5 Random F1 Facts- Part 1

Data Analysis Edition

#Let's import some modules:

import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import plotly.express as px
import os

#Setting all graphs to be a specific color and size
sns.set_palette('Set3')
plt.rcParams['figure.figsize']=10,6

#Read in the 13 CSV files:

folder = '../f1db_csv/'

filelist = [file for file in os.listdir(folder) if file.endswith('.csv')]
for file in filelist:
    exec("%s = pd.read_csv('%s')" % (file.split('.')[0], os.path.join(folder,file)))

#Check the dataframes were created correctly:

filelist

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

#Merging into 1 dataframe:
f1 = results.copy()
f1 = f1.merge(constructors, how='left', on='constructorId')
f1 = f1.merge(races, how='left', on='raceId')
f1 = f1.merge(circuits, how='left', on='circuitId')
f1 = f1.merge(drivers, how='left', on='driverId')
f1 = f1.merge(status, how='left', on='statusId')
f1.head()

	resultId	raceId	driverId	constructorId	number_x	grid	position	positionText	positionOrder	points	laps	time_x	milliseconds	fastestLap	rank	fastestLapTime	fastestLapSpeed	statusId	constructorRef	name_x	nationality_x	url_x	year	round	circuitId	name_y	date	time_y	url_y	circuitRef	name	location	country	lat	lng	alt	url_x	driverRef	number_y	code	forename	surname	dob	nationality_y	url_y	status
0	1	18	1	1	22	1	1	1	1	10.0	58	1:34:50.616	5690616	39	2	1:27.452	218.300	1	mclaren	McLaren	British	http://en.wikipedia.org/wiki/McLaren	2008	1	1	Australian Grand Prix	2008-03-16	04:30:00	http://en.wikipedia.org/wiki/2008_Australian_G...	albert_park	Albert Park Grand Prix Circuit	Melbourne	Australia	-37.8497	144.968	10	http://en.wikipedia.org/wiki/Melbourne_Grand_P...	hamilton	44	HAM	Lewis	Hamilton	1985-01-07	British	http://en.wikipedia.org/wiki/Lewis_Hamilton	Finished
1	2	18	2	2	3	5	2	2	2	8.0	58	+5.478	5696094	41	3	1:27.739	217.586	1	bmw_sauber	BMW Sauber	German	http://en.wikipedia.org/wiki/BMW_Sauber	2008	1	1	Australian Grand Prix	2008-03-16	04:30:00	http://en.wikipedia.org/wiki/2008_Australian_G...	albert_park	Albert Park Grand Prix Circuit	Melbourne	Australia	-37.8497	144.968	10	http://en.wikipedia.org/wiki/Melbourne_Grand_P...	heidfeld	\N	HEI	Nick	Heidfeld	1977-05-10	German	http://en.wikipedia.org/wiki/Nick_Heidfeld	Finished
2	3	18	3	3	7	7	3	3	3	6.0	58	+8.163	5698779	41	5	1:28.090	216.719	1	williams	Williams	British	http://en.wikipedia.org/wiki/Williams_Grand_Pr...	2008	1	1	Australian Grand Prix	2008-03-16	04:30:00	http://en.wikipedia.org/wiki/2008_Australian_G...	albert_park	Albert Park Grand Prix Circuit	Melbourne	Australia	-37.8497	144.968	10	http://en.wikipedia.org/wiki/Melbourne_Grand_P...	rosberg	6	ROS	Nico	Rosberg	1985-06-27	German	http://en.wikipedia.org/wiki/Nico_Rosberg	Finished
3	4	18	4	4	5	11	4	4	4	5.0	58	+17.181	5707797	58	7	1:28.603	215.464	1	renault	Renault	French	http://en.wikipedia.org/wiki/Renault_in_Formul...	2008	1	1	Australian Grand Prix	2008-03-16	04:30:00	http://en.wikipedia.org/wiki/2008_Australian_G...	albert_park	Albert Park Grand Prix Circuit	Melbourne	Australia	-37.8497	144.968	10	http://en.wikipedia.org/wiki/Melbourne_Grand_P...	alonso	14	ALO	Fernando	Alonso	1981-07-29	Spanish	http://en.wikipedia.org/wiki/Fernando_Alonso	Finished
4	5	18	5	1	23	3	5	5	5	4.0	58	+18.014	5708630	43	1	1:27.418	218.385	1	mclaren	McLaren	British	http://en.wikipedia.org/wiki/McLaren	2008	1	1	Australian Grand Prix	2008-03-16	04:30:00	http://en.wikipedia.org/wiki/2008_Australian_G...	albert_park	Albert Park Grand Prix Circuit	Melbourne	Australia	-37.8497	144.968	10	http://en.wikipedia.org/wiki/Melbourne_Grand_P...	kovalainen	\N	KOV	Heikki	Kovalainen	1981-10-19	Finnish	http://en.wikipedia.org/wiki/Heikki_Kovalainen	Finished

f1.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 24880 entries, 0 to 24879
Data columns (total 46 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   resultId         24880 non-null  int64  
 1   raceId           24880 non-null  int64  
 2   driverId         24880 non-null  int64  
 3   constructorId    24880 non-null  int64  
 4   number_x         24880 non-null  object 
 5   grid             24880 non-null  int64  
 6   position         24880 non-null  object 
 7   positionText     24880 non-null  object 
 8   positionOrder    24880 non-null  int64  
 9   points           24880 non-null  float64
 10  laps             24880 non-null  int64  
 11  time_x           24880 non-null  object 
 12  milliseconds     24880 non-null  object 
 13  fastestLap       24880 non-null  object 
 14  rank             24880 non-null  object 
 15  fastestLapTime   24880 non-null  object 
 16  fastestLapSpeed  24880 non-null  object 
 17  statusId         24880 non-null  int64  
 18  constructorRef   24880 non-null  object 
 19  name_x           24880 non-null  object 
 20  nationality_x    24880 non-null  object 
 21  url_x            24880 non-null  object 
 22  year             24880 non-null  int64  
 23  round            24880 non-null  int64  
 24  circuitId        24880 non-null  int64  
 25  name_y           24880 non-null  object 
 26  date             24880 non-null  object 
 27  time_y           24880 non-null  object 
 28  url_y            24880 non-null  object 
 29  circuitRef       24880 non-null  object 
 30  name             24880 non-null  object 
 31  location         24880 non-null  object 
 32  country          24880 non-null  object 
 33  lat              24880 non-null  float64
 34  lng              24880 non-null  float64
 35  alt              24880 non-null  object 
 36  url_x            24880 non-null  object 
 37  driverRef        24880 non-null  object 
 38  number_y         24880 non-null  object 
 39  code             24880 non-null  object 
 40  forename         24880 non-null  object 
 41  surname          24880 non-null  object 
 42  dob              24880 non-null  object 
 43  nationality_y    24880 non-null  object 
 44  url_y            24879 non-null  object 
 45  status           24880 non-null  object 
dtypes: float64(3), int64(11), object(32)
memory usage: 8.9+ MB

#replacing any empty vales with NaN
f1.replace(r'\N', np.nan, inplace=True)

#Focusing the dataframe:
f1 = f1[['year', 'name', 'date', 'driverRef', 'name_x', 'grid',
       'position', 'points', 'laps', 'time_x',
       'milliseconds', 'fastestLap', 'rank', 'fastestLapTime',
       'fastestLapSpeed',  'round',
        'dob', 'nationality_y', 'status']]
f1.position = f1.position.astype(float)
f1 = f1.set_index('date')

f1 = f1.rename(columns={'name_x':'constructor', 'time_x': 'time', 'nationality_y':'nationality'})
f1 = f1.sort_values('year')
f1.head()

	year	name	driverRef	constructor	grid	position	points	laps	time	milliseconds	fastestLap	rank	fastestLapTime	fastestLapSpeed	round	dob	nationality	status
date
1950-05-13	1950	Silverstone Circuit	claes	Talbot-Lago	21	11.0	0.0	64	NaN	NaN	NaN	NaN	NaN	NaN	1	1916-08-11	Belgian	+6 Laps
1950-06-18	1950	Circuit de Spa-Francorchamps	farina	Alfa Romeo	1	4.0	4.0	35	+4:05.0	10291000	NaN	NaN	NaN	NaN	5	1906-10-30	Italian	Finished
1950-06-18	1950	Circuit de Spa-Francorchamps	ascari	Ferrari	7	5.0	2.0	34	NaN	NaN	NaN	NaN	NaN	NaN	5	1918-07-13	Italian	+1 Lap
1950-06-18	1950	Circuit de Spa-Francorchamps	villoresi	Ferrari	4	6.0	0.0	33	NaN	NaN	NaN	NaN	NaN	NaN	5	1909-05-16	Italian	+2 Laps
1950-06-18	1950	Circuit de Spa-Francorchamps	levegh	Talbot-Lago	10	7.0	0.0	33	NaN	NaN	NaN	NaN	NaN	NaN	5	1905-12-22	French	+2 Laps

f1.info()

<class 'pandas.core.frame.DataFrame'>
Index: 24880 entries, 1950-05-13 to 2020-11-01
Data columns (total 18 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   year             24880 non-null  int64  
 1   name             24880 non-null  object 
 2   driverRef        24880 non-null  object 
 3   constructor      24880 non-null  object 
 4   grid             24880 non-null  int64  
 5   position         14154 non-null  float64
 6   points           24880 non-null  float64
 7   laps             24880 non-null  int64  
 8   time             6546 non-null   object 
 9   milliseconds     6545 non-null   object 
 10  fastestLap       6474 non-null   object 
 11  rank             6631 non-null   object 
 12  fastestLapTime   6474 non-null   object 
 13  fastestLapSpeed  6474 non-null   object 
 14  round            24880 non-null  int64  
 15  dob              24880 non-null  object 
 16  nationality      24880 non-null  object 
 17  status           24880 non-null  object 
dtypes: float64(2), int64(4), object(12)
memory usage: 3.6+ MB

Question 1: Who participated in the most races?

most_races = f1.driverRef.value_counts().head().copy()
most_races = pd.DataFrame(most_races)
most_races = most_races.reset_index()
most_races.columns = ['driver', 'number_of_races']
most_races

	driver	number_of_races
0	raikkonen	328
1	barrichello	326
2	alonso	314
3	button	309
4	michael_schumacher	308

sns.barplot(x='driver', y='number_of_races', data=most_races)
plt.title('Who has the most races in F1?', fontsize=20)
plt.ylabel('Number of Races', fontsize=15)
plt.xlabel('Driver', fontsize=15)
plt.xticks(np.arange(5), most_races.driver.str.capitalize(), fontsize=11);

Question 2: Who has driven in F1 the longest?

most_years = f1[['year', 'driverRef']].copy()
most_years = most_years.drop_duplicates(['year', 'driverRef'])[['year', 'driverRef']]
most_years = pd.DataFrame(most_years)
most_years = most_years.value_counts('driverRef').reset_index().head(5)
most_years.columns = ['driver', 'number_of_years']
most_years.style.hide_index()

driver	number_of_years
barrichello	19
michael_schumacher	19
hill	18
button	18
raikkonen	18

sns.barplot(x='driver', y='number_of_years', data=most_years)
plt.title('Who raced for the longest in F1?', fontsize=20)
plt.ylabel('Number of Years Racing', fontsize=15)
plt.xlabel('Driver', fontsize=15)
plt.xticks(np.arange(5), most_races.driver.str.capitalize(), fontsize=11)
;

''

Question 3: Which track has been used the most?

track_use = f1[['year', 'name']].copy()
track_use = track_use.drop_duplicates(['year', 'name'])[['year', 'name']]
track_use = pd.DataFrame(track_use)
track_use = track_use.value_counts('name').reset_index()
track_use.columns = ['circuit', 'years_used']
most_track_use = track_use.copy().head()
most_track_use.style.hide_index()

circuit	years_used
Autodromo Nazionale di Monza	70
Circuit de Monaco	66
Silverstone Circuit	54
Circuit de Spa-Francorchamps	53
Nürburgring	41

sns.barplot(x='circuit', y='years_used', data=most_track_use )
plt.title('Circuit used for the most years in F1', fontsize=20)
plt.ylabel('Number of Years Used', fontsize=15)
plt.xlabel('Circuit Name', labelpad=12, fontsize=15)
plt.xticks(np.arange(5), most_races.driver.str.capitalize(), rotation=15, size=11);

Question 4: What tracks have only been visited once?

a = track_use[track_use['years_used']==1]

least = f1.copy()

least.columns

Index(['year', 'name', 'driverRef', 'constructor', 'grid', 'position',
       'points', 'laps', 'time', 'milliseconds', 'fastestLap', 'rank',
       'fastestLapTime', 'fastestLapSpeed', 'round', 'dob', 'nationality',
       'status'],
      dtype='object')

least = a.merge(least, how='left', left_on='circuit', right_on='name')
least = least[['year','circuit', 'years_used']].drop_duplicates().sort_values('year')
least.style.hide_index()

year	circuit	years_used
1957	Pescara Circuit	1
1958	Ain Diab	1
1959	Sebring International Raceway	1
1959	AVUS	1
1959	Monsanto Park Circuit	1
1960	Riverside International Raceway	1
1964	Zeltweg	1
1967	Le Mans	1
1984	Fair Park	1
1993	Donington Park	1
2020	Autodromo Internazionale del Mugello	1
2020	Autódromo Internacional do Algarve	1

Question 5: Which circuits have the fastest lap times?

fastest_lap = f1[['name', 'fastestLapTime', 'driverRef']].copy()
fastest_lap = fastest_lap.sort_values(by='fastestLapTime')
fastest_lap = fastest_lap.drop_duplicates(['name'])[['name', 'fastestLapTime', 'driverRef']].head(5)
fastest_lap

	name	fastestLapTime	driverRef
date
2020-07-12	Red Bull Ring	1:05.619	sainz
2004-06-20	Indianapolis Motor Speedway	1:10.399	barrichello
2018-11-11	Autódromo José Carlos Pace	1:10.540	bottas
2019-06-09	Circuit Gilles Villeneuve	1:13.078	bottas
2004-07-25	Hockenheimring	1:13.780	raikkonen

fastest_lap['fastestLapTimeSec'] = (fastest_lap['fastestLapTime'].str.split(':', expand = True)[0].astype('float64') * 60) + fastest_lap['fastestLapTime'].str.split(':', expand = True)[1].astype('float64')
fastest_lap

	name	fastestLapTime	driverRef	fastestLapTimeSec
date
2020-07-12	Red Bull Ring	1:05.619	sainz	65.619
2004-06-20	Indianapolis Motor Speedway	1:10.399	barrichello	70.399
2018-11-11	Autódromo José Carlos Pace	1:10.540	bottas	70.540
2019-06-09	Circuit Gilles Villeneuve	1:13.078	bottas	73.078
2004-07-25	Hockenheimring	1:13.780	raikkonen	73.780

sns.barplot(x='name', y='fastestLapTimeSec', data=fastest_lap)
plt.title('Which tracks have the fastest lap times in F1?', fontsize=20)
plt.ylabel('Lap Times (Sec)', fontsize=15)
plt.xlabel('Circuit', fontsize=15)
plt.xticks(rotation=15, fontsize=11);