sandero SteapWay,20000$,2020,5L/100km
BMW M3,65000$,2022,15L/100km
Dodge Challenger SRT Hellcat,,2020,25L/100km
Pourche Tycan,,2019,15L/100km
Tesla Cybertruck,,2019,no patrol
Mercedes-Benz G63 AMG,,2020,25L/100km
BMW M8,,2020,20L/100km
"""
 
df = pd.read_csv(StringIO(table), header=None, sep=",")
df.columns = ['model', 'price', 'year', 'fuel']
df.price = df.price.str.extract('(\d+)').fillna(0).astype(int)
df.fuel = df.fuel.map(lambda x: x.split('L')[0])\
                 .map(lambda x: float(x) if x[0].isdigit() else np.nan)
df.year = df.year.astype(int)
print(df)
 
fuel = 15 #float(input())
year = datetime.datetime.today().year
res = df.loc[(year - df.year.values <= 5) & (df.fuel.values <= fuel), 'model'].tolist()
print(*res, sep='\n')