Formula 1 analizė 1950 - 2021¶

Hipotezės¶

  1. Micheal Schumacher yra geriausias F1 vairutojas
  2. Į 10 geriausių kvalifikacijoje pasirodžiusių vairuotojų įeina, Max Verstappen
  3. Daugiausiai kartų varžybos yra vykusios Italijoje
  4. Greičiausiai apvažiuotas ratas priklauso Sir Lewis Hamilton
  5. Daugiausiai laimėjimų turi Ferrari komanda
  6. Daugiausiai 1-3 vietų laimėjimų turi Mclearn komanda

Lentelės¶

In [5]:
import pandas as pd
import numpy as np
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
pip install mysql.connector

Requirement already satisfied: mysql.connector in c:\users\simon\anaconda3\lib\site-packages (2.2.9)
Note: you may need to restart the kernel to use updated packages.
In [3]:
import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3317",
    user="root",
    password="***",
)
cursor = mydb.cursor()
cursor.execute('USE formula 1')
cursor.execute('SELECT * FROM circuits')
table_rows = cursor.fetchall()
df = pd.DataFrame(table_rows)
df

---------------------------------------------------------------------------
ProgrammingError                          Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_28564/456834254.py in <module>
      8 )
      9 cursor = mydb.cursor()
---> 10 cursor.execute('USE formula 1')
     11 cursor.execute('SELECT * FROM circuits')
     12 table_rows = cursor.fetchall()

~\anaconda3\lib\site-packages\mysql\connector\cursor.py in execute(self, operation, params, multi)
    549         else:
    550             try:
--> 551                 self._handle_result(self._connection.cmd_query(stmt))
    552             except errors.InterfaceError:
    553                 if self._connection._have_next_result:  # pylint: disable=W0212

~\anaconda3\lib\site-packages\mysql\connector\connection.py in cmd_query(self, query, raw, buffered, raw_as_string)
    488         if not isinstance(query, bytes):
    489             query = query.encode('utf-8')
--> 490         result = self._handle_result(self._send_cmd(ServerCmd.QUERY, query))
    491 
    492         if self._have_next_result:

~\anaconda3\lib\site-packages\mysql\connector\connection.py in _handle_result(self, packet)
    393             return self._handle_eof(packet)
    394         elif packet[4] == 255:
--> 395             raise errors.get_exception(packet)
    396 
    397         # We have a text result set

ProgrammingError: 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '1' at line 1
In [13]:
results = pd.read_csv('C:\\Users\\simon\\F1\\results.csv')
results
Out[13]:
resultId raceId driverId constructorId number grid position positionText positionOrder points laps time milliseconds fastestLap rank fastestLapTime fastestLapSpeed statusId
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
1 2 18 2 2 3 5 2 2 2 8.0 58 +5.478 5696094 41 3 1:27.739 217.586 1
2 3 18 3 3 7 7 3 3 3 6.0 58 +8.163 5698779 41 5 1:28.090 216.719 1
3 4 18 4 4 5 11 4 4 4 5.0 58 +17.181 5707797 58 7 1:28.603 215.464 1
4 5 18 5 1 23 3 5 5 5 4.0 58 +18.014 5708630 43 1 1:27.418 218.385 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
25394 25400 1073 815 9 11 4 15 15 15 0.0 55 \N \N 51 2 1:26.419 219.993 5
25395 25401 1073 849 3 6 16 \N R 16 0.0 50 \N \N 30 15 1:29.293 212.912 3
25396 25402 1073 841 51 99 14 \N R 17 0.0 33 \N \N 33 16 1:29.442 212.557 6
25397 25403 1073 847 3 63 17 \N R 18 0.0 26 \N \N 23 19 1:30.647 209.732 6
25398 25404 1073 8 51 7 18 \N R 19 0.0 25 \N \N 23 18 1:29.698 211.951 23

25399 rows × 18 columns

In [14]:
circuits = pd.read_csv('C:\\Users\\simon\\F1\\circuits.csv')
circuits
Out[14]:
circuitId circuitRef name location country lat lng alt url
0 1 albert_park Albert Park Grand Prix Circuit Melbourne Australia -37.84970 144.96800 10 http://en.wikipedia.org/wiki/Melbourne_Grand_P...
1 2 sepang Sepang International Circuit Kuala Lumpur Malaysia 2.76083 101.73800 18 http://en.wikipedia.org/wiki/Sepang_Internatio...
2 3 bahrain Bahrain International Circuit Sakhir Bahrain 26.03250 50.51060 7 http://en.wikipedia.org/wiki/Bahrain_Internati...
3 4 catalunya Circuit de Barcelona-Catalunya Montmeló Spain 41.57000 2.26111 109 http://en.wikipedia.org/wiki/Circuit_de_Barcel...
4 5 istanbul Istanbul Park Istanbul Turkey 40.95170 29.40500 130 http://en.wikipedia.org/wiki/Istanbul_Park
... ... ... ... ... ... ... ... ... ...
74 75 portimao Autódromo Internacional do Algarve Portimão Portugal 37.22700 -8.62670 108 http://en.wikipedia.org/wiki/Algarve_Internati...
75 76 mugello Autodromo Internazionale del Mugello Mugello Italy 43.99750 11.37190 255 http://en.wikipedia.org/wiki/Mugello_Circuit
76 77 jeddah Jeddah Street Circuit Jeddah Saudi Arabia 21.54330 39.17280 15 http://en.wikipedia.org/wiki/Jeddah_Street_Cir...
77 78 losail Losail International Circuit Al Daayen Qatar 25.49000 51.45420 \N http://en.wikipedia.org/wiki/Losail_Internatio...
78 79 miami Miami International Autodrome Miami United States 25.95810 -80.23890 \N https://en.wikipedia.org/wiki/Miami_Internatio...

79 rows × 9 columns

In [15]:
cons_res = pd.read_csv('C:\\Users\\simon\\F1\\constructor_results.csv')
cons_res
Out[15]:
constructorResultsId raceId constructorId points status
0 1 18 1 14.0 \N
1 2 18 2 8.0 \N
2 3 18 3 9.0 \N
3 4 18 4 5.0 \N
4 5 18 5 2.0 \N
... ... ... ... ... ...
11945 16445 1073 214 6.0 \N
11946 16446 1073 117 0.0 \N
11947 16447 1073 210 0.0 \N
11948 16448 1073 3 0.0 \N
11949 16449 1073 51 0.0 \N

11950 rows × 5 columns

In [16]:
cons_standings = pd.read_csv('C:\\Users\\simon\\F1\\constructor_standings.csv')
cons_standings
Out[16]:
constructorStandingsId raceId constructorId points position positionText wins
0 1 18 1 14.0 1 1 1
1 2 18 2 8.0 3 3 0
2 3 18 3 9.0 2 2 0
3 4 18 4 5.0 4 4 0
4 5 18 5 2.0 5 5 0
... ... ... ... ... ... ... ...
12711 27938 1074 210 0.0 6 - 0
12712 27939 1074 1 0.0 7 - 0
12713 27940 1074 131 0.0 8 - 0
12714 27941 1074 9 0.0 9 - 0
12715 27942 1074 3 0.0 10 - 0

12716 rows × 7 columns

In [17]:
constructors = pd.read_csv('C:\\Users\\simon\\F1\\constructors.csv')
constructors
Out[17]:
constructorId constructorRef name nationality url
0 1 mclaren McLaren British http://en.wikipedia.org/wiki/McLaren
1 2 bmw_sauber BMW Sauber German http://en.wikipedia.org/wiki/BMW_Sauber
2 3 williams Williams British http://en.wikipedia.org/wiki/Williams_Grand_Pr...
3 4 renault Renault French http://en.wikipedia.org/wiki/Renault_in_Formul...
4 5 toro_rosso Toro Rosso Italian http://en.wikipedia.org/wiki/Scuderia_Toro_Rosso
... ... ... ... ... ...
206 209 manor Manor Marussia British http://en.wikipedia.org/wiki/Manor_Motorsport
207 210 haas Haas F1 Team American http://en.wikipedia.org/wiki/Haas_F1_Team
208 211 racing_point Racing Point British http://en.wikipedia.org/wiki/Racing_Point_F1_Team
209 213 alphatauri AlphaTauri Italian http://en.wikipedia.org/wiki/Scuderia_AlphaTauri
210 214 alpine Alpine F1 Team French http://en.wikipedia.org/wiki/Alpine_F1_Team

211 rows × 5 columns

In [18]:
driver_standings = pd.read_csv('C:\\Users\\simon\\F1\\driver_standings.csv')
driver_standings
Out[18]:
driverStandingsId raceId driverId points position positionText wins
0 1 18 1 10.0 1 1 1
1 2 18 2 8.0 2 2 0
2 3 18 3 6.0 3 3 0
3 4 18 4 5.0 4 4 0
4 5 18 5 4.0 5 5 0
... ... ... ... ... ... ... ...
33389 70776 1074 840 0.0 16 - 0
33390 70777 1074 852 0.0 17 - 0
33391 70778 1074 830 0.0 18 - 0
33392 70779 1074 20 0.0 19 - 0
33393 70780 1074 855 0.0 20 - 0

33394 rows × 7 columns

In [19]:
drivers = pd.read_csv('C:\\Users\\simon\\F1\\drivers.csv')
drivers
Out[19]:
driverId driverRef number code forename surname dob nationality url
0 1 hamilton 44 HAM Lewis Hamilton 1985-01-07 British http://en.wikipedia.org/wiki/Lewis_Hamilton
1 2 heidfeld \N HEI Nick Heidfeld 1977-05-10 German http://en.wikipedia.org/wiki/Nick_Heidfeld
2 3 rosberg 6 ROS Nico Rosberg 1985-06-27 German http://en.wikipedia.org/wiki/Nico_Rosberg
3 4 alonso 14 ALO Fernando Alonso 1981-07-29 Spanish http://en.wikipedia.org/wiki/Fernando_Alonso
4 5 kovalainen \N KOV Heikki Kovalainen 1981-10-19 Finnish http://en.wikipedia.org/wiki/Heikki_Kovalainen
... ... ... ... ... ... ... ... ... ...
849 851 aitken 89 AIT Jack Aitken 1995-09-23 British http://en.wikipedia.org/wiki/Jack_Aitken
850 852 tsunoda 22 TSU Yuki Tsunoda 2000-05-11 Japanese http://en.wikipedia.org/wiki/Yuki_Tsunoda
851 853 mazepin 9 MAZ Nikita Mazepin 1999-03-02 Russian http://en.wikipedia.org/wiki/Nikita_Mazepin
852 854 mick_schumacher 47 MSC Mick Schumacher 1999-03-22 German http://en.wikipedia.org/wiki/Mick_Schumacher
853 855 zhou \N ZHO Guanyu Zhou 1999-05-30 Chinese https://en.wikipedia.org/wiki/Guanyu_Zhou

854 rows × 9 columns

In [20]:
lap_times = pd.read_csv('C:\\Users\\simon\\F1\\lap_times.csv')
lap_times
Out[20]:
raceId driverId lap position time milliseconds
0 841 20 1 1 1:38.109 98109
1 841 20 2 1 1:33.006 93006
2 841 20 3 1 1:32.713 92713
3 841 20 4 1 1:32.803 92803
4 841 20 5 1 1:32.342 92342
... ... ... ... ... ... ...
514587 1073 847 22 15 1:30.821 90821
514588 1073 847 23 15 1:30.647 90647
514589 1073 847 24 14 1:31.577 91577
514590 1073 847 25 16 1:32.794 92794
514591 1073 847 26 18 2:46.262 166262

514592 rows × 6 columns

In [21]:
pit_stops = pd.read_csv('C:\\Users\\simon\\F1\\pit_stops.csv')
pit_stops
Out[21]:
raceId driverId stop lap time duration milliseconds
0 841 153 1 1 17:05:23 26.898 26898
1 841 30 1 1 17:05:52 25.021 25021
2 841 17 1 11 17:20:48 23.426 23426
3 841 4 1 12 17:22:34 23.251 23251
4 841 13 1 13 17:24:10 23.842 23842
... ... ... ... ... ... ... ...
8823 1073 840 2 52 18:22:55 22.661 22661
8824 1073 815 3 53 18:23:09 21.385 21385
8825 1073 854 2 52 18:23:42 22.070 22070
8826 1073 852 2 53 18:24:01 21.909 21909
8827 1073 842 2 54 18:25:56 21.920 21920

8828 rows × 7 columns

In [22]:
qualy = pd.read_csv('C:\\Users\\simon\\F1\\qualifying.csv')
qualy
Out[22]:
qualifyId raceId driverId constructorId number position q1 q2 q3
0 1 18 1 1 22 1 1:26.572 1:25.187 1:26.714
1 2 18 9 2 4 2 1:26.103 1:25.315 1:26.869
2 3 18 5 1 23 3 1:25.664 1:25.452 1:27.079
3 4 18 13 6 2 4 1:25.994 1:25.691 1:27.178
4 5 18 2 2 3 5 1:25.960 1:25.518 1:27.236
... ... ... ... ... ... ... ... ... ...
9130 9171 1073 849 3 6 16 1:24.338 \N \N
9131 9172 1073 847 3 63 17 1:24.423 \N \N
9132 9173 1073 8 51 7 18 1:24.779 \N \N
9133 9174 1073 854 210 47 19 1:24.906 \N \N
9134 9175 1073 853 210 9 20 1:25.685 \N \N

9135 rows × 9 columns

In [23]:
races = pd.read_csv('C:\\Users\\simon\\F1\\races.csv')
races
Out[23]:
raceId year round circuitId name date time url
0 1 2009 1 1 Australian Grand Prix 2009-03-29 06:00:00 http://en.wikipedia.org/wiki/2009_Australian_G...
1 2 2009 2 2 Malaysian Grand Prix 2009-04-05 09:00:00 http://en.wikipedia.org/wiki/2009_Malaysian_Gr...
2 3 2009 3 17 Chinese Grand Prix 2009-04-19 07:00:00 http://en.wikipedia.org/wiki/2009_Chinese_Gran...
3 4 2009 4 3 Bahrain Grand Prix 2009-04-26 12:00:00 http://en.wikipedia.org/wiki/2009_Bahrain_Gran...
4 5 2009 5 4 Spanish Grand Prix 2009-05-10 12:00:00 http://en.wikipedia.org/wiki/2009_Spanish_Gran...
... ... ... ... ... ... ... ... ...
1075 1092 2022 19 22 Japanese Grand Prix 2022-10-09 05:10:00 https://en.wikipedia.org/wiki/2022_Japanese_Gr...
1076 1093 2022 20 69 United States Grand Prix 2022-10-23 19:00:00 https://en.wikipedia.org/wiki/2022_United_Stat...
1077 1094 2022 21 32 Mexico City Grand Prix 2022-10-30 19:00:00 https://en.wikipedia.org/wiki/2022_Mexican_Gra...
1078 1095 2022 22 18 São Paulo Grand Prix 2022-11-13 17:00:00 https://en.wikipedia.org/wiki/2022_S%C3%A3o_Pa...
1079 1096 2022 23 24 Abu Dhabi Grand Prix 2022-11-20 13:00:00 https://en.wikipedia.org/wiki/2022_Abu_Dhabi_G...

1080 rows × 8 columns

In [24]:
results = pd.read_csv('C:\\Users\\simon\\F1\\results.csv')
results
Out[24]:
resultId raceId driverId constructorId number grid position positionText positionOrder points laps time milliseconds fastestLap rank fastestLapTime fastestLapSpeed statusId
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
1 2 18 2 2 3 5 2 2 2 8.0 58 +5.478 5696094 41 3 1:27.739 217.586 1
2 3 18 3 3 7 7 3 3 3 6.0 58 +8.163 5698779 41 5 1:28.090 216.719 1
3 4 18 4 4 5 11 4 4 4 5.0 58 +17.181 5707797 58 7 1:28.603 215.464 1
4 5 18 5 1 23 3 5 5 5 4.0 58 +18.014 5708630 43 1 1:27.418 218.385 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
25394 25400 1073 815 9 11 4 15 15 15 0.0 55 \N \N 51 2 1:26.419 219.993 5
25395 25401 1073 849 3 6 16 \N R 16 0.0 50 \N \N 30 15 1:29.293 212.912 3
25396 25402 1073 841 51 99 14 \N R 17 0.0 33 \N \N 33 16 1:29.442 212.557 6
25397 25403 1073 847 3 63 17 \N R 18 0.0 26 \N \N 23 19 1:30.647 209.732 6
25398 25404 1073 8 51 7 18 \N R 19 0.0 25 \N \N 23 18 1:29.698 211.951 23

25399 rows × 18 columns

In [25]:
seasons = pd.read_csv('C:\\Users\\simon\\F1\\seasons.csv')
seasons
Out[25]:
year url
0 2009 https://en.wikipedia.org/wiki/2009_Formula_One...
1 2008 https://en.wikipedia.org/wiki/2008_Formula_One...
2 2007 https://en.wikipedia.org/wiki/2007_Formula_One...
3 2006 https://en.wikipedia.org/wiki/2006_Formula_One...
4 2005 https://en.wikipedia.org/wiki/2005_Formula_One...
... ... ...
68 2018 https://en.wikipedia.org/wiki/2018_Formula_One...
69 2019 https://en.wikipedia.org/wiki/2019_Formula_One...
70 2020 https://en.wikipedia.org/wiki/2020_Formula_One...
71 2021 https://en.wikipedia.org/wiki/2021_Formula_One...
72 2022 https://en.wikipedia.org/wiki/2022_Formula_One...

73 rows × 2 columns

In [26]:
status = pd.read_csv('C:\\Users\\simon\\F1\\status.csv')
status
Out[26]:
statusId status
0 1 Finished
1 2 Disqualified
2 3 Accident
3 4 Collision
4 5 Engine
... ... ...
132 135 Brake duct
133 136 Seat
134 137 Damage
135 138 Debris
136 139 Illness

137 rows × 2 columns

Analizė¶

Micheal Schumacher yra geriausias F1 vairutojas¶

In [141]:
results = pd.read_csv('C:\\Users\\simon\\F1\\results.csv')
results
Out[141]:
resultId raceId driverId constructorId number grid position positionText positionOrder points laps time milliseconds fastestLap rank fastestLapTime fastestLapSpeed statusId
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
1 2 18 2 2 3 5 2 2 2 8.0 58 +5.478 5696094 41 3 1:27.739 217.586 1
2 3 18 3 3 7 7 3 3 3 6.0 58 +8.163 5698779 41 5 1:28.090 216.719 1
3 4 18 4 4 5 11 4 4 4 5.0 58 +17.181 5707797 58 7 1:28.603 215.464 1
4 5 18 5 1 23 3 5 5 5 4.0 58 +18.014 5708630 43 1 1:27.418 218.385 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
25394 25400 1073 815 9 11 4 15 15 15 0.0 55 \N \N 51 2 1:26.419 219.993 5
25395 25401 1073 849 3 6 16 \N R 16 0.0 50 \N \N 30 15 1:29.293 212.912 3
25396 25402 1073 841 51 99 14 \N R 17 0.0 33 \N \N 33 16 1:29.442 212.557 6
25397 25403 1073 847 3 63 17 \N R 18 0.0 26 \N \N 23 19 1:30.647 209.732 6
25398 25404 1073 8 51 7 18 \N R 19 0.0 25 \N \N 23 18 1:29.698 211.951 23

25399 rows × 18 columns

In [27]:
laimi = results[results['positionText']=='1'].groupby('driverId')['positionText'].count().sort_values()
laimi
Out[27]:
driverId
386      1
525      1
628      1
255      1
611      1
      ... 
102     41
117     51
20      53
30      91
1      103
Name: positionText, Length: 111, dtype: int64
In [28]:
grid = results[results['grid']==1].groupby('driverId')['grid'].count().sort_values()
grid
Out[28]:
driverId
846      1
654      1
123      1
152      1
633      1
      ... 
373     34
20      57
102     65
30      68
1      103
Name: grid, Length: 107, dtype: int64
In [29]:
lenk_sk = results[['raceId', 'driverId']].groupby('driverId')['raceId'].count().sort_values()
lenk_sk
Out[29]:
driverId
378      1
443      1
426      1
422      1
421      1
      ... 
30     308
18     309
22     326
4      336
8      352
Name: raceId, Length: 853, dtype: int64
In [34]:
proc_1 = (laimi / lenk_sk) * 100
proc_1.dropna(how='any').sort_values(ascending=False)
Out[34]:
driverId
766    50.000000
579    41.379310
657    40.000000
647    36.111111
1      35.763889
         ...    
5       0.892857
200     0.877193
44      0.632911
55      0.495050
15      0.390625
Length: 111, dtype: float64
In [35]:
proc_1.iloc[29]
Out[35]:
29.545454545454547
In [73]:
proc_1.iloc[0]
Out[73]:
35.76388888888889
In [36]:
proc_2 = grid / lenk_sk * 100
proc_2.dropna(how='any').sort_values(ascending=False)
Out[36]:
driverId
579    50.000000
373    46.575342
102    40.123457
647    38.888889
1      35.763889
         ...    
304     0.892857
123     0.609756
807     0.549451
2       0.543478
110     0.467290
Length: 107, dtype: float64
In [37]:
proc_2.iloc[29]
Out[37]:
22.07792207792208
In [74]:
proc_2.iloc[0]
Out[74]:
35.76388888888889
In [54]:
print(f'Pagal duombazės duomenis Micheal Schumacher dalyvavo {lenk_sk[30]} varžybose, iš kurių laimėjo {laimi[30]}, o pradėjo varžybas pirmoje pozicijoje {grid[30]} kartus. Tai kiek varžybų dalyvavo ir kiek jų laimėjo  procentaliai jo efektivumas yra {proc_1[30]}. Lygininat kvalifikacijos ir lenktynių pozicijas, procentaliai efektivumas yra {proc_2[30]}.')

Pagal duombazės duomenis Micheal Schumacher dalyvavo 308 varžybose, iš kurių laimėjo 91, o pradėjo varžybas pirmoje pozicijoje 68 kartus. Tai kiek varžybų dalyvavo ir kiek jų laimėjo  procentaliai jo efektivumas yra 29.545454545454547. Lygininat kvalifikacijos ir lenktynių pozicijas, procentaliai efektivumas yra 22.07792207792208.
In [60]:
print(f'Lygininat M.Schumacher su kitu 7 kartus pasaulio čempionu L. Hamiltonu, mes matome, kad jisai dalyvavo {lenk_sk[1]} varžybose, iš kurių laimėjo {laimi[1]}, procentaliai jo efektyvumas yra {proc_1[1]}. Kalbant apie kvalifikaciją, Hamiltonas iškovojo {grid[1]} kartus pirmąją vietą ir jo efektyvumas procentaliai yra {proc_2[1]}')

Lygininat M.Schumacher su kitu 7 kartus pasaulio čempionu L. Hamiltonu, mes matome, kad jisai dalyvavo 288 varžybose, iš kurių laimėjo 103, procentaliai jo efektyvumas yra 35.76388888888889. Kalbant apie kvalifikaciją, Hamiltonas iškovojo 103 kartus pirmąją vietą ir jo efektyvumas procentaliai yra 35.76388888888889
In [147]:
df = pd.DataFrame({'M.Schumacher': [7, 308, 91, 68, 29.55, 22.08], 'L.Hamilton': [7, 288, 103, 103, 35.76, 35.76], 'Skirtumas':[0, 20, 12, 35, 6.21, 13.68]}, index=['Laimėti čempionatai', 'Viso važiuotu varžybų', 'Viso laimėtu varžybu', 'Kvalifikacija P1', 'Proc. Varžybos vs laimėjimų sk.', 'Proc. Kvalifikacija vs varžybų sk.'])
df
Out[147]:
M.Schumacher L.Hamilton Skirtumas
Laimėti čempionatai 7.00 7.00 0.00
Viso važiuotu varžybų 308.00 288.00 20.00
Viso laimėtu varžybu 91.00 103.00 12.00
Kvalifikacija P1 68.00 103.00 35.00
Proc. Varžybos vs laimėjimų sk. 29.55 35.76 6.21
Proc. Kvalifikacija vs varžybų sk. 22.08 35.76 13.68
In [150]:
df = pd.DataFrame({'M.Schumacher': [7, 308, 91, 68, 29.55, 22.08], 'L.Hamilton': [7, 288, 103, 103, 35.76, 35.76]}, index=['Laimėti čempionatai', 'Viso važiuotu varžybų', 'Viso laimėtu varžybu', 'Kvalifikacija P1', 'Proc. Varžybos vs laimėjimų sk.', 'Proc. Kvalifikacija vs varžybų sk.'])
df
Out[150]:
M.Schumacher L.Hamilton
Laimėti čempionatai 7.00 7.00
Viso važiuotu varžybų 308.00 288.00
Viso laimėtu varžybu 91.00 103.00
Kvalifikacija P1 68.00 103.00
Proc. Varžybos vs laimėjimų sk. 29.55 35.76
Proc. Kvalifikacija vs varžybų sk. 22.08 35.76
In [151]:
df.plot.area()
plt.xticks(rotation ='vertical')
Out[151]:
(array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.]),
 [Text(-1.0, 0, 'Proc. Kvalifikacija vs varžybų sk.'),
  Text(0.0, 0, 'Laimėti čempionatai'),
  Text(1.0, 0, 'Viso važiuotu varžybų'),
  Text(2.0, 0, 'Viso laimėtu varžybu'),
  Text(3.0, 0, 'Kvalifikacija P1'),
  Text(4.0, 0, 'Proc. Varžybos vs laimėjimų sk.'),
  Text(5.0, 0, 'Proc. Kvalifikacija vs varžybų sk.'),
  Text(6.0, 0, '')])
In [75]:
drivers.iloc[29]
Out[75]:
driverId                                                    30
driverRef                                   michael_schumacher
number                                                      \N
code                                                       MSC
forename                                               Michael
surname                                             Schumacher
dob                                                 1969-01-03
nationality                                             German
url            http://en.wikipedia.org/wiki/Michael_Schumacher
Name: 29, dtype: object
In [97]:
drivers.iloc[0]
Out[97]:
driverId                                                 1
driverRef                                         hamilton
number                                                  44
code                                                   HAM
forename                                             Lewis
surname                                           Hamilton
dob                                             1985-01-07
nationality                                        British
url            http://en.wikipedia.org/wiki/Lewis_Hamilton
Name: 0, dtype: object
In [42]:
drivers[drivers['surname'] == 'Schumacher']
Out[42]:
driverId driverRef number code forename surname dob nationality url
22 23 ralf_schumacher \N SCH Ralf Schumacher 1975-06-30 German http://en.wikipedia.org/wiki/Ralf_Schumacher
29 30 michael_schumacher \N MSC Michael Schumacher 1969-01-03 German http://en.wikipedia.org/wiki/Michael_Schumacher
852 854 mick_schumacher 47 MSC Mick Schumacher 1999-03-22 German http://en.wikipedia.org/wiki/Mick_Schumacher
In [98]:
drivers[drivers['surname'] == 'Hamilton']
Out[98]:
driverId driverRef number code forename surname dob nationality url
0 1 hamilton 44 HAM Lewis Hamilton 1985-01-07 British http://en.wikipedia.org/wiki/Lewis_Hamilton
708 708 duncan_hamilton \N \N Duncan Hamilton 1920-04-30 British http://en.wikipedia.org/wiki/Duncan_Hamilton_(...

Į 10 geriausių kvalifikacijoje pasirodžiusių vairuotojų įeina, Max Verstappen¶

In [212]:
qualy = pd.read_csv('C:\\Users\\simon\\F1\\qualifying.csv')
qualy
Out[212]:
qualifyId raceId driverId constructorId number position q1 q2 q3
0 1 18 1 1 22 1 1:26.572 1:25.187 1:26.714
1 2 18 9 2 4 2 1:26.103 1:25.315 1:26.869
2 3 18 5 1 23 3 1:25.664 1:25.452 1:27.079
3 4 18 13 6 2 4 1:25.994 1:25.691 1:27.178
4 5 18 2 2 3 5 1:25.960 1:25.518 1:27.236
... ... ... ... ... ... ... ... ... ...
9130 9171 1073 849 3 6 16 1:24.338 \N \N
9131 9172 1073 847 3 63 17 1:24.423 \N \N
9132 9173 1073 8 51 7 18 1:24.779 \N \N
9133 9174 1073 854 210 47 19 1:24.906 \N \N
9134 9175 1073 853 210 9 20 1:25.685 \N \N

9135 rows × 9 columns

In [99]:
qualy[['driverId', 'position']].groupby('driverId')['position'].mean().sort_values(ascending=True).head(10)
Out[99]:
driverId
102    1.000000
1      3.350694
95     4.833333
71     5.372881
830    5.638298
822    5.648045
20     5.666667
30     5.666667
77     5.760870
31     6.031250
Name: position, dtype: float64
In [208]:
drivers.iloc[[101, 0, 94, 70, 829, 821, 19, 29, 76, 30]]
Out[208]:
driverId driverRef number code forename surname dob nationality url
101 102 senna \N \N Ayrton Senna 1960-03-21 Brazilian http://en.wikipedia.org/wiki/Ayrton_Senna
0 1 hamilton 44 HAM Lewis Hamilton 1985-01-07 British http://en.wikipedia.org/wiki/Lewis_Hamilton
94 95 mansell \N \N Nigel Mansell 1953-08-08 British http://en.wikipedia.org/wiki/Nigel_Mansell
70 71 damon_hill \N \N Damon Hill 1960-09-17 British http://en.wikipedia.org/wiki/Damon_Hill
829 830 max_verstappen 33 VER Max Verstappen 1997-09-30 Dutch http://en.wikipedia.org/wiki/Max_Verstappen
821 822 bottas 77 BOT Valtteri Bottas 1989-08-28 Finnish http://en.wikipedia.org/wiki/Valtteri_Bottas
19 20 vettel 5 VET Sebastian Vettel 1987-07-03 German http://en.wikipedia.org/wiki/Sebastian_Vettel
29 30 michael_schumacher \N MSC Michael Schumacher 1969-01-03 German http://en.wikipedia.org/wiki/Michael_Schumacher
76 77 berger \N \N Gerhard Berger 1959-08-27 Austrian http://en.wikipedia.org/wiki/Gerhard_Berger
30 31 montoya \N MON Juan Pablo Montoya 1975-09-20 Colombian http://en.wikipedia.org/wiki/Juan_Pablo_Montoya

10 blogiausiai kvalifikavusių vairuotojų:

In [173]:
qualy[['driverId', 'position']].groupby('driverId')['position'].mean().sort_values(ascending=True).tail(10)
Out[173]:
driverId
812    23.181818
93     23.285714
115    23.333333
86     23.750000
101    24.333333
98     24.333333
113    25.500000
111    25.714286
107    26.000000
108    26.500000
Name: position, dtype: float64
In [209]:
drivers.iloc[[811, 92, 114, 85, 100, 97, 112, 110, 106, 107]]
Out[209]:
driverId driverRef number code forename surname dob nationality url
811 812 chandhok \N CHA Karun Chandhok 1984-01-19 Indian http://en.wikipedia.org/wiki/Karun_Chandhok
92 93 schiattarella \N \N Domenico Schiattarella 1967-11-17 Italian http://en.wikipedia.org/wiki/Domenico_Schiatta...
114 115 noda \N \N Hideki Noda 1969-03-07 Japanese http://en.wikipedia.org/wiki/Hideki_Noda
85 86 lavaggi \N \N Giovanni Lavaggi 1958-02-18 Italian http://en.wikipedia.org/wiki/Giovanni_Lavaggi
100 101 brabham \N \N David Brabham 1965-09-05 Australian http://en.wikipedia.org/wiki/David_Brabham
97 98 deletraz \N \N Jean-Denis Délétraz 1963-10-01 Swiss http://en.wikipedia.org/wiki/Jean-Denis_Deletraz
112 113 adams \N \N Philippe Adams 1969-11-19 Belgian http://en.wikipedia.org/wiki/Philippe_Adams
110 111 gounon \N \N Jean-Marc Gounon 1963-01-01 French http://en.wikipedia.org/wiki/Jean-Marc_Gounon
106 107 ratzenberger \N \N Roland Ratzenberger 1960-07-04 Austrian http://en.wikipedia.org/wiki/Roland_Ratzenberger
107 108 belmondo \N \N Paul Belmondo 1963-04-23 French http://en.wikipedia.org/wiki/Paul_Belmondo
In [184]:
qualy[['driverId', 'position']].groupby('driverId')['position'].mean().sort_values(ascending=True)
Out[184]:
driverId
102     1.000000
1       3.350694
95      4.833333
71      5.372881
830     5.638298
         ...    
98     24.333333
113    25.500000
111    25.714286
107    26.000000
108    26.500000
Name: position, Length: 164, dtype: float64

Daugiausiai kartų varžybos yra vykusios Italijoje¶

In [28]:
races['name'].value_counts()
Out[28]:
British Grand Prix               73
Italian Grand Prix               73
Monaco Grand Prix                68
Belgian Grand Prix               67
German Grand Prix                64
French Grand Prix                62
Spanish Grand Prix               52
Canadian Grand Prix              51
Brazilian Grand Prix             47
United States Grand Prix         43
Hungarian Grand Prix             37
Australian Grand Prix            36
Japanese Grand Prix              36
Austrian Grand Prix              35
Dutch Grand Prix                 32
San Marino Grand Prix            26
South African Grand Prix         23
European Grand Prix              23
Argentine Grand Prix             20
Mexican Grand Prix               20
Malaysian Grand Prix             19
Bahrain Grand Prix               18
Portuguese Grand Prix            18
Chinese Grand Prix               16
Abu Dhabi Grand Prix             14
Singapore Grand Prix             13
Indianapolis 500                 11
Turkish Grand Prix                9
Russian Grand Prix                9
United States Grand Prix West     8
Detroit Grand Prix                7
Swiss Grand Prix                  6
Swedish Grand Prix                6
Azerbaijan Grand Prix             5
Korean Grand Prix                 4
Indian Grand Prix                 3
Emilia Romagna Grand Prix         3
Pacific Grand Prix                2
Saudi Arabian Grand Prix          2
São Paulo Grand Prix              2
Luxembourg Grand Prix             2
Styrian Grand Prix                2
Caesars Palace Grand Prix         2
Mexico City Grand Prix            2
Dallas Grand Prix                 1
Sakhir Grand Prix                 1
Qatar Grand Prix                  1
70th Anniversary Grand Prix       1
Eifel Grand Prix                  1
Tuscan Grand Prix                 1
Pescara Grand Prix                1
Moroccan Grand Prix               1
Miami Grand Prix                  1
Name: name, dtype: int64
In [27]:
races['name'].value_counts().plot.bar()
Out[27]:
<AxesSubplot:>

Greičiausiai apvažiuotas ratas priklauso Sir Lewis Hamilton¶

In [102]:
results[['fastestLapTime', 'driverId', 'raceId']].sort_values('fastestLapTime')
Out[102]:
fastestLapTime driverId raceId
24928 0:55.404 847 1046
24927 0:56.563 822 1046
24920 0:56.789 815 1046
24932 0:56.887 841 1046
24931 0:56.905 20 1046
... ... ... ...
10781 \N 158 456
10780 \N 197 456
10779 \N 173 456
10787 \N 95 456
12699 \N 178 524

25399 rows × 3 columns

In [33]:
races[['raceId', 'name', 'date']].iloc[1033]
Out[33]:
raceId                 1046
name      Sakhir Grand Prix
date             2020-12-06
Name: 1033, dtype: object
In [42]:
drivers[['driverId', 'forename', 'surname']].iloc[845]
Out[42]:
driverId        847
forename     George
surname     Russell
Name: 845, dtype: object

Daugiausiai laimėjimų turi Ferrari komanda¶

In [91]:
cons_standings = pd.read_csv('C:\\Users\\simon\\F1\\constructor_standings.csv')
cons_standings.sort_values('position',ascending=False)
Out[91]:
constructorStandingsId raceId constructorId points position positionText wins
9926 19934 728 173 0.0 22 22 0
10078 23145 575 74 0.0 21 21 0
7231 23427 559 69 0.0 21 21 0
9090 19935 728 179 0.0 21 21 0
4114 8948 364 44 0.0 20 20 0
... ... ... ... ... ... ... ...
8235 22018 631 32 61.0 1 1 5
8223 22006 630 32 61.0 1 1 5
8211 21994 629 32 61.0 1 1 5
8199 21982 628 32 52.0 1 1 4
6358 11410 505 3 57.0 1 1 4

12716 rows × 7 columns

In [263]:
cons_standings[cons_standings['position'] == 1].groupby('constructorId')['positionText'].count().sort_values(ascending=False)
Out[263]:
constructorId
6      229
1      165
131    145
3      114
9       69
4       46
32      44
22      29
172     23
170     20
23      17
25      16
191     16
66      14
180     12
196     11
34       6
87       4
27       4
118      3
37       3
59       1
167      1
51       1
2        1
Name: positionText, dtype: int64
In [260]:
constructors[constructors['constructorId'] == 6]
Out[260]:
constructorId constructorRef name nationality url
5 6 ferrari Ferrari Italian http://en.wikipedia.org/wiki/Scuderia_Ferrari
In [90]:
constructors.head(18)
Out[90]:
constructorId constructorRef name nationality url
0 1 mclaren McLaren British http://en.wikipedia.org/wiki/McLaren
1 2 bmw_sauber BMW Sauber German http://en.wikipedia.org/wiki/BMW_Sauber
2 3 williams Williams British http://en.wikipedia.org/wiki/Williams_Grand_Pr...
3 4 renault Renault French http://en.wikipedia.org/wiki/Renault_in_Formul...
4 5 toro_rosso Toro Rosso Italian http://en.wikipedia.org/wiki/Scuderia_Toro_Rosso
5 6 ferrari Ferrari Italian http://en.wikipedia.org/wiki/Scuderia_Ferrari
6 7 toyota Toyota Japanese http://en.wikipedia.org/wiki/Toyota_Racing
7 8 super_aguri Super Aguri Japanese http://en.wikipedia.org/wiki/Super_Aguri_F1
8 9 red_bull Red Bull Austrian http://en.wikipedia.org/wiki/Red_Bull_Racing
9 10 force_india Force India Indian http://en.wikipedia.org/wiki/Racing_Point_Forc...
10 11 honda Honda Japanese http://en.wikipedia.org/wiki/Honda_Racing_F1
11 12 spyker Spyker Dutch http://en.wikipedia.org/wiki/Spyker_F1
12 13 mf1 MF1 Russian http://en.wikipedia.org/wiki/Midland_F1_Racing
13 14 spyker_mf1 Spyker MF1 Dutch http://en.wikipedia.org/wiki/Midland_F1_Racing
14 15 sauber Sauber Swiss http://en.wikipedia.org/wiki/Sauber
15 16 bar BAR British http://en.wikipedia.org/wiki/British_American_...
16 17 jordan Jordan Irish http://en.wikipedia.org/wiki/Jordan_Grand_Prix
17 18 minardi Minardi Italian http://en.wikipedia.org/wiki/Minardi

Daugiausiai 1-3 vietų laimėjimų turi Mclearn komanda¶

In [152]:
results[['driverId', 'constructorId', 'positionText']]
Out[152]:
driverId constructorId positionText
0 1 1 1
1 2 2 2
2 3 3 3
3 4 4 4
4 5 1 5
... ... ... ...
25394 815 9 15
25395 849 3 R
25396 841 51 R
25397 847 3 R
25398 8 51 R

25399 rows × 3 columns

In [265]:
results[(results['positionText'] == '1') | 
        (results['positionText'] == '2') | 
        (results['positionText'] == '3')].groupby('constructorId')['positionText'].count().sort_values(ascending=False)
Out[265]:
constructorId
6      790
1      477
3      313
131    264
9      206
      ... 
46       1
132      1
109      1
125      1
181      1
Name: positionText, Length: 76, dtype: int64
In [298]:
constructors[(constructors['constructorId'] == 1) |
            (constructors['constructorId'] == 6) |
            (constructors['constructorId'] == 3)|
            (constructors['constructorId'] == 131)]
Out[298]:
constructorId constructorRef name nationality url
0 1 mclaren McLaren British http://en.wikipedia.org/wiki/McLaren
2 3 williams Williams British http://en.wikipedia.org/wiki/Williams_Grand_Pr...
5 6 ferrari Ferrari Italian http://en.wikipedia.org/wiki/Scuderia_Ferrari
129 131 mercedes Mercedes German http://en.wikipedia.org/wiki/Mercedes-Benz_in_...
In [279]:
constructors.head(30)
Out[279]:
constructorId constructorRef name nationality url
0 1 mclaren McLaren British http://en.wikipedia.org/wiki/McLaren
1 2 bmw_sauber BMW Sauber German http://en.wikipedia.org/wiki/BMW_Sauber
2 3 williams Williams British http://en.wikipedia.org/wiki/Williams_Grand_Pr...
3 4 renault Renault French http://en.wikipedia.org/wiki/Renault_in_Formul...
4 5 toro_rosso Toro Rosso Italian http://en.wikipedia.org/wiki/Scuderia_Toro_Rosso
5 6 ferrari Ferrari Italian http://en.wikipedia.org/wiki/Scuderia_Ferrari
6 7 toyota Toyota Japanese http://en.wikipedia.org/wiki/Toyota_Racing
7 8 super_aguri Super Aguri Japanese http://en.wikipedia.org/wiki/Super_Aguri_F1
8 9 red_bull Red Bull Austrian http://en.wikipedia.org/wiki/Red_Bull_Racing
9 10 force_india Force India Indian http://en.wikipedia.org/wiki/Racing_Point_Forc...
10 11 honda Honda Japanese http://en.wikipedia.org/wiki/Honda_Racing_F1
11 12 spyker Spyker Dutch http://en.wikipedia.org/wiki/Spyker_F1
12 13 mf1 MF1 Russian http://en.wikipedia.org/wiki/Midland_F1_Racing
13 14 spyker_mf1 Spyker MF1 Dutch http://en.wikipedia.org/wiki/Midland_F1_Racing
14 15 sauber Sauber Swiss http://en.wikipedia.org/wiki/Sauber
15 16 bar BAR British http://en.wikipedia.org/wiki/British_American_...
16 17 jordan Jordan Irish http://en.wikipedia.org/wiki/Jordan_Grand_Prix
17 18 minardi Minardi Italian http://en.wikipedia.org/wiki/Minardi
18 19 jaguar Jaguar British http://en.wikipedia.org/wiki/Jaguar_Racing
19 20 prost Prost French http://en.wikipedia.org/wiki/Prost_Grand_Prix
20 21 arrows Arrows British http://en.wikipedia.org/wiki/Arrows_Grand_Prix...
21 22 benetton Benetton Italian http://en.wikipedia.org/wiki/Benetton_Formula
22 23 brawn Brawn British http://en.wikipedia.org/wiki/Brawn_GP
23 24 stewart Stewart British http://en.wikipedia.org/wiki/Stewart_Grand_Prix
24 25 tyrrell Tyrrell British http://en.wikipedia.org/wiki/Tyrrell_Racing
25 26 lola Lola British http://en.wikipedia.org/wiki/MasterCard_Lola
26 27 ligier Ligier French http://en.wikipedia.org/wiki/Ligier
27 28 forti Forti Italian http://en.wikipedia.org/wiki/Forti
28 29 footwork Footwork British http://en.wikipedia.org/wiki/Footwork_Arrows
29 30 pacific Pacific British http://en.wikipedia.org/wiki/Pacific_Racing

Išvados¶

In [ ]:
1. Micheal Schumacher yra geriausias F1 vairutojas. 
Hipotezė nepatvirtinta, nes, nepaisant to, kad M.Schumacher turi 7 pasaulio čempionų titulus, kaip ir Sir L. Hamilton
visi kiti rezultatai buvo pagerinti Sir Lewis Hamilton.
In [ ]:
2. Į 10 geriausių kvalifikacijoje pasirodžiusių vairuotojų įeina, Max Verstappen.
Hipotezė patvirtinta. M. Verstappen yra 5 vietoje iš 10.
In [ ]:
3. Daugiausiai kartų varžybos yra vykusios Italijoje.
Taip Italijoje vyko F1 varžybos nuo pačių pradžių, t.y. 1950 m. Taip pat, kaip ir Didžiojoje Britanijoje
In [ ]:
4. Greičiausiai apvažiuotas ratas priklauso Sir Lewis Hamilton.
Greičiasias apvažiuotas ratas priklauso George Russel, 2020-12-06, Sakhir Grand Prix, Bahreine.
In [ ]:
5. Daugiausiai laimėjimų turi Ferrari komanda. 
Taip, iš viso Ferrai komanda 1950 - 2021 yra laimėjus 299 pirmųjų vietų.
In [ ]:
6. Daugiausiai 1-3 vietų laimėjimų turi Mclearn komanda.
Daugiausiai 1-3 vietų laimėjo Ferrai komanda. Mclearn yra 1950 - 2021 laikotarpiu 2-oje vietoje.