done

+
Merge pull request 'gusev_vladislav_lab_3' (#62 ) from gusev_vladislav_lab_3 into main
2023-11-06 23:11:22 +04:00 · 2023-10-29 21:02:40 +04:00 · 2023-10-24 16:48:23 +04:00 · 2023-10-24 16:48:10 +04:00 · 2023-10-24 16:47:50 +04:00 · 2023-10-24 16:38:21 +04:00
42 changed files with 56648 additions and 6 deletions
--- a/.idea/IIS_2023_1.iml
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--- a/abanin_daniil_lab_3/README.md
+++ b/abanin_daniil_lab_3/README.md
@@ -0,0 +1,27 @@
+## Лабораторная работа №3
+
+### Деревья решений
+
+## Cтудент группы ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (lab3)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* Выполняет ранжирование признаков для регрессионной модели
+* По данным "Eligibility Prediction for Loan" решает задачу классификации (с помощью дерева решений), в которой необходимо выявить риски выдачи кредита и определить его статус (выдан или отказ). В качестве исходных данных используются три признака: Credit_History - соответствие кредитной истории стандартам банка, ApplicantIncome - доход заявителя, LoanAmount - сумма кредита.
+
+### Примеры работы:
+
+#### Результаты:
+* Наиболее важным параметром при выдачи кредита оказался доход заявителя - ApplicantIncome, затем LoanAmount - сумма выдаваемого кредита
+
+![Result](result.png)
--- a/abanin_daniil_lab_3/lab3.py
+++ b/abanin_daniil_lab_3/lab3.py
@@ -0,0 +1,33 @@
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+import pandas as pd
+import numpy as np
+
+pd.options.mode.chained_assignment = None
+
+FILE_PATH = "loan.csv"
+REQUIRED_COLUMNS = ['Credit_History', 'LoanAmount', 'ApplicantIncome']
+TARGET_COLUMN = 'Loan_Status'
+
+
+def print_classifier_info(feature_importance):
+    feature_names = REQUIRED_COLUMNS
+    embarked_score = feature_importance[-3:].sum()
+    scores = np.append(feature_importance[:2], embarked_score)
+    scores = map(lambda score: round(score, 2), scores)
+    print(dict(zip(feature_names, scores)))
+
+
+if __name__ == '__main__':
+    data = pd.read_csv(FILE_PATH)
+
+    X = data[REQUIRED_COLUMNS]
+    y = data[TARGET_COLUMN]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    classifier_tree = DecisionTreeClassifier(random_state=42)
+    classifier_tree.fit(X_train, y_train)
+
+    print_classifier_info(classifier_tree.feature_importances_)
+    print("Оценка качества (задача классификации) - ", classifier_tree.score(X_test, y_test))
--- a/abanin_daniil_lab_3/loan.csv
+++ b/abanin_daniil_lab_3/loan.csv
@@ -0,0 +1,615 @@
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+LP001356,Male,Yes,0,1,No,4652,3583.0,0.0,360.0,1,Semiurban,1.0
+LP001357,Male,,,1,No,3816,754.0,160.0,360.0,1,Urban,1.0
+LP001367,Male,Yes,1,1,No,3052,1030.0,100.0,360.0,1,Urban,1.0
+LP001369,Male,Yes,2,1,No,11417,1126.0,225.0,360.0,1,Urban,1.0
+LP001370,Male,No,0,0,,7333,0.0,120.0,360.0,1,Rural,0.0
+LP001379,Male,Yes,2,1,No,3800,3600.0,216.0,360.0,0,Urban,0.0
+LP001384,Male,Yes,3+,0,No,2071,754.0,94.0,480.0,1,Semiurban,1.0
+LP001385,Male,No,0,1,No,5316,0.0,136.0,360.0,1,Urban,1.0
+LP001387,Female,Yes,0,1,,2929,2333.0,139.0,360.0,1,Semiurban,1.0
+LP001391,Male,Yes,0,0,No,3572,4114.0,152.0,,0,Rural,0.0
+LP001392,Female,No,1,1,Yes,7451,0.0,0.0,360.0,1,Semiurban,1.0
+LP001398,Male,No,0,1,,5050,0.0,118.0,360.0,1,Semiurban,1.0
+LP001401,Male,Yes,1,1,No,14583,0.0,185.0,180.0,1,Rural,1.0
+LP001404,Female,Yes,0,1,No,3167,2283.0,154.0,360.0,1,Semiurban,1.0
+LP001405,Male,Yes,1,1,No,2214,1398.0,85.0,360.0,0,Urban,1.0
+LP001421,Male,Yes,0,1,No,5568,2142.0,175.0,360.0,1,Rural,0.0
+LP001422,Female,No,0,1,No,10408,0.0,259.0,360.0,1,Urban,1.0
+LP001426,Male,Yes,,1,No,5667,2667.0,180.0,360.0,1,Rural,1.0
+LP001430,Female,No,0,1,No,4166,0.0,44.0,360.0,1,Semiurban,1.0
+LP001431,Female,No,0,1,No,2137,8980.0,137.0,360.0,0,Semiurban,1.0
+LP001432,Male,Yes,2,1,No,2957,0.0,81.0,360.0,1,Semiurban,1.0
+LP001439,Male,Yes,0,0,No,4300,2014.0,194.0,360.0,1,Rural,1.0
+LP001443,Female,No,0,1,No,3692,0.0,93.0,360.0,0,Rural,1.0
+LP001448,,Yes,3+,1,No,23803,0.0,370.0,360.0,1,Rural,1.0
+LP001449,Male,No,0,1,No,3865,1640.0,0.0,360.0,1,Rural,1.0
+LP001451,Male,Yes,1,1,Yes,10513,3850.0,160.0,180.0,0,Urban,0.0
+LP001465,Male,Yes,0,1,No,6080,2569.0,182.0,360.0,0,Rural,0.0
+LP001469,Male,No,0,1,Yes,20166,0.0,650.0,480.0,0,Urban,1.0
+LP001473,Male,No,0,1,No,2014,1929.0,74.0,360.0,1,Urban,1.0
+LP001478,Male,No,0,1,No,2718,0.0,70.0,360.0,1,Semiurban,1.0
+LP001482,Male,Yes,0,1,Yes,3459,0.0,25.0,120.0,1,Semiurban,1.0
+LP001487,Male,No,0,1,No,4895,0.0,102.0,360.0,1,Semiurban,1.0
+LP001488,Male,Yes,3+,1,No,4000,7750.0,290.0,360.0,1,Semiurban,0.0
+LP001489,Female,Yes,0,1,No,4583,0.0,84.0,360.0,1,Rural,0.0
+LP001491,Male,Yes,2,1,Yes,3316,3500.0,88.0,360.0,1,Urban,1.0
+LP001492,Male,No,0,1,No,14999,0.0,242.0,360.0,0,Semiurban,0.0
+LP001493,Male,Yes,2,0,No,4200,1430.0,129.0,360.0,1,Rural,0.0
+LP001497,Male,Yes,2,1,No,5042,2083.0,185.0,360.0,1,Rural,0.0
+LP001498,Male,No,0,1,No,5417,0.0,168.0,360.0,1,Urban,1.0
+LP001504,Male,No,0,1,Yes,6950,0.0,175.0,180.0,1,Semiurban,1.0
+LP001507,Male,Yes,0,1,No,2698,2034.0,122.0,360.0,1,Semiurban,1.0
+LP001508,Male,Yes,2,1,No,11757,0.0,187.0,180.0,1,Urban,1.0
+LP001514,Female,Yes,0,1,No,2330,4486.0,100.0,360.0,1,Semiurban,1.0
+LP001516,Female,Yes,2,1,No,14866,0.0,70.0,360.0,1,Urban,1.0
+LP001518,Male,Yes,1,1,No,1538,1425.0,30.0,360.0,1,Urban,1.0
+LP001519,Female,No,0,1,No,10000,1666.0,225.0,360.0,1,Rural,0.0
+LP001520,Male,Yes,0,1,No,4860,830.0,125.0,360.0,1,Semiurban,1.0
+LP001528,Male,No,0,1,No,6277,0.0,118.0,360.0,0,Rural,0.0
+LP001529,Male,Yes,0,1,Yes,2577,3750.0,152.0,360.0,1,Rural,1.0
+LP001531,Male,No,0,1,No,9166,0.0,244.0,360.0,1,Urban,0.0
+LP001532,Male,Yes,2,0,No,2281,0.0,113.0,360.0,1,Rural,0.0
+LP001535,Male,No,0,1,No,3254,0.0,50.0,360.0,1,Urban,1.0
+LP001536,Male,Yes,3+,1,No,39999,0.0,600.0,180.0,0,Semiurban,1.0
+LP001541,Male,Yes,1,1,No,6000,0.0,160.0,360.0,0,Rural,1.0
+LP001543,Male,Yes,1,1,No,9538,0.0,187.0,360.0,1,Urban,1.0
+LP001546,Male,No,0,1,,2980,2083.0,120.0,360.0,1,Rural,1.0
+LP001552,Male,Yes,0,1,No,4583,5625.0,255.0,360.0,1,Semiurban,1.0
+LP001560,Male,Yes,0,0,No,1863,1041.0,98.0,360.0,1,Semiurban,1.0
+LP001562,Male,Yes,0,1,No,7933,0.0,275.0,360.0,1,Urban,0.0
+LP001565,Male,Yes,1,1,No,3089,1280.0,121.0,360.0,0,Semiurban,0.0
+LP001570,Male,Yes,2,1,No,4167,1447.0,158.0,360.0,1,Rural,1.0
+LP001572,Male,Yes,0,1,No,9323,0.0,75.0,180.0,1,Urban,1.0
+LP001574,Male,Yes,0,1,No,3707,3166.0,182.0,,1,Rural,1.0
+LP001577,Female,Yes,0,1,No,4583,0.0,112.0,360.0,1,Rural,0.0
+LP001578,Male,Yes,0,1,No,2439,3333.0,129.0,360.0,1,Rural,1.0
+LP001579,Male,No,0,1,No,2237,0.0,63.0,480.0,0,Semiurban,0.0
+LP001580,Male,Yes,2,1,No,8000,0.0,200.0,360.0,1,Semiurban,1.0
+LP001581,Male,Yes,0,0,,1820,1769.0,95.0,360.0,1,Rural,1.0
+LP001585,,Yes,3+,1,No,51763,0.0,700.0,300.0,1,Urban,1.0
+LP001586,Male,Yes,3+,0,No,3522,0.0,81.0,180.0,1,Rural,0.0
+LP001594,Male,Yes,0,1,No,5708,5625.0,187.0,360.0,1,Semiurban,1.0
+LP001603,Male,Yes,0,0,Yes,4344,736.0,87.0,360.0,1,Semiurban,0.0
+LP001606,Male,Yes,0,1,No,3497,1964.0,116.0,360.0,1,Rural,1.0
+LP001608,Male,Yes,2,1,No,2045,1619.0,101.0,360.0,1,Rural,1.0
+LP001610,Male,Yes,3+,1,No,5516,11300.0,495.0,360.0,0,Semiurban,0.0
+LP001616,Male,Yes,1,1,No,3750,0.0,116.0,360.0,1,Semiurban,1.0
+LP001630,Male,No,0,0,No,2333,1451.0,102.0,480.0,0,Urban,0.0
+LP001633,Male,Yes,1,1,No,6400,7250.0,180.0,360.0,0,Urban,0.0
+LP001634,Male,No,0,1,No,1916,5063.0,67.0,360.0,0,Rural,0.0
+LP001636,Male,Yes,0,1,No,4600,0.0,73.0,180.0,1,Semiurban,1.0
+LP001637,Male,Yes,1,1,No,33846,0.0,260.0,360.0,1,Semiurban,0.0
+LP001639,Female,Yes,0,1,No,3625,0.0,108.0,360.0,1,Semiurban,1.0
+LP001640,Male,Yes,0,1,Yes,39147,4750.0,120.0,360.0,1,Semiurban,1.0
+LP001641,Male,Yes,1,1,Yes,2178,0.0,66.0,300.0,0,Rural,0.0
+LP001643,Male,Yes,0,1,No,2383,2138.0,58.0,360.0,0,Rural,1.0
+LP001644,,Yes,0,1,Yes,674,5296.0,168.0,360.0,1,Rural,1.0
+LP001647,Male,Yes,0,1,No,9328,0.0,188.0,180.0,1,Rural,1.0
+LP001653,Male,No,0,0,No,4885,0.0,48.0,360.0,1,Rural,1.0
+LP001656,Male,No,0,1,No,12000,0.0,164.0,360.0,1,Semiurban,0.0
+LP001657,Male,Yes,0,0,No,6033,0.0,160.0,360.0,1,Urban,0.0
+LP001658,Male,No,0,1,No,3858,0.0,76.0,360.0,1,Semiurban,1.0
+LP001664,Male,No,0,1,No,4191,0.0,120.0,360.0,1,Rural,1.0
+LP001665,Male,Yes,1,1,No,3125,2583.0,170.0,360.0,1,Semiurban,0.0
+LP001666,Male,No,0,1,No,8333,3750.0,187.0,360.0,1,Rural,1.0
+LP001669,Female,No,0,0,No,1907,2365.0,120.0,,1,Urban,1.0
+LP001671,Female,Yes,0,1,No,3416,2816.0,113.0,360.0,0,Semiurban,1.0
+LP001673,Male,No,0,1,Yes,11000,0.0,83.0,360.0,1,Urban,0.0
+LP001674,Male,Yes,1,0,No,2600,2500.0,90.0,360.0,1,Semiurban,1.0
+LP001677,Male,No,2,1,No,4923,0.0,166.0,360.0,0,Semiurban,1.0
+LP001682,Male,Yes,3+,0,No,3992,0.0,0.0,180.0,1,Urban,0.0
+LP001688,Male,Yes,1,0,No,3500,1083.0,135.0,360.0,1,Urban,1.0
+LP001691,Male,Yes,2,0,No,3917,0.0,124.0,360.0,1,Semiurban,1.0
+LP001692,Female,No,0,0,No,4408,0.0,120.0,360.0,1,Semiurban,1.0
+LP001693,Female,No,0,1,No,3244,0.0,80.0,360.0,1,Urban,1.0
+LP001698,Male,No,0,0,No,3975,2531.0,55.0,360.0,1,Rural,1.0
+LP001699,Male,No,0,1,No,2479,0.0,59.0,360.0,1,Urban,1.0
+LP001702,Male,No,0,1,No,3418,0.0,127.0,360.0,1,Semiurban,0.0
+LP001708,Female,No,0,1,No,10000,0.0,214.0,360.0,1,Semiurban,0.0
+LP001711,Male,Yes,3+,1,No,3430,1250.0,128.0,360.0,0,Semiurban,0.0
+LP001713,Male,Yes,1,1,Yes,7787,0.0,240.0,360.0,1,Urban,1.0
+LP001715,Male,Yes,3+,0,Yes,5703,0.0,130.0,360.0,1,Rural,1.0
+LP001716,Male,Yes,0,1,No,3173,3021.0,137.0,360.0,1,Urban,1.0
+LP001720,Male,Yes,3+,0,No,3850,983.0,100.0,360.0,1,Semiurban,1.0
+LP001722,Male,Yes,0,1,No,150,1800.0,135.0,360.0,1,Rural,0.0
+LP001726,Male,Yes,0,1,No,3727,1775.0,131.0,360.0,1,Semiurban,1.0
+LP001732,Male,Yes,2,1,,5000,0.0,72.0,360.0,0,Semiurban,0.0
+LP001734,Female,Yes,2,1,No,4283,2383.0,127.0,360.0,0,Semiurban,1.0
+LP001736,Male,Yes,0,1,No,2221,0.0,60.0,360.0,0,Urban,0.0
+LP001743,Male,Yes,2,1,No,4009,1717.0,116.0,360.0,1,Semiurban,1.0
+LP001744,Male,No,0,1,No,2971,2791.0,144.0,360.0,1,Semiurban,1.0
+LP001749,Male,Yes,0,1,No,7578,1010.0,175.0,,1,Semiurban,1.0
+LP001750,Male,Yes,0,1,No,6250,0.0,128.0,360.0,1,Semiurban,1.0
+LP001751,Male,Yes,0,1,No,3250,0.0,170.0,360.0,1,Rural,0.0
+LP001754,Male,Yes,,0,Yes,4735,0.0,138.0,360.0,1,Urban,0.0
+LP001758,Male,Yes,2,1,No,6250,1695.0,210.0,360.0,1,Semiurban,1.0
+LP001760,Male,,,1,No,4758,0.0,158.0,480.0,1,Semiurban,1.0
+LP001761,Male,No,0,1,Yes,6400,0.0,200.0,360.0,1,Rural,1.0
+LP001765,Male,Yes,1,1,No,2491,2054.0,104.0,360.0,1,Semiurban,1.0
+LP001768,Male,Yes,0,1,,3716,0.0,42.0,180.0,1,Rural,1.0
+LP001770,Male,No,0,0,No,3189,2598.0,120.0,,1,Rural,1.0
+LP001776,Female,No,0,1,No,8333,0.0,280.0,360.0,1,Semiurban,1.0
+LP001778,Male,Yes,1,1,No,3155,1779.0,140.0,360.0,1,Semiurban,1.0
+LP001784,Male,Yes,1,1,No,5500,1260.0,170.0,360.0,1,Rural,1.0
+LP001786,Male,Yes,0,1,,5746,0.0,255.0,360.0,0,Urban,0.0
+LP001788,Female,No,0,1,Yes,3463,0.0,122.0,360.0,0,Urban,1.0
+LP001790,Female,No,1,1,No,3812,0.0,112.0,360.0,1,Rural,1.0
+LP001792,Male,Yes,1,1,No,3315,0.0,96.0,360.0,1,Semiurban,1.0
+LP001798,Male,Yes,2,1,No,5819,5000.0,120.0,360.0,1,Rural,1.0
+LP001800,Male,Yes,1,0,No,2510,1983.0,140.0,180.0,1,Urban,0.0
+LP001806,Male,No,0,1,No,2965,5701.0,155.0,60.0,1,Urban,1.0
+LP001807,Male,Yes,2,1,Yes,6250,1300.0,108.0,360.0,1,Rural,1.0
+LP001811,Male,Yes,0,0,No,3406,4417.0,123.0,360.0,1,Semiurban,1.0
+LP001813,Male,No,0,1,Yes,6050,4333.0,120.0,180.0,1,Urban,0.0
+LP001814,Male,Yes,2,1,No,9703,0.0,112.0,360.0,1,Urban,1.0
+LP001819,Male,Yes,1,0,No,6608,0.0,137.0,180.0,1,Urban,1.0
+LP001824,Male,Yes,1,1,No,2882,1843.0,123.0,480.0,1,Semiurban,1.0
+LP001825,Male,Yes,0,1,No,1809,1868.0,90.0,360.0,1,Urban,1.0
+LP001835,Male,Yes,0,0,No,1668,3890.0,201.0,360.0,0,Semiurban,0.0
+LP001836,Female,No,2,1,No,3427,0.0,138.0,360.0,1,Urban,0.0
+LP001841,Male,No,0,0,Yes,2583,2167.0,104.0,360.0,1,Rural,1.0
+LP001843,Male,Yes,1,0,No,2661,7101.0,279.0,180.0,1,Semiurban,1.0
+LP001844,Male,No,0,1,Yes,16250,0.0,192.0,360.0,0,Urban,0.0
+LP001846,Female,No,3+,1,No,3083,0.0,255.0,360.0,1,Rural,1.0
+LP001849,Male,No,0,0,No,6045,0.0,115.0,360.0,0,Rural,0.0
+LP001854,Male,Yes,3+,1,No,5250,0.0,94.0,360.0,1,Urban,0.0
+LP001859,Male,Yes,0,1,No,14683,2100.0,304.0,360.0,1,Rural,0.0
+LP001864,Male,Yes,3+,0,No,4931,0.0,128.0,360.0,0,Semiurban,0.0
+LP001865,Male,Yes,1,1,No,6083,4250.0,330.0,360.0,0,Urban,1.0
+LP001868,Male,No,0,1,No,2060,2209.0,134.0,360.0,1,Semiurban,1.0
+LP001870,Female,No,1,1,No,3481,0.0,155.0,36.0,1,Semiurban,0.0
+LP001871,Female,No,0,1,No,7200,0.0,120.0,360.0,1,Rural,1.0
+LP001872,Male,No,0,1,Yes,5166,0.0,128.0,360.0,1,Semiurban,1.0
+LP001875,Male,No,0,1,No,4095,3447.0,151.0,360.0,1,Rural,1.0
+LP001877,Male,Yes,2,1,No,4708,1387.0,150.0,360.0,1,Semiurban,1.0
+LP001882,Male,Yes,3+,1,No,4333,1811.0,160.0,360.0,0,Urban,1.0
+LP001883,Female,No,0,1,,3418,0.0,135.0,360.0,1,Rural,0.0
+LP001884,Female,No,1,1,No,2876,1560.0,90.0,360.0,1,Urban,1.0
+LP001888,Female,No,0,1,No,3237,0.0,30.0,360.0,1,Urban,1.0
+LP001891,Male,Yes,0,1,No,11146,0.0,136.0,360.0,1,Urban,1.0
+LP001892,Male,No,0,1,No,2833,1857.0,126.0,360.0,1,Rural,1.0
+LP001894,Male,Yes,0,1,No,2620,2223.0,150.0,360.0,1,Semiurban,1.0
+LP001896,Male,Yes,2,1,No,3900,0.0,90.0,360.0,1,Semiurban,1.0
+LP001900,Male,Yes,1,1,No,2750,1842.0,115.0,360.0,1,Semiurban,1.0
+LP001903,Male,Yes,0,1,No,3993,3274.0,207.0,360.0,1,Semiurban,1.0
+LP001904,Male,Yes,0,1,No,3103,1300.0,80.0,360.0,1,Urban,1.0
+LP001907,Male,Yes,0,1,No,14583,0.0,436.0,360.0,1,Semiurban,1.0
+LP001908,Female,Yes,0,0,No,4100,0.0,124.0,360.0,0,Rural,1.0
+LP001910,Male,No,1,0,Yes,4053,2426.0,158.0,360.0,0,Urban,0.0
+LP001914,Male,Yes,0,1,No,3927,800.0,112.0,360.0,1,Semiurban,1.0
+LP001915,Male,Yes,2,1,No,2301,985.7999878,78.0,180.0,1,Urban,1.0
+LP001917,Female,No,0,1,No,1811,1666.0,54.0,360.0,1,Urban,1.0
+LP001922,Male,Yes,0,1,No,20667,0.0,0.0,360.0,1,Rural,0.0
+LP001924,Male,No,0,1,No,3158,3053.0,89.0,360.0,1,Rural,1.0
+LP001925,Female,No,0,1,Yes,2600,1717.0,99.0,300.0,1,Semiurban,0.0
+LP001926,Male,Yes,0,1,No,3704,2000.0,120.0,360.0,1,Rural,1.0
+LP001931,Female,No,0,1,No,4124,0.0,115.0,360.0,1,Semiurban,1.0
+LP001935,Male,No,0,1,No,9508,0.0,187.0,360.0,1,Rural,1.0
+LP001936,Male,Yes,0,1,No,3075,2416.0,139.0,360.0,1,Rural,1.0
+LP001938,Male,Yes,2,1,No,4400,0.0,127.0,360.0,0,Semiurban,0.0
+LP001940,Male,Yes,2,1,No,3153,1560.0,134.0,360.0,1,Urban,1.0
+LP001945,Female,No,,1,No,5417,0.0,143.0,480.0,0,Urban,0.0
+LP001947,Male,Yes,0,1,No,2383,3334.0,172.0,360.0,1,Semiurban,1.0
+LP001949,Male,Yes,3+,1,,4416,1250.0,110.0,360.0,1,Urban,1.0
+LP001953,Male,Yes,1,1,No,6875,0.0,200.0,360.0,1,Semiurban,1.0
+LP001954,Female,Yes,1,1,No,4666,0.0,135.0,360.0,1,Urban,1.0
+LP001955,Female,No,0,1,No,5000,2541.0,151.0,480.0,1,Rural,0.0
+LP001963,Male,Yes,1,1,No,2014,2925.0,113.0,360.0,1,Urban,0.0
+LP001964,Male,Yes,0,0,No,1800,2934.0,93.0,360.0,0,Urban,0.0
+LP001972,Male,Yes,,0,No,2875,1750.0,105.0,360.0,1,Semiurban,1.0
+LP001974,Female,No,0,1,No,5000,0.0,132.0,360.0,1,Rural,1.0
+LP001977,Male,Yes,1,1,No,1625,1803.0,96.0,360.0,1,Urban,1.0
+LP001978,Male,No,0,1,No,4000,2500.0,140.0,360.0,1,Rural,1.0
+LP001990,Male,No,0,0,No,2000,0.0,0.0,360.0,1,Urban,0.0
+LP001993,Female,No,0,1,No,3762,1666.0,135.0,360.0,1,Rural,1.0
+LP001994,Female,No,0,1,No,2400,1863.0,104.0,360.0,0,Urban,0.0
+LP001996,Male,No,0,1,No,20233,0.0,480.0,360.0,1,Rural,0.0
+LP001998,Male,Yes,2,0,No,7667,0.0,185.0,360.0,0,Rural,1.0
+LP002002,Female,No,0,1,No,2917,0.0,84.0,360.0,1,Semiurban,1.0
+LP002004,Male,No,0,0,No,2927,2405.0,111.0,360.0,1,Semiurban,1.0
+LP002006,Female,No,0,1,No,2507,0.0,56.0,360.0,1,Rural,1.0
+LP002008,Male,Yes,2,1,Yes,5746,0.0,144.0,84.0,0,Rural,1.0
+LP002024,,Yes,0,1,No,2473,1843.0,159.0,360.0,1,Rural,0.0
+LP002031,Male,Yes,1,0,No,3399,1640.0,111.0,180.0,1,Urban,1.0
+LP002035,Male,Yes,2,1,No,3717,0.0,120.0,360.0,1,Semiurban,1.0
+LP002036,Male,Yes,0,1,No,2058,2134.0,88.0,360.0,0,Urban,1.0
+LP002043,Female,No,1,1,No,3541,0.0,112.0,360.0,0,Semiurban,1.0
+LP002050,Male,Yes,1,1,Yes,10000,0.0,155.0,360.0,1,Rural,0.0
+LP002051,Male,Yes,0,1,No,2400,2167.0,115.0,360.0,1,Semiurban,1.0
+LP002053,Male,Yes,3+,1,No,4342,189.0,124.0,360.0,1,Semiurban,1.0
+LP002054,Male,Yes,2,0,No,3601,1590.0,0.0,360.0,1,Rural,1.0
+LP002055,Female,No,0,1,No,3166,2985.0,132.0,360.0,0,Rural,1.0
+LP002065,Male,Yes,3+,1,No,15000,0.0,300.0,360.0,1,Rural,1.0
+LP002067,Male,Yes,1,1,Yes,8666,4983.0,376.0,360.0,0,Rural,0.0
+LP002068,Male,No,0,1,No,4917,0.0,130.0,360.0,0,Rural,1.0
+LP002082,Male,Yes,0,1,Yes,5818,2160.0,184.0,360.0,1,Semiurban,1.0
+LP002086,Female,Yes,0,1,No,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002087,Female,No,0,1,No,2500,0.0,67.0,360.0,1,Urban,1.0
+LP002097,Male,No,1,1,No,4384,1793.0,117.0,360.0,1,Urban,1.0
+LP002098,Male,No,0,1,No,2935,0.0,98.0,360.0,1,Semiurban,1.0
+LP002100,Male,No,,1,No,2833,0.0,71.0,360.0,1,Urban,1.0
+LP002101,Male,Yes,0,1,,63337,0.0,490.0,180.0,1,Urban,1.0
+LP002103,,Yes,1,1,Yes,9833,1833.0,182.0,180.0,1,Urban,1.0
+LP002106,Male,Yes,,1,Yes,5503,4490.0,70.0,,1,Semiurban,1.0
+LP002110,Male,Yes,1,1,,5250,688.0,160.0,360.0,1,Rural,1.0
+LP002112,Male,Yes,2,1,Yes,2500,4600.0,176.0,360.0,1,Rural,1.0
+LP002113,Female,No,3+,0,No,1830,0.0,0.0,360.0,0,Urban,0.0
+LP002114,Female,No,0,1,No,4160,0.0,71.0,360.0,1,Semiurban,1.0
+LP002115,Male,Yes,3+,0,No,2647,1587.0,173.0,360.0,1,Rural,0.0
+LP002116,Female,No,0,1,No,2378,0.0,46.0,360.0,1,Rural,0.0
+LP002119,Male,Yes,1,0,No,4554,1229.0,158.0,360.0,1,Urban,1.0
+LP002126,Male,Yes,3+,0,No,3173,0.0,74.0,360.0,1,Semiurban,1.0
+LP002128,Male,Yes,2,1,,2583,2330.0,125.0,360.0,1,Rural,1.0
+LP002129,Male,Yes,0,1,No,2499,2458.0,160.0,360.0,1,Semiurban,1.0
+LP002130,Male,Yes,,0,No,3523,3230.0,152.0,360.0,0,Rural,0.0
+LP002131,Male,Yes,2,0,No,3083,2168.0,126.0,360.0,1,Urban,1.0
+LP002137,Male,Yes,0,1,No,6333,4583.0,259.0,360.0,0,Semiurban,1.0
+LP002138,Male,Yes,0,1,No,2625,6250.0,187.0,360.0,1,Rural,1.0
+LP002139,Male,Yes,0,1,No,9083,0.0,228.0,360.0,1,Semiurban,1.0
+LP002140,Male,No,0,1,No,8750,4167.0,308.0,360.0,1,Rural,0.0
+LP002141,Male,Yes,3+,1,No,2666,2083.0,95.0,360.0,1,Rural,1.0
+LP002142,Female,Yes,0,1,Yes,5500,0.0,105.0,360.0,0,Rural,0.0
+LP002143,Female,Yes,0,1,No,2423,505.0,130.0,360.0,1,Semiurban,1.0
+LP002144,Female,No,,1,No,3813,0.0,116.0,180.0,1,Urban,1.0
+LP002149,Male,Yes,2,1,No,8333,3167.0,165.0,360.0,1,Rural,1.0
+LP002151,Male,Yes,1,1,No,3875,0.0,67.0,360.0,1,Urban,0.0
+LP002158,Male,Yes,0,0,No,3000,1666.0,100.0,480.0,0,Urban,0.0
+LP002160,Male,Yes,3+,1,No,5167,3167.0,200.0,360.0,1,Semiurban,1.0
+LP002161,Female,No,1,1,No,4723,0.0,81.0,360.0,1,Semiurban,0.0
+LP002170,Male,Yes,2,1,No,5000,3667.0,236.0,360.0,1,Semiurban,1.0
+LP002175,Male,Yes,0,1,No,4750,2333.0,130.0,360.0,1,Urban,1.0
+LP002178,Male,Yes,0,1,No,3013,3033.0,95.0,300.0,0,Urban,1.0
+LP002180,Male,No,0,1,Yes,6822,0.0,141.0,360.0,1,Rural,1.0
+LP002181,Male,No,0,0,No,6216,0.0,133.0,360.0,1,Rural,0.0
+LP002187,Male,No,0,1,No,2500,0.0,96.0,480.0,1,Semiurban,0.0
+LP002188,Male,No,0,1,No,5124,0.0,124.0,,0,Rural,0.0
+LP002190,Male,Yes,1,1,No,6325,0.0,175.0,360.0,1,Semiurban,1.0
+LP002191,Male,Yes,0,1,No,19730,5266.0,570.0,360.0,1,Rural,0.0
+LP002194,Female,No,0,1,Yes,15759,0.0,55.0,360.0,1,Semiurban,1.0
+LP002197,Male,Yes,2,1,No,5185,0.0,155.0,360.0,1,Semiurban,1.0
+LP002201,Male,Yes,2,1,Yes,9323,7873.0,380.0,300.0,1,Rural,1.0
+LP002205,Male,No,1,1,No,3062,1987.0,111.0,180.0,0,Urban,0.0
+LP002209,Female,No,0,1,,2764,1459.0,110.0,360.0,1,Urban,1.0
+LP002211,Male,Yes,0,1,No,4817,923.0,120.0,180.0,1,Urban,1.0
+LP002219,Male,Yes,3+,1,No,8750,4996.0,130.0,360.0,1,Rural,1.0
+LP002223,Male,Yes,0,1,No,4310,0.0,130.0,360.0,0,Semiurban,1.0
+LP002224,Male,No,0,1,No,3069,0.0,71.0,480.0,1,Urban,0.0
+LP002225,Male,Yes,2,1,No,5391,0.0,130.0,360.0,1,Urban,1.0
+LP002226,Male,Yes,0,1,,3333,2500.0,128.0,360.0,1,Semiurban,1.0
+LP002229,Male,No,0,1,No,5941,4232.0,296.0,360.0,1,Semiurban,1.0
+LP002231,Female,No,0,1,No,6000,0.0,156.0,360.0,1,Urban,1.0
+LP002234,Male,No,0,1,Yes,7167,0.0,128.0,360.0,1,Urban,1.0
+LP002236,Male,Yes,2,1,No,4566,0.0,100.0,360.0,1,Urban,0.0
+LP002237,Male,No,1,1,,3667,0.0,113.0,180.0,1,Urban,1.0
+LP002239,Male,No,0,0,No,2346,1600.0,132.0,360.0,1,Semiurban,1.0
+LP002243,Male,Yes,0,0,No,3010,3136.0,0.0,360.0,0,Urban,0.0
+LP002244,Male,Yes,0,1,No,2333,2417.0,136.0,360.0,1,Urban,1.0
+LP002250,Male,Yes,0,1,No,5488,0.0,125.0,360.0,1,Rural,1.0
+LP002255,Male,No,3+,1,No,9167,0.0,185.0,360.0,1,Rural,1.0
+LP002262,Male,Yes,3+,1,No,9504,0.0,275.0,360.0,1,Rural,1.0
+LP002263,Male,Yes,0,1,No,2583,2115.0,120.0,360.0,0,Urban,1.0
+LP002265,Male,Yes,2,0,No,1993,1625.0,113.0,180.0,1,Semiurban,1.0
+LP002266,Male,Yes,2,1,No,3100,1400.0,113.0,360.0,1,Urban,1.0
+LP002272,Male,Yes,2,1,No,3276,484.0,135.0,360.0,0,Semiurban,1.0
+LP002277,Female,No,0,1,No,3180,0.0,71.0,360.0,0,Urban,0.0
+LP002281,Male,Yes,0,1,No,3033,1459.0,95.0,360.0,1,Urban,1.0
+LP002284,Male,No,0,0,No,3902,1666.0,109.0,360.0,1,Rural,1.0
+LP002287,Female,No,0,1,No,1500,1800.0,103.0,360.0,0,Semiurban,0.0
+LP002288,Male,Yes,2,0,No,2889,0.0,45.0,180.0,0,Urban,0.0
+LP002296,Male,No,0,0,No,2755,0.0,65.0,300.0,1,Rural,0.0
+LP002297,Male,No,0,1,No,2500,20000.0,103.0,360.0,1,Semiurban,1.0
+LP002300,Female,No,0,0,No,1963,0.0,53.0,360.0,1,Semiurban,1.0
+LP002301,Female,No,0,1,Yes,7441,0.0,194.0,360.0,1,Rural,0.0
+LP002305,Female,No,0,1,No,4547,0.0,115.0,360.0,1,Semiurban,1.0
+LP002308,Male,Yes,0,0,No,2167,2400.0,115.0,360.0,1,Urban,1.0
+LP002314,Female,No,0,0,No,2213,0.0,66.0,360.0,1,Rural,1.0
+LP002315,Male,Yes,1,1,No,8300,0.0,152.0,300.0,0,Semiurban,0.0
+LP002317,Male,Yes,3+,1,No,81000,0.0,360.0,360.0,0,Rural,0.0
+LP002318,Female,No,1,0,Yes,3867,0.0,62.0,360.0,1,Semiurban,0.0
+LP002319,Male,Yes,0,1,,6256,0.0,160.0,360.0,0,Urban,1.0
+LP002328,Male,Yes,0,0,No,6096,0.0,218.0,360.0,0,Rural,0.0
+LP002332,Male,Yes,0,0,No,2253,2033.0,110.0,360.0,1,Rural,1.0
+LP002335,Female,Yes,0,0,No,2149,3237.0,178.0,360.0,0,Semiurban,0.0
+LP002337,Female,No,0,1,No,2995,0.0,60.0,360.0,1,Urban,1.0
+LP002341,Female,No,1,1,No,2600,0.0,160.0,360.0,1,Urban,0.0
+LP002342,Male,Yes,2,1,Yes,1600,20000.0,239.0,360.0,1,Urban,0.0
+LP002345,Male,Yes,0,1,No,1025,2773.0,112.0,360.0,1,Rural,1.0
+LP002347,Male,Yes,0,1,No,3246,1417.0,138.0,360.0,1,Semiurban,1.0
+LP002348,Male,Yes,0,1,No,5829,0.0,138.0,360.0,1,Rural,1.0
+LP002357,Female,No,0,0,No,2720,0.0,80.0,,0,Urban,0.0
+LP002361,Male,Yes,0,1,No,1820,1719.0,100.0,360.0,1,Urban,1.0
+LP002362,Male,Yes,1,1,No,7250,1667.0,110.0,,0,Urban,0.0
+LP002364,Male,Yes,0,1,No,14880,0.0,96.0,360.0,1,Semiurban,1.0
+LP002366,Male,Yes,0,1,No,2666,4300.0,121.0,360.0,1,Rural,1.0
+LP002367,Female,No,1,0,No,4606,0.0,81.0,360.0,1,Rural,0.0
+LP002368,Male,Yes,2,1,No,5935,0.0,133.0,360.0,1,Semiurban,1.0
+LP002369,Male,Yes,0,1,No,2920,16.12000084,87.0,360.0,1,Rural,1.0
+LP002370,Male,No,0,0,No,2717,0.0,60.0,180.0,1,Urban,1.0
+LP002377,Female,No,1,1,Yes,8624,0.0,150.0,360.0,1,Semiurban,1.0
+LP002379,Male,No,0,1,No,6500,0.0,105.0,360.0,0,Rural,0.0
+LP002386,Male,No,0,1,,12876,0.0,405.0,360.0,1,Semiurban,1.0
+LP002387,Male,Yes,0,1,No,2425,2340.0,143.0,360.0,1,Semiurban,1.0
+LP002390,Male,No,0,1,No,3750,0.0,100.0,360.0,1,Urban,1.0
+LP002393,Female,,,1,No,10047,0.0,0.0,240.0,1,Semiurban,1.0
+LP002398,Male,No,0,1,No,1926,1851.0,50.0,360.0,1,Semiurban,1.0
+LP002401,Male,Yes,0,1,No,2213,1125.0,0.0,360.0,1,Urban,1.0
+LP002403,Male,No,0,1,Yes,10416,0.0,187.0,360.0,0,Urban,0.0
+LP002407,Female,Yes,0,0,Yes,7142,0.0,138.0,360.0,1,Rural,1.0
+LP002408,Male,No,0,1,No,3660,5064.0,187.0,360.0,1,Semiurban,1.0
+LP002409,Male,Yes,0,1,No,7901,1833.0,180.0,360.0,1,Rural,1.0
+LP002418,Male,No,3+,0,No,4707,1993.0,148.0,360.0,1,Semiurban,1.0
+LP002422,Male,No,1,1,No,37719,0.0,152.0,360.0,1,Semiurban,1.0
+LP002424,Male,Yes,0,1,No,7333,8333.0,175.0,300.0,0,Rural,1.0
+LP002429,Male,Yes,1,1,Yes,3466,1210.0,130.0,360.0,1,Rural,1.0
+LP002434,Male,Yes,2,0,No,4652,0.0,110.0,360.0,1,Rural,1.0
+LP002435,Male,Yes,0,1,,3539,1376.0,55.0,360.0,1,Rural,0.0
+LP002443,Male,Yes,2,1,No,3340,1710.0,150.0,360.0,0,Rural,0.0
+LP002444,Male,No,1,0,Yes,2769,1542.0,190.0,360.0,0,Semiurban,0.0
+LP002446,Male,Yes,2,0,No,2309,1255.0,125.0,360.0,0,Rural,0.0
+LP002447,Male,Yes,2,0,No,1958,1456.0,60.0,300.0,0,Urban,1.0
+LP002448,Male,Yes,0,1,No,3948,1733.0,149.0,360.0,0,Rural,0.0
+LP002449,Male,Yes,0,1,No,2483,2466.0,90.0,180.0,0,Rural,1.0
+LP002453,Male,No,0,1,Yes,7085,0.0,84.0,360.0,1,Semiurban,1.0
+LP002455,Male,Yes,2,1,No,3859,0.0,96.0,360.0,1,Semiurban,1.0
+LP002459,Male,Yes,0,1,No,4301,0.0,118.0,360.0,1,Urban,1.0
+LP002467,Male,Yes,0,1,No,3708,2569.0,173.0,360.0,1,Urban,0.0
+LP002472,Male,No,2,1,No,4354,0.0,136.0,360.0,1,Rural,1.0
+LP002473,Male,Yes,0,1,No,8334,0.0,160.0,360.0,1,Semiurban,0.0
+LP002478,,Yes,0,1,Yes,2083,4083.0,160.0,360.0,0,Semiurban,1.0
+LP002484,Male,Yes,3+,1,No,7740,0.0,128.0,180.0,1,Urban,1.0
+LP002487,Male,Yes,0,1,No,3015,2188.0,153.0,360.0,1,Rural,1.0
+LP002489,Female,No,1,0,,5191,0.0,132.0,360.0,1,Semiurban,1.0
+LP002493,Male,No,0,1,No,4166,0.0,98.0,360.0,0,Semiurban,0.0
+LP002494,Male,No,0,1,No,6000,0.0,140.0,360.0,1,Rural,1.0
+LP002500,Male,Yes,3+,0,No,2947,1664.0,70.0,180.0,0,Urban,0.0
+LP002501,,Yes,0,1,No,16692,0.0,110.0,360.0,1,Semiurban,1.0
+LP002502,Female,Yes,2,0,,210,2917.0,98.0,360.0,1,Semiurban,1.0
+LP002505,Male,Yes,0,1,No,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002515,Male,Yes,1,1,Yes,3450,2079.0,162.0,360.0,1,Semiurban,1.0
+LP002517,Male,Yes,1,0,No,2653,1500.0,113.0,180.0,0,Rural,0.0
+LP002519,Male,Yes,3+,1,No,4691,0.0,100.0,360.0,1,Semiurban,1.0
+LP002522,Female,No,0,1,Yes,2500,0.0,93.0,360.0,0,Urban,1.0
+LP002524,Male,No,2,1,No,5532,4648.0,162.0,360.0,1,Rural,1.0
+LP002527,Male,Yes,2,1,Yes,16525,1014.0,150.0,360.0,1,Rural,1.0
+LP002529,Male,Yes,2,1,No,6700,1750.0,230.0,300.0,1,Semiurban,1.0
+LP002530,,Yes,2,1,No,2873,1872.0,132.0,360.0,0,Semiurban,0.0
+LP002531,Male,Yes,1,1,Yes,16667,2250.0,86.0,360.0,1,Semiurban,1.0
+LP002533,Male,Yes,2,1,No,2947,1603.0,0.0,360.0,1,Urban,0.0
+LP002534,Female,No,0,0,No,4350,0.0,154.0,360.0,1,Rural,1.0
+LP002536,Male,Yes,3+,0,No,3095,0.0,113.0,360.0,1,Rural,1.0
+LP002537,Male,Yes,0,1,No,2083,3150.0,128.0,360.0,1,Semiurban,1.0
+LP002541,Male,Yes,0,1,No,10833,0.0,234.0,360.0,1,Semiurban,1.0
+LP002543,Male,Yes,2,1,No,8333,0.0,246.0,360.0,1,Semiurban,1.0
+LP002544,Male,Yes,1,0,No,1958,2436.0,131.0,360.0,1,Rural,1.0
+LP002545,Male,No,2,1,No,3547,0.0,80.0,360.0,0,Rural,0.0
+LP002547,Male,Yes,1,1,No,18333,0.0,500.0,360.0,1,Urban,0.0
+LP002555,Male,Yes,2,1,Yes,4583,2083.0,160.0,360.0,1,Semiurban,1.0
+LP002556,Male,No,0,1,No,2435,0.0,75.0,360.0,1,Urban,0.0
+LP002560,Male,No,0,0,No,2699,2785.0,96.0,360.0,0,Semiurban,1.0
+LP002562,Male,Yes,1,0,No,5333,1131.0,186.0,360.0,0,Urban,1.0
+LP002571,Male,No,0,0,No,3691,0.0,110.0,360.0,1,Rural,1.0
+LP002582,Female,No,0,0,Yes,17263,0.0,225.0,360.0,1,Semiurban,1.0
+LP002585,Male,Yes,0,1,No,3597,2157.0,119.0,360.0,0,Rural,0.0
+LP002586,Female,Yes,1,1,No,3326,913.0,105.0,84.0,1,Semiurban,1.0
+LP002587,Male,Yes,0,0,No,2600,1700.0,107.0,360.0,1,Rural,1.0
+LP002588,Male,Yes,0,1,No,4625,2857.0,111.0,12.0,0,Urban,1.0
+LP002600,Male,Yes,1,1,Yes,2895,0.0,95.0,360.0,1,Semiurban,1.0
+LP002602,Male,No,0,1,No,6283,4416.0,209.0,360.0,0,Rural,0.0
+LP002603,Female,No,0,1,No,645,3683.0,113.0,480.0,1,Rural,1.0
+LP002606,Female,No,0,1,No,3159,0.0,100.0,360.0,1,Semiurban,1.0
+LP002615,Male,Yes,2,1,No,4865,5624.0,208.0,360.0,1,Semiurban,1.0
+LP002618,Male,Yes,1,0,No,4050,5302.0,138.0,360.0,0,Rural,0.0
+LP002619,Male,Yes,0,0,No,3814,1483.0,124.0,300.0,1,Semiurban,1.0
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--- a/abanin_daniil_lab_3/result.png
+++ b/abanin_daniil_lab_3/result.png
--- a/basharin_sevastyan_lab_1/README.md
+++ b/basharin_sevastyan_lab_1/README.md
@@ -0,0 +1,83 @@
+## Лабораторная работа 1. Вариант 4.
+### Задание 
+Построить графики, отобразить 
+качество моделей, объяснить полученные результаты.
+
+Данные: `make_circles (noise=0.2, factor=0.5, random_state=rs)`
+
+Модели:
+- Линейная регресся 
+- Полиномиальная регрессия (со степенью 4) 
+- Гребневая полиномиальная регресся (со степенью 4, alpha = 1.0)
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+После будет запущена программа и сгенерированы 3 графика.
+
+### Используемые технологии
+- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python.
+- `matplotlib.pyplot` (псевдоним `plt`): Matplotlib - это библиотека для создания статических, анимированных и интерактивных визуализаций в Python. `pyplot` - это модуль Matplotlib, который используется для создания графиков и диаграмм.
+- `matplotlib.colors.ListedColormap` - этот модуль Matplotlib используется для создания цветных схем цветовых карт, которые могут быть использованы для визуализации данных.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `model_selection` - Этот модуль scikit-learn предоставляет инструменты для разделения данных на обучающие и тестовые наборы.
+  - `linear_model` - содержит реализации линейных моделей, таких как линейная регрессия, логистическая регрессия и другие.
+  - `pipeline` - позволяет объединить несколько этапов обработки данных и построения моделей в одну конвейерную цепочку.
+  - `PolynomialFeatures` - Этот класс scikit-learn используется для генерации полиномиальных признаков, позволяя моделям учитывать нелинейные зависимости в данных.
+  - `make_circles` - Эта функция scikit-learn создает набор данных, представляющий собой два класса, расположенных в форме двух пересекающихся окружностей. Это удобно для демонстрации работы различных моделей классификации.
+  - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации.
+
+### Описание работы
+Программа генерирует данные, разделяет данные на тестовые и обучающие для моделей по заданию.
+```python
+rs = randrange(50)
+X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rs)
+```
+`X_train` и `y_train` используются для обучения, а на данных `X_test` и `y_test` - оценка их качества.
+
+Поскольку все модели в задании регрессионные, результаты работы будем оценивать через решение задачи предсказания.
+
+Для оценки будем использовать следующие критерии: среднеквадратическому отклонению и коэфициенту детерминации. Чем ошибка меньше и чем коэфициент детерминации больше, тем лучше.
+```python
+np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3)  #среднеквадратическое отклонение 
+np.round(metrics.r2_score(y_test, y_predict), 2)  #коэфициент детерминации
+```
+Оценочные параметры округлены с помощью функции `round` до 3х и 2х знаков после запятой.
+
+### Линейная регрессия
+Для создания модели линейной регрессии воспользуемся `LinearRegression`.
+```python
+linear_reg = LinearRegression()
+```
+Обучим её и предскажем с её помощью `y` на тестовой выборке `x_text`.
+```python
+model.fit(X_train, y_train)
+y_predict = model.predict(X_test)
+```
+График для оценки результатов:
+![](linear.png "")
+
+#### Полиномиальная регрессия
+Добавим 3 недостающих члена к линейной модели, возведённых в соответствующие степени 2, 3 и 4.
+```python
+poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(random_state=rs))
+```
+График для оценки результатов:
+![](poly.png "")
+
+#### Полиномиальная гребневая регрессия
+Линейная регрессия является разновидностью полиномиальной регрессии со степенью ведущего члена равной 1.
+```python
+ridge_poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(penalty='l2', C=1.0, random_state=rs))
+```
+График для оценки результатов:
+![](ridge.png "")
+
+Точность измерений:
+![](result.png "")
+
+### Вывод
+Наиболее низкое среднеквадратичное отклонение и наиболее высокий коэффициент детерминации показала модель полиномиальной и полиномиальной гребневой регрессии. Это означает, что они являются лучшими моделями для данного набора данных.
--- a/basharin_sevastyan_lab_1/linear.png
+++ b/basharin_sevastyan_lab_1/linear.png
--- a/basharin_sevastyan_lab_1/main.py
+++ b/basharin_sevastyan_lab_1/main.py
@@ -0,0 +1,60 @@
+from random import randrange
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+from sklearn.datasets import make_circles
+
+rs = randrange(50)
+X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)  # Сгенерируем данные
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
+                                                    random_state=rs)  # Разделим данные на обучающий и тестовый наборы
+
+# Линейная модель
+linear_reg = LinearRegression()
+# Полиномиальная регрессия (со степенью 4)
+poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(random_state=rs))
+# Гребневая полиномиальная регрессия (со степенью 4 и alpha=1.0)
+ridge_poly_reg = make_pipeline(PolynomialFeatures(degree=4), StandardScaler(), LogisticRegression(penalty='l2', C=1.0,
+                                                                                                  random_state=rs))
+
+
+# Обучение моделей
+def mid_sq_n_det(name, model):
+    model.fit(X_train, y_train)
+    y_predict = model.predict(X_test)
+    print(f'Рассчёт среднеквадратичной ошибки для {name}: '
+          f'{np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3)}')  # Рассчёт среднеквадратичной ошибки модели
+    print(f'Рассчёт коэфициента детерминации для {name}: {np.round(metrics.r2_score(y_test, y_predict), 2)}')  # Рассчёт коэфициента детерминации модели
+    return name, model
+
+
+# Графики
+models = [mid_sq_n_det("Линейная регрессия", linear_reg),
+          mid_sq_n_det("Полиномиальная регрессия (со степенью 4)", poly_reg),
+          mid_sq_n_det("Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0)", ridge_poly_reg)]
+
+cmap_background = ListedColormap(['#FFAAAA', '#AAAAFF'])
+cmap_points = ListedColormap(['#FF0000', '#0000FF'])
+
+plt.figure(figsize=(15, 4))
+for i, (name, model) in enumerate(models):
+    plt.subplot(1, 3, i + 1)
+    xx, yy = np.meshgrid(np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1, 100),
+                         np.linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, 100))
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contourf(xx, yy, Z, cmap=cmap_background, alpha=0.5)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_points, marker='o', label='Тестовые точки')
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap_points, marker='x', label='Обучающие точки')
+    plt.legend()
+    plt.title(name)
+    plt.text(0.5, -1.2, 'Красный класс', color='r', fontsize=12)
+    plt.text(0.5, -1.7, 'Синий класс', color='b', fontsize=12)
+
+plt.tight_layout()
+plt.show()
--- a/basharin_sevastyan_lab_1/poly.png
+++ b/basharin_sevastyan_lab_1/poly.png
--- a/basharin_sevastyan_lab_1/result.png
+++ b/basharin_sevastyan_lab_1/result.png
--- a/basharin_sevastyan_lab_1/ridge.png
+++ b/basharin_sevastyan_lab_1/ridge.png
--- a/belyaeva_ekaterina_lab_2/README.md
+++ b/belyaeva_ekaterina_lab_2/README.md
@@ -0,0 +1,55 @@
+## Задание 
+
+Используя код из пункта «Решение задачи ранжирования признаков», выполните ранжирование признаков с помощью указанных по варианту моделей. Отобразите получившиеся оценки каждого признака каждой моделью и среднюю оценку. Проведите анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? (Названия\индексы признаков и будут ответом на задание).
+
+Вариант 6:
+
+- Гребневая регрессия (Ridge) 
+- Сокращение признаков Случайными деревьями (Random Forest Regressor) 
+- Линейная корреляция (f_regression)
+
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки numpy, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код выполняет оценку важности признаков в задаче регрессии. 
+
+Сначала генерируются исходные данные с использованием 14 признаков (X) и функции-выхода (Y), которая представляет собой регрессионную проблему Фридмана. Затем используются две модели - гребневая регрессия (Ridge) и случайный лес (Random Forest) - для обучения на данных и оценки важности признаков.
+
+Затем вычисляются коэффициенты корреляции между признаками и целевой переменной, и результаты сохраняются в словаре ranks с ключом "Correlation".
+
+Далее в цикле вычисляются средние значения оценок важности признаков для каждого признака. Результаты сохраняются в словаре mean.
+
+Как результат, программа выводит оценки важности для каждой модели и средние значения важности для каждого признака
+## Результат
+
+В результате получаем следующее:
+
+Ridge  
+[('x4', 1.0), ('x14', 0.92), ('x1', 0.76), ('x2', 0.75), ('x12', 0.67), ('x5', 0.61), ('x11', 0.59), ('x6', 0.08), ('x8', 0.08), ('x3', 0.06), ('x7', 0.03), ('x10', 0.01), ('x9', 0.0), ('x13', 0.0)]  
+Random Forest  
+[('x14', 1.0), ('x2', 0.76), ('x1', 0.66), ('x4', 0.55), ('x11', 0.29), ('x12', 0.28), ('x5', 0.23), ('x3', 0.1), ('x13', 0.09), ('x7', 0.01), ('x6', 0.0), ('x8', 0.0), ('x9', 0.0), ('x10', 0.0)]  
+Correlation  
+[('x4', 1.0), ('x14', 0.98), ('x2', 0.45), ('x12', 0.44), ('x1', 0.3), ('x11', 0.29), ('x5', 0.04), ('x8', 0.02), ('x7', 0.01), ('x9', 0.01), ('x3', 0.0), ('x6', 0.0), ('x10', 0.0), ('x13', 0.0)]  
+Mean Importance:  
+x14 : 0.97  
+x4 : 0.85  
+x2 : 0.65  
+x1 : 0.57  
+x12 : 0.46  
+x11 : 0.39  
+x5 : 0.29  
+x3 : 0.05  
+x6 : 0.03  
+x8 : 0.03  
+x13 : 0.03  
+x7 : 0.02  
+x9 : 0.0  
+x10 : 0.0  
+
+Вывод: Самым важным признаком в среднем оказался х14, потом х4 и далее по убывающей - х2, х1, х12, х11, х5. Остальные признаки показали минимальную значимость или не имеют ее совсем.
+
+Но стоит отметить, что несмотря на среднюю оценку признаков, разные модели выявили их значимость по-разному, что можно увидеть в тексте выше.
+Корреляция и гребневая регрессия показали чуть более схожий результат, нежели сокращение признаков случайными деревьями, хотя стоит заметить, что результаты всех моделей все равно отличаются.
--- a/belyaeva_ekaterina_lab_2/main.py
+++ b/belyaeva_ekaterina_lab_2/main.py
@@ -0,0 +1,74 @@
+from sklearn.linear_model import Ridge
+from sklearn.feature_selection import f_regression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+
+# генерируем исходные данные: 750 строк-наблюдений и 14 столбцов-признаков
+np.random.seed(0)
+size = 750
+X = np.random.uniform(0, 1, (size, 14))
+# Задаем функцию-выход: регрессионную проблему Фридмана
+Y = (10 * np.sin(np.pi*X[:, 0]*X[:, 1]) + 20*(X[:, 2] - .5)**2 +
+     10*X[:, 3] + 5*X[:, 4]**5 + np.random.normal(0, 1))
+# Добавляем зависимость признаков
+X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
+
+# Гребневая регрессия
+ridge = Ridge(alpha=7)
+ridge.fit(X, Y)
+# Случайные деревья
+rf = RandomForestRegressor(n_estimators=100, random_state=0)
+rf.fit(X, Y)
+
+ranks = {}
+
+names = ["x%s" % i for i in range(1, 15)]
+
+def rank_to_dict(ranks, names):
+    ranks = np.abs(ranks)
+    minmax = MinMaxScaler()
+    ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel()
+    ranks = map(lambda x: round(x, 2), ranks)
+    return dict(zip(names, ranks))
+
+ranks["Ridge"] = rank_to_dict(ridge.coef_, names)
+ranks["Random Forest"] = rank_to_dict(rf.feature_importances_, names)
+
+# Вычисляем коэффициенты корреляции между признаками и целевой переменной
+correlation_coeffs = f_regression(X, Y)[0]
+
+# Добавляем результаты корреляции в словарь ranks
+ranks["Correlation"] = rank_to_dict(correlation_coeffs, names)
+
+# Создаем пустой словарь для данных
+mean = {}
+
+# Бежим по словарю ranks
+for key, value in ranks.items():
+    # Пробегаемся по словарю значений ranks, которые являются парой имя:оценка
+    for item in value.items():
+        # Имя будет ключом для нашего mean
+        # Если элемента с текущим ключом в mean нет - добавляем
+        if item[0] not in mean:
+            mean[item[0]] = 0
+        # Суммируем значения по каждому ключу-имени признака
+        mean[item[0]] += item[1]
+
+# Находим среднее по каждому признаку
+for key, value in mean.items():
+    res = value / len(ranks)
+    mean[key] = round(res, 2)
+
+# Сортируем и распечатываем список
+mean = sorted(mean.items(), key=lambda x: x[1], reverse=True)
+
+for key, value in ranks.items():
+    ranks[key] = sorted(value.items(), key=lambda x: x[1], reverse=True)
+for key, value in ranks.items():
+    print(key)
+    print(value)
+
+print("Mean Importance:")
+for item in mean:
+    print(item[0], ":", item[1])
--- a/gusev_vladislav_lab_3/README.md
+++ b/gusev_vladislav_lab_3/README.md
@@ -0,0 +1,27 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Решите с помощью библиотечной реализации дерева решений задачу: Запрограммировать дерево решений как минимум на 99% ваших данных для задачи: Зависимость глубины алмаза (depth) от длины (x), ширины (y) и высоты алмаза (z) . Проверить работу модели на оставшемся проценте, сделать вывод.
+
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_3.py, решение будет в консоли.
+
+### Технологии
+Sklearn - библиотека с большим количеством алгоритмов машинного обучения. Нам понадобится библиотека для дерева решения регрессии sklearn.tree.DecisionTreeRegressor.
+
+### По коду
+1) Для начала загружаем данные из csv файла
+2) Разделеям данные на признаки (X) и целевую переменную (y)
+3) Разделяем данные на обучающее и тестовые
+4) Обучаем дерево регрессией (model)
+5) Выводим важность признаков, предсказание значений на тестовой выборке и оценку производительности модели
+
+Пример:
+
+![img.png](img.png)
+
+### Вывод
+- score: ~0.88. Это мера того, насколько хорошо модель соответствует данным. По значению 88% можно сказать, что модель хорошо соответствует данным.
+- feature_importances: ~0.26, ~0.34, ~0,39. Это говорит о важности признаков для нашей модели. Можно сказать, что высота (z) имеет наибольшую важность.
+- Mean Squared Error: 0.22. Это ошибка модели. Это говорит о том, что модель в среднем ошибается в 22% случаев.
+
+По итогу можно сказать, что модель отработала хорошо, из-за score ~0.88.
--- a/gusev_vladislav_lab_3/diamonds_prices.csv
+++ b/gusev_vladislav_lab_3/diamonds_prices.csv
--- a/gusev_vladislav_lab_3/gusev_vladislav_lab_3.py
+++ b/gusev_vladislav_lab_3/gusev_vladislav_lab_3.py
@@ -0,0 +1,31 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.metrics import mean_squared_error
+
+# Загрузка данных из csv-файла
+data = pd.read_csv('diamonds_prices.csv', index_col='diamond_id')
+
+# Разделение данных на признаки (X) и целевую переменную (y)
+X = data[['x', 'y', 'z']]
+
+print (X.head())
+y = data['depth']
+
+# Разделение данных на обучающую и тестовую выборки
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42)
+
+#Решение с помощью дерева регрессии
+model = DecisionTreeRegressor()
+model.fit(X_train, y_train)
+test_score = model.score(X_test, y_test)
+# Получение важности признаков
+feature_importances = model.feature_importances_
+# Предсказание значений на тестовой выборке
+y_pred = model.predict(X_test)
+# Оценка производительности модели
+mse = mean_squared_error(y_test, y_pred)
+
+print("score", test_score)
+print("feature_importances", feature_importances)
+print("Mean Squared Error: {:.2f}".format(mse))
--- a/gusev_vladislav_lab_3/img.png
+++ b/gusev_vladislav_lab_3/img.png
--- a/ilbekov_dmitriy_lab_3/F1DriversDataset.csv
+++ b/ilbekov_dmitriy_lab_3/F1DriversDataset.csv
@@ -0,0 +1,869 @@
+Driver,Nationality,Seasons,Championships,Race_Entries,Race_Starts,Pole_Positions,Race_Wins,Podiums,Fastest_Laps,Points,Active,Championship Years,Decade,Pole_Rate,Start_Rate,Win_Rate,Podium_Rate,FastLap_Rate,Points_Per_Entry,Years_Active,Champion
+Carlo Abate,Italy,"[1962, 1963]",0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+George Abecassis,United Kingdom,"[1951, 1952]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Kenny Acheson,United Kingdom,"[1983, 1985]",0.0,10.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.3,0.0,0.0,0.0,0.0,2,False
+Andrea de Adamich,Italy,"[1968, 1970, 1971, 1972, 1973]",0.0,36.0,30.0,0.0,0.0,0.0,0.0,6.0,False,,1970,0.0,0.8333333333333334,0.0,0.0,0.0,0.16666666666666666,5,False
+Philippe Adams,Belgium,[1994],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Walt Ader,United States,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Kurt Adolff,West Germany,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Fred Agabashian,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957]",0.0,9.0,8.0,1.0,0.0,0.0,0.0,1.5,False,,1950,0.1111111111111111,0.8888888888888888,0.0,0.0,0.0,0.16666666666666666,8,False
+Kurt Ahrens Jr.,West Germany,"[1966, 1967, 1968, 1969]",0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,4,False
+Jack Aitken,United Kingdom,[2020],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Christijan Albers,Netherlands,"[2005, 2006, 2007]",0.0,46.0,46.0,0.0,0.0,0.0,0.0,4.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.08695652173913043,3,False
+Alexander Albon,Thailand,"[2019, 2020, 2022]",0.0,61.0,60.0,0.0,0.0,2.0,0.0,202.0,True,,2020,0.0,0.9836065573770492,0.0,0.03278688524590164,0.0,3.3114754098360657,3,False
+Michele Alboreto,Italy,"[1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994]",0.0,215.0,194.0,2.0,5.0,23.0,5.0,186.5,False,,1990,0.009302325581395349,0.9023255813953488,0.023255813953488372,0.10697674418604651,0.023255813953488372,0.8674418604651163,14,False
+Jean Alesi,France,"[1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001]",0.0,202.0,201.0,2.0,1.0,32.0,4.0,241.0,False,,2000,0.009900990099009901,0.995049504950495,0.0049504950495049506,0.15841584158415842,0.019801980198019802,1.193069306930693,13,False
+Jaime Alguersuari,Spain,"[2009, 2010, 2011]",0.0,46.0,46.0,0.0,0.0,0.0,0.0,31.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.6739130434782609,3,False
+Philippe Alliot,France,"[1984, 1985, 1986, 1987, 1988, 1989, 1990, 1993, 1994]",0.0,116.0,109.0,0.0,0.0,0.0,0.0,7.0,False,,1990,0.0,0.9396551724137931,0.0,0.0,0.0,0.0603448275862069,9,False
+Cliff Allison,United Kingdom,"[1958, 1959, 1960, 1961]",0.0,18.0,16.0,0.0,0.0,1.0,0.0,11.0,False,,1960,0.0,0.8888888888888888,0.0,0.05555555555555555,0.0,0.6111111111111112,4,False
+Fernando Alonso,Spain,"[2001, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2021, 2022]",2.0,359.0,356.0,22.0,32.0,99.0,23.0,2076.0,True,"[2005, 2006]",2010,0.06128133704735376,0.9916434540389972,0.08913649025069638,0.2757660167130919,0.06406685236768803,5.782729805013927,19,True
+Giovanna Amati,Italy,[1992],0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+George Amick,United States,[1958],0.0,2.0,1.0,0.0,0.0,1.0,0.0,6.0,False,,1960,0.0,0.5,0.0,0.5,0.0,3.0,1,False
+Red Amick,United States,"[1959, 1960]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Chris Amon,New Zealand,"[1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976]",0.0,108.0,96.0,5.0,0.0,11.0,3.0,83.0,False,,1970,0.046296296296296294,0.8888888888888888,0.0,0.10185185185185185,0.027777777777777776,0.7685185185185185,14,False
+Bob Anderson,United Kingdom,"[1963, 1964, 1965, 1966, 1967]",0.0,29.0,25.0,0.0,0.0,1.0,0.0,8.0,False,,1960,0.0,0.8620689655172413,0.0,0.034482758620689655,0.0,0.27586206896551724,5,False
+Conny Andersson,Sweden,"[1976, 1977]",0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.2,0.0,0.0,0.0,0.0,2,False
+Emil Andres,United States,[1950],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Mario Andretti,United States,"[1968, 1969, 1970, 1971, 1972, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982]",1.0,131.0,128.0,18.0,12.0,19.0,10.0,180.0,False,[1978],1980,0.13740458015267176,0.9770992366412213,0.0916030534351145,0.1450381679389313,0.07633587786259542,1.3740458015267176,14,True
+Michael Andretti,United States,[1993],0.0,13.0,13.0,0.0,0.0,1.0,0.0,7.0,False,,1990,0.0,1.0,0.0,0.07692307692307693,0.0,0.5384615384615384,1,False
+Keith Andrews,United States,"[1955, 1956]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Elio de Angelis,Italy,"[1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986]",0.0,109.0,108.0,3.0,2.0,9.0,0.0,122.0,False,,1980,0.027522935779816515,0.9908256880733946,0.01834862385321101,0.08256880733944955,0.0,1.1192660550458715,8,False
+Marco Apicella,Italy,[1993],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Mário de Araújo Cabral,Portugal,"[1959, 1960, 1963, 1964]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,4,False
+Frank Armi,United States,[1954],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Chuck Arnold,United States,[1959],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+René Arnoux,France,"[1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989]",0.0,164.0,149.0,18.0,7.0,22.0,12.0,181.0,False,,1980,0.10975609756097561,0.9085365853658537,0.042682926829268296,0.13414634146341464,0.07317073170731707,1.103658536585366,12,False
+Peter Arundell,United Kingdom,"[1963, 1964, 1966]",0.0,13.0,11.0,0.0,0.0,2.0,0.0,12.0,False,,1960,0.0,0.8461538461538461,0.0,0.15384615384615385,0.0,0.9230769230769231,3,False
+Alberto Ascari,Italy,"[1950, 1951, 1952, 1953, 1954, 1955]",2.0,33.0,32.0,14.0,13.0,17.0,12.0,107.64,False,"[1952, 1953]",1950,0.42424242424242425,0.9696969696969697,0.3939393939393939,0.5151515151515151,0.36363636363636365,3.2618181818181817,6,True
+Peter Ashdown,United Kingdom,[1959],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ian Ashley,United Kingdom,"[1974, 1975, 1976, 1977]",0.0,11.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.36363636363636365,0.0,0.0,0.0,0.0,4,False
+Gerry Ashmore,United Kingdom,"[1961, 1962]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.75,0.0,0.0,0.0,0.0,2,False
+Bill Aston,United Kingdom,[1952],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Richard Attwood,United Kingdom,"[1964, 1965, 1967, 1968, 1969]",0.0,17.0,16.0,0.0,0.0,1.0,1.0,11.0,False,,1970,0.0,0.9411764705882353,0.0,0.058823529411764705,0.058823529411764705,0.6470588235294118,5,False
+Manny Ayulo,United States,"[1951, 1952, 1953, 1954]",0.0,6.0,4.0,0.0,0.0,1.0,0.0,2.0,False,,1950,0.0,0.6666666666666666,0.0,0.16666666666666666,0.0,0.3333333333333333,4,False
+Luca Badoer,Italy,"[1993, 1995, 1996, 1999, 2009]",0.0,58.0,50.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.8620689655172413,0.0,0.0,0.0,0.0,5,False
+Giancarlo Baghetti,Italy,"[1961, 1962, 1963, 1964, 1965, 1966, 1967]",0.0,21.0,21.0,0.0,1.0,1.0,1.0,14.0,False,,1960,0.0,1.0,0.047619047619047616,0.047619047619047616,0.047619047619047616,0.6666666666666666,7,False
+Julian Bailey,United Kingdom,"[1988, 1991]",0.0,20.0,7.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,0.35,0.0,0.0,0.0,0.05,2,False
+Mauro Baldi,Italy,"[1982, 1983, 1984, 1985]",0.0,41.0,36.0,0.0,0.0,0.0,0.0,5.0,False,,1980,0.0,0.8780487804878049,0.0,0.0,0.0,0.12195121951219512,4,False
+Bobby Ball,United States,"[1951, 1952]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,1.0,0.0,0.0,0.0,1.0,2,False
+Marcel Balsa,France,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Lorenzo Bandini,Italy,"[1961, 1962, 1963, 1964, 1965, 1966, 1967]",0.0,42.0,42.0,1.0,1.0,8.0,2.0,58.0,False,,1960,0.023809523809523808,1.0,0.023809523809523808,0.19047619047619047,0.047619047619047616,1.380952380952381,7,False
+Henry Banks,United States,"[1950, 1951, 1952]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Fabrizio Barbazza,Italy,"[1991, 1993]",0.0,20.0,8.0,0.0,0.0,0.0,0.0,2.0,False,,1990,0.0,0.4,0.0,0.0,0.0,0.1,2,False
+John Barber,United Kingdom,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Skip Barber,United States,"[1971, 1972]",0.0,6.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,2,False
+Paolo Barilla,Italy,"[1989, 1990]",0.0,15.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.6,0.0,0.0,0.0,0.0,2,False
+Rubens Barrichello,Brazil,"[1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011]",0.0,326.0,322.0,14.0,11.0,68.0,17.0,658.0,False,,2000,0.04294478527607362,0.9877300613496932,0.03374233128834356,0.2085889570552147,0.05214723926380368,2.01840490797546,19,False
+Michael Bartels,Germany,[1991],0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Edgar Barth,"East Germany, West Germany","[1953, 1957, 1958, 1960, 1961, 1964]",0.0,7.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.7142857142857143,0.0,0.0,0.0,0.0,6,False
+Giorgio Bassi,Italy,[1965],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Erwin Bauer,West Germany,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Zsolt Baumgartner,Hungary,"[2003, 2004]",0.0,20.0,20.0,0.0,0.0,0.0,0.0,1.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.05,2,False
+Élie Bayol,France,"[1952, 1953, 1954, 1955, 1956]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,0.875,0.0,0.0,0.0,0.25,5,False
+Don Beauman,United Kingdom,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Karl-Günther Bechem[g],West Germany,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Jean Behra,France,"[1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959]",0.0,53.0,52.0,0.0,0.0,9.0,1.0,51.14,False,,1960,0.0,0.9811320754716981,0.0,0.16981132075471697,0.018867924528301886,0.9649056603773585,8,False
+Derek Bell,United Kingdom,"[1968, 1969, 1970, 1971, 1972, 1974]",0.0,16.0,9.0,0.0,0.0,0.0,0.0,1.0,False,,1970,0.0,0.5625,0.0,0.0,0.0,0.0625,6,False
+Stefan Bellof,West Germany,"[1984, 1985]",0.0,22.0,20.0,0.0,0.0,0.0,0.0,4.0,False,,1980,0.0,0.9090909090909091,0.0,0.0,0.0,0.18181818181818182,2,False
+Paul Belmondo,France,"[1992, 1994]",0.0,27.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.25925925925925924,0.0,0.0,0.0,0.0,2,False
+Tom Belsø,Denmark,"[1973, 1974]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Jean-Pierre Beltoise,France,"[1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974]",0.0,88.0,86.0,0.0,1.0,8.0,4.0,77.0,False,,1970,0.0,0.9772727272727273,0.011363636363636364,0.09090909090909091,0.045454545454545456,0.875,8,False
+Olivier Beretta,Monaco,[1994],0.0,10.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.9,0.0,0.0,0.0,0.0,1,False
+Allen Berg,Canada,[1986],0.0,9.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Georges Berger,Belgium,"[1953, 1954]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Gerhard Berger,Austria,"[1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997]",0.0,210.0,210.0,12.0,10.0,48.0,21.0,385.0,False,,1990,0.05714285714285714,1.0,0.047619047619047616,0.22857142857142856,0.1,1.8333333333333333,14,False
+Éric Bernard,France,"[1989, 1990, 1991, 1994]",0.0,47.0,45.0,0.0,0.0,1.0,0.0,10.0,False,,1990,0.0,0.9574468085106383,0.0,0.02127659574468085,0.0,0.2127659574468085,4,False
+Enrique Bernoldi,Brazil,"[2001, 2002]",0.0,29.0,28.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.9655172413793104,0.0,0.0,0.0,0.0,2,False
+Enrico Bertaggia,Italy,[1989],0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Tony Bettenhausen,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,11.0,11.0,0.0,0.0,1.0,1.0,11.0,False,,1960,0.0,1.0,0.0,0.09090909090909091,0.09090909090909091,1.0,11,False
+Mike Beuttler,United Kingdom,"[1971, 1972, 1973]",0.0,29.0,28.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.9655172413793104,0.0,0.0,0.0,0.0,3,False
+Birabongse Bhanudej,Thailand,"[1950, 1951, 1952, 1953, 1954]",0.0,19.0,19.0,0.0,0.0,0.0,0.0,8.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.42105263157894735,5,False
+Jules Bianchi,France,"[2013, 2014]",0.0,34.0,34.0,0.0,0.0,0.0,0.0,2.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.058823529411764705,2,False
+Lucien Bianchi,Belgium,"[1959, 1960, 1961, 1962, 1963, 1965, 1968]",0.0,19.0,17.0,0.0,0.0,1.0,0.0,6.0,False,,1960,0.0,0.8947368421052632,0.0,0.05263157894736842,0.0,0.3157894736842105,7,False
+Gino Bianco,Brazil,[1952],0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Hans Binder,Austria,"[1976, 1977, 1978]",0.0,15.0,13.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.8666666666666667,0.0,0.0,0.0,0.0,3,False
+Clemente Biondetti,Italy,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Pablo Birger,Argentina,"[1953, 1955]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Art Bisch,United States,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Harry Blanchard,United States,[1959],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Michael Bleekemolen,Netherlands,"[1977, 1978]",0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.2,0.0,0.0,0.0,0.0,2,False
+Alex Blignaut,South Africa,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Trevor Blokdyk,South Africa,"[1963, 1965]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Mark Blundell,United Kingdom,"[1991, 1993, 1994, 1995]",0.0,63.0,61.0,0.0,0.0,3.0,0.0,32.0,False,,1990,0.0,0.9682539682539683,0.0,0.047619047619047616,0.0,0.5079365079365079,4,False
+Raul Boesel,Brazil,"[1982, 1983]",0.0,30.0,23.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.7666666666666667,0.0,0.0,0.0,0.0,2,False
+Menato Boffa,Italy,[1961],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Bob Bondurant,United States,"[1965, 1966]",0.0,9.0,9.0,0.0,0.0,0.0,0.0,3.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.3333333333333333,2,False
+Felice Bonetto,Italy,"[1950, 1951, 1952, 1953]",0.0,16.0,15.0,0.0,0.0,2.0,0.0,17.5,False,,1950,0.0,0.9375,0.0,0.125,0.0,1.09375,4,False
+Jo Bonnier,Sweden,"[1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971]",0.0,108.0,104.0,1.0,1.0,1.0,0.0,39.0,False,,1960,0.009259259259259259,0.9629629629629629,0.009259259259259259,0.009259259259259259,0.0,0.3611111111111111,16,False
+Roberto Bonomi,Argentina,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Juan Manuel Bordeu,Argentina,[1961],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Slim Borgudd,Sweden,"[1981, 1982]",0.0,15.0,10.0,0.0,0.0,0.0,0.0,1.0,False,,1980,0.0,0.6666666666666666,0.0,0.0,0.0,0.06666666666666667,2,False
+Luki Botha,South Africa,[1967],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Valtteri Bottas,Finland,"[2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",0.0,202.0,201.0,20.0,10.0,67.0,19.0,1791.0,True,,2020,0.09900990099009901,0.995049504950495,0.04950495049504951,0.3316831683168317,0.09405940594059406,8.866336633663366,10,False
+Jean-Christophe Boullion,France,[1995],0.0,11.0,11.0,0.0,0.0,0.0,0.0,3.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.2727272727272727,1,False
+Sébastien Bourdais,France,"[2008, 2009]",0.0,27.0,27.0,0.0,0.0,0.0,0.0,6.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.2222222222222222,2,False
+Thierry Boutsen,Belgium,"[1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993]",0.0,164.0,163.0,1.0,3.0,15.0,1.0,132.0,False,,1990,0.006097560975609756,0.9939024390243902,0.018292682926829267,0.09146341463414634,0.006097560975609756,0.8048780487804879,11,False
+Johnny Boyd,United States,"[1955, 1956, 1957, 1958, 1959, 1960]",0.0,6.0,6.0,0.0,0.0,1.0,0.0,4.0,False,,1960,0.0,1.0,0.0,0.16666666666666666,0.0,0.6666666666666666,6,False
+David Brabham,Australia,"[1990, 1994]",0.0,30.0,24.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.8,0.0,0.0,0.0,0.0,2,False
+Gary Brabham,Australia,[1990],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Jack Brabham,Australia,"[1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970]",3.0,128.0,126.0,13.0,14.0,31.0,12.0,253.0,False,"[1959, 1960, 1966]",1960,0.1015625,0.984375,0.109375,0.2421875,0.09375,1.9765625,16,True
+Bill Brack,Canada,"[1968, 1969, 1972]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Ernesto Brambilla,Italy,"[1963, 1969]",0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Vittorio Brambilla,Italy,"[1974, 1975, 1976, 1977, 1978, 1979, 1980]",0.0,79.0,74.0,1.0,1.0,1.0,1.0,15.5,False,,1980,0.012658227848101266,0.9367088607594937,0.012658227848101266,0.012658227848101266,0.012658227848101266,0.1962025316455696,7,False
+Toni Branca,Switzerland,"[1950, 1951]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Gianfranco Brancatelli,Italy,[1979],0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Eric Brandon,United Kingdom,"[1952, 1954]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Don Branson,United States,"[1959, 1960]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,1.0,0.0,0.0,0.0,1.5,2,False
+Tom Bridger,United Kingdom,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Tony Brise,United Kingdom,[1975],0.0,10.0,10.0,0.0,0.0,0.0,0.0,1.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.1,1,False
+Chris Bristow,United Kingdom,"[1959, 1960]",0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Peter Broeker,Canada,[1963],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Tony Brooks,United Kingdom,"[1956, 1957, 1958, 1959, 1960, 1961]",0.0,39.0,38.0,3.0,6.0,10.0,3.0,75.0,False,,1960,0.07692307692307693,0.9743589743589743,0.15384615384615385,0.2564102564102564,0.07692307692307693,1.9230769230769231,6,False
+Alan Brown,United Kingdom,"[1952, 1953, 1954]",0.0,9.0,8.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,0.8888888888888888,0.0,0.0,0.0,0.2222222222222222,3,False
+Walt Brown,United States,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Warwick Brown,Australia,[1976],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Adolf Brudes,West Germany,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Martin Brundle,United Kingdom,"[1984, 1985, 1986, 1987, 1988, 1989, 1991, 1992, 1993, 1994, 1995, 1996]",0.0,165.0,158.0,0.0,0.0,9.0,0.0,98.0,False,,1990,0.0,0.9575757575757575,0.0,0.05454545454545454,0.0,0.593939393939394,12,False
+Gianmaria Bruni,Italy,[2004],0.0,18.0,18.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jimmy Bryan,United States,"[1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,10.0,9.0,0.0,1.0,3.0,0.0,18.0,False,,1960,0.0,0.9,0.1,0.3,0.0,1.8,9,False
+Clemar Bucci,Argentina,"[1954, 1955]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Ronnie Bucknum,United States,"[1964, 1965, 1966]",0.0,11.0,11.0,0.0,0.0,0.0,0.0,2.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.18181818181818182,3,False
+Ivor Bueb,United Kingdom,"[1957, 1958, 1959]",0.0,6.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,3,False
+Sébastien Buemi,Switzerland,"[2009, 2010, 2011]",0.0,55.0,55.0,0.0,0.0,0.0,0.0,29.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.5272727272727272,3,False
+Luiz Bueno,Brazil,[1973],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ian Burgess,United Kingdom,"[1958, 1959, 1960, 1961, 1962, 1963]",0.0,20.0,16.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,6,False
+Luciano Burti,Brazil,"[2000, 2001]",0.0,15.0,14.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.9333333333333333,0.0,0.0,0.0,0.0,2,False
+Roberto Bussinello,Italy,"[1961, 1965]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Jenson Button,United Kingdom,"[2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017]",1.0,309.0,306.0,8.0,15.0,50.0,8.0,1235.0,False,[2009],2010,0.025889967637540454,0.9902912621359223,0.04854368932038835,0.16181229773462782,0.025889967637540454,3.9967637540453076,18,True
+Tommy Byrne,Ireland,[1982],0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.4,0.0,0.0,0.0,0.0,1,False
+Giulio Cabianca,Italy,"[1958, 1959, 1960]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,0.75,0.0,0.0,0.0,0.75,3,False
+Phil Cade,United States,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Alex Caffi,Italy,"[1986, 1987, 1988, 1989, 1990, 1991]",0.0,75.0,56.0,0.0,0.0,0.0,0.0,6.0,False,,1990,0.0,0.7466666666666667,0.0,0.0,0.0,0.08,6,False
+John Campbell-Jones,United Kingdom,"[1962, 1963]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Adrián Campos,Spain,"[1987, 1988]",0.0,21.0,17.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.8095238095238095,0.0,0.0,0.0,0.0,2,False
+John Cannon,Canada,[1971],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Eitel Cantoni,Uruguay,[1952],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bill Cantrell,United States,[1950],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Ivan Capelli,Italy,"[1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993]",0.0,98.0,93.0,0.0,0.0,3.0,0.0,31.0,False,,1990,0.0,0.9489795918367347,0.0,0.030612244897959183,0.0,0.3163265306122449,9,False
+Piero Carini,Italy,"[1952, 1953]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Duane Carter,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1959, 1960]",0.0,8.0,8.0,0.0,0.0,1.0,0.0,6.5,False,,1950,0.0,1.0,0.0,0.125,0.0,0.8125,8,False
+Eugenio Castellotti,Italy,"[1955, 1956, 1957]",0.0,14.0,14.0,1.0,0.0,3.0,0.0,19.5,False,,1960,0.07142857142857142,1.0,0.0,0.21428571428571427,0.0,1.3928571428571428,3,False
+Johnny Cecotto,Venezuela,"[1983, 1984]",0.0,23.0,18.0,0.0,0.0,0.0,0.0,1.0,False,,1980,0.0,0.782608695652174,0.0,0.0,0.0,0.043478260869565216,2,False
+Andrea de Cesaris,Italy,"[1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994]",0.0,214.0,208.0,1.0,0.0,5.0,1.0,59.0,False,,1990,0.004672897196261682,0.9719626168224299,0.0,0.02336448598130841,0.004672897196261682,0.2757009345794392,15,False
+François Cevert,France,"[1970, 1971, 1972, 1973]",0.0,47.0,46.0,0.0,1.0,13.0,2.0,89.0,False,,1970,0.0,0.9787234042553191,0.02127659574468085,0.2765957446808511,0.0425531914893617,1.8936170212765957,4,False
+Eugène Chaboud,France,"[1950, 1951]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,1.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.3333333333333333,2,False
+Jay Chamberlain,United States,[1962],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Karun Chandhok,India,"[2010, 2011]",0.0,11.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Alain de Changy,Belgium,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Colin Chapman,United Kingdom,[1956],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Dave Charlton,South Africa,"[1965, 1967, 1968, 1970, 1971, 1972, 1973, 1974, 1975]",0.0,14.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.7857142857142857,0.0,0.0,0.0,0.0,9,False
+Pedro Chaves,Portugal,[1991],0.0,13.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Bill Cheesbourg,United States,"[1957, 1958, 1959]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.75,0.0,0.0,0.0,0.0,3,False
+Eddie Cheever,United States,"[1978, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989]",0.0,143.0,132.0,0.0,0.0,9.0,0.0,70.0,False,,1980,0.0,0.9230769230769231,0.0,0.06293706293706294,0.0,0.48951048951048953,11,False
+Andrea Chiesa,Switzerland,[1992],0.0,10.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.3,0.0,0.0,0.0,0.0,1,False
+Max Chilton,United Kingdom,"[2013, 2014]",0.0,35.0,35.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Ettore Chimeri,Venezuela,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Louis Chiron,Monaco,"[1950, 1951, 1953, 1955, 1956, 1958]",0.0,19.0,15.0,0.0,0.0,1.0,0.0,4.0,False,,1950,0.0,0.7894736842105263,0.0,0.05263157894736842,0.0,0.21052631578947367,6,False
+Joie Chitwood,United States,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,False,,1950,0.0,1.0,0.0,0.0,0.0,1.0,1,False
+Bob Christie,United States,"[1956, 1957, 1958, 1959, 1960]",0.0,7.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.7142857142857143,0.0,0.0,0.0,0.0,5,False
+Johnny Claes,Belgium,"[1950, 1951, 1952, 1953, 1955]",0.0,25.0,23.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.92,0.0,0.0,0.0,0.0,5,False
+David Clapham,South Africa,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Jim Clark,United Kingdom,"[1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968]",2.0,73.0,72.0,33.0,25.0,32.0,28.0,255.0,False,"[1963, 1965]",1960,0.4520547945205479,0.9863013698630136,0.3424657534246575,0.4383561643835616,0.3835616438356164,3.493150684931507,9,True
+Kevin Cogan,United States,"[1980, 1981]",0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Peter Collins,United Kingdom,"[1952, 1953, 1954, 1955, 1956, 1957, 1958]",0.0,35.0,32.0,0.0,3.0,9.0,0.0,47.0,False,,1960,0.0,0.9142857142857143,0.08571428571428572,0.2571428571428571,0.0,1.3428571428571427,7,False
+Bernard Collomb,France,"[1961, 1962, 1963, 1964]",0.0,6.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,4,False
+Alberto Colombo,Italy,[1978],0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Érik Comas,France,"[1991, 1992, 1993, 1994]",0.0,63.0,59.0,0.0,0.0,0.0,0.0,7.0,False,,1990,0.0,0.9365079365079365,0.0,0.0,0.0,0.1111111111111111,4,False
+Franco Comotti,Italy,"[1950, 1952]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+George Connor,United States,"[1950, 1951, 1952]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.75,0.0,0.0,0.0,0.0,3,False
+George Constantine,United States,[1959],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+John Cordts,Canada,[1969],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+David Coulthard,United Kingdom,"[1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008]",0.0,247.0,246.0,12.0,13.0,62.0,18.0,535.0,False,,2000,0.048582995951417005,0.9959514170040485,0.05263157894736842,0.25101214574898784,0.0728744939271255,2.165991902834008,15,False
+Piers Courage,United Kingdom,"[1967, 1968, 1969, 1970]",0.0,29.0,27.0,0.0,0.0,2.0,0.0,20.0,False,,1970,0.0,0.9310344827586207,0.0,0.06896551724137931,0.0,0.6896551724137931,4,False
+Chris Craft,United Kingdom,[1971],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jim Crawford,United Kingdom,[1975],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ray Crawford,United States,"[1955, 1956, 1959]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Alberto Crespo,Argentina,[1952],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Antonio Creus,Spain,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Larry Crockett,United States,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Tony Crook,United Kingdom,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Art Cross,United States,"[1952, 1953, 1954, 1955]",0.0,4.0,4.0,0.0,0.0,1.0,0.0,8.0,False,,1950,0.0,1.0,0.0,0.25,0.0,2.0,4,False
+Geoffrey Crossley,United Kingdom,[1950],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jérôme d'Ambrosio,Belgium,"[2011, 2012]",0.0,20.0,20.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Chuck Daigh,United States,[1960],0.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Yannick Dalmas,France,"[1987, 1988, 1989, 1990, 1994]",0.0,49.0,24.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.4897959183673469,0.0,0.0,0.0,0.0,5,False
+Derek Daly,Ireland,"[1978, 1979, 1980, 1981, 1982]",0.0,64.0,49.0,0.0,0.0,0.0,0.0,15.0,False,,1980,0.0,0.765625,0.0,0.0,0.0,0.234375,5,False
+Christian Danner,West Germany,"[1985, 1986, 1987, 1989]",0.0,47.0,36.0,0.0,0.0,0.0,0.0,4.0,False,,1990,0.0,0.7659574468085106,0.0,0.0,0.0,0.0851063829787234,4,False
+Jorge Daponte,Argentina,[1954],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Anthony Davidson,United Kingdom,"[2002, 2005, 2007, 2008]",0.0,24.0,24.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,4,False
+Jimmy Davies,United States,"[1950, 1951, 1953, 1954, 1955]",0.0,8.0,5.0,0.0,0.0,1.0,0.0,4.0,False,,1950,0.0,0.625,0.0,0.125,0.0,0.5,5,False
+Colin Davis,United Kingdom,[1959],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jimmy Daywalt,United States,"[1953, 1954, 1955, 1956, 1957, 1959]",0.0,10.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6,0.0,0.0,0.0,0.0,6,False
+Jean-Denis Délétraz,Switzerland,"[1994, 1995]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Patrick Depailler,France,"[1972, 1974, 1975, 1976, 1977, 1978, 1979, 1980]",0.0,95.0,95.0,1.0,2.0,19.0,4.0,139.0,False,,1980,0.010526315789473684,1.0,0.021052631578947368,0.2,0.042105263157894736,1.4631578947368422,8,False
+Pedro Diniz,Brazil,"[1995, 1996, 1997, 1998, 1999, 2000]",0.0,99.0,98.0,0.0,0.0,0.0,0.0,10.0,False,,2000,0.0,0.98989898989899,0.0,0.0,0.0,0.10101010101010101,6,False
+Duke Dinsmore,United States,"[1950, 1951, 1953, 1956]",0.0,6.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,4,False
+Frank Dochnal,United States,[1963],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+José Dolhem,France,[1974],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Martin Donnelly,United Kingdom,"[1989, 1990]",0.0,15.0,13.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.8666666666666667,0.0,0.0,0.0,0.0,2,False
+Mark Donohue,United States,"[1971, 1974, 1975]",0.0,16.0,14.0,0.0,0.0,1.0,0.0,8.0,False,,1970,0.0,0.875,0.0,0.0625,0.0,0.5,3,False
+Robert Doornbos,Monaco Netherlands,"[2005, 2006]",0.0,11.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Ken Downing,United Kingdom,[1952],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bob Drake,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Paddy Driver,South Africa,"[1963, 1974]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Piero Drogo,Italy,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bernard de Dryver,Belgium,"[1977, 1978]",0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Johnny Dumfries,United Kingdom,[1986],0.0,16.0,15.0,0.0,0.0,0.0,0.0,3.0,False,,1990,0.0,0.9375,0.0,0.0,0.0,0.1875,1,False
+Geoff Duke,United Kingdom,[1961],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Len Duncan,United States,[1954],0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.25,0.0,0.0,0.0,0.0,1,False
+Piero Dusio,Italy,[1952],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+George Eaton,Canada,"[1969, 1970, 1971]",0.0,13.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.8461538461538461,0.0,0.0,0.0,0.0,3,False
+Bernie Ecclestone,United Kingdom,[1958],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Don Edmunds,United States,[1957],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Guy Edwards,United Kingdom,"[1974, 1976, 1977]",0.0,17.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6470588235294118,0.0,0.0,0.0,0.0,3,False
+Vic Elford,United Kingdom,"[1968, 1969, 1971]",0.0,13.0,13.0,0.0,0.0,0.0,0.0,8.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.6153846153846154,3,False
+Ed Elisian,United States,"[1954, 1955, 1956, 1957, 1958]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,5,False
+Paul Emery,United Kingdom,"[1956, 1958]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Tomáš Enge,Czech Republic,[2001],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Paul England,Australia,[1957],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Marcus Ericsson,Sweden,"[2014, 2015, 2016, 2017, 2018]",0.0,97.0,97.0,0.0,0.0,0.0,0.0,18.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.18556701030927836,5,False
+Harald Ertl,Austria,"[1975, 1976, 1977, 1978, 1980]",0.0,28.0,19.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6785714285714286,0.0,0.0,0.0,0.0,5,False
+Nasif Estéfano,Argentina,"[1960, 1962]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Philippe Étancelin,France,"[1950, 1951, 1952]",0.0,12.0,12.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.25,3,False
+Bob Evans,United Kingdom,"[1975, 1976]",0.0,12.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,2,False
+Corrado Fabi,Italy,"[1983, 1984]",0.0,18.0,12.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Teo Fabi,Italy,"[1982, 1984, 1985, 1986, 1987]",0.0,71.0,64.0,3.0,0.0,2.0,2.0,23.0,False,,1980,0.04225352112676056,0.9014084507042254,0.0,0.028169014084507043,0.028169014084507043,0.323943661971831,5,False
+Pascal Fabre,France,[1987],0.0,14.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.7857142857142857,0.0,0.0,0.0,0.0,1,False
+Carlo Facetti,Italy,[1974],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Luigi Fagioli,Italy,"[1950, 1951]",0.0,7.0,7.0,0.0,1.0,6.0,0.0,28.0,False,,1950,0.0,1.0,0.14285714285714285,0.8571428571428571,0.0,4.0,2,False
+Jack Fairman,United Kingdom,"[1953, 1955, 1956, 1957, 1958, 1959, 1960, 1961]",0.0,13.0,12.0,0.0,0.0,0.0,0.0,5.0,False,,1960,0.0,0.9230769230769231,0.0,0.0,0.0,0.38461538461538464,8,False
+Juan Manuel Fangio,Argentina,"[1950, 1951, 1953, 1954, 1955, 1956, 1957, 1958]",5.0,52.0,51.0,29.0,24.0,35.0,23.0,245.0,False,"[1951, 1954, 1955, 1956, 1957]",1950,0.5576923076923077,0.9807692307692307,0.46153846153846156,0.6730769230769231,0.4423076923076923,4.711538461538462,8,True
+Nino Farina,Italy,"[1950, 1951, 1952, 1953, 1954, 1955]",1.0,34.0,33.0,5.0,5.0,20.0,5.0,115.33,False,[1950],1950,0.14705882352941177,0.9705882352941176,0.14705882352941177,0.5882352941176471,0.14705882352941177,3.392058823529412,6,True
+Walt Faulkner,United States,"[1950, 1951, 1953, 1954, 1955]",0.0,6.0,5.0,1.0,0.0,0.0,0.0,1.0,False,,1950,0.16666666666666666,0.8333333333333334,0.0,0.0,0.0,0.16666666666666666,5,False
+William Ferguson,South Africa,[1972],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Maria Teresa de Filippis,Italy,"[1958, 1959]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6,0.0,0.0,0.0,0.0,2,False
+Ralph Firman,Ireland,[2003],0.0,15.0,14.0,0.0,0.0,0.0,0.0,1.0,False,,2000,0.0,0.9333333333333333,0.0,0.0,0.0,0.06666666666666667,1,False
+Ludwig Fischer,West Germany,[1952],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Rudi Fischer,Switzerland,"[1951, 1952]",0.0,8.0,7.0,0.0,0.0,2.0,0.0,10.0,False,,1950,0.0,0.875,0.0,0.25,0.0,1.25,2,False
+Mike Fisher,United States,[1967],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Giancarlo Fisichella,Italy,"[1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]",0.0,231.0,229.0,4.0,3.0,19.0,2.0,275.0,False,,2000,0.017316017316017316,0.9913419913419913,0.012987012987012988,0.08225108225108226,0.008658008658008658,1.1904761904761905,14,False
+John Fitch,United States,"[1953, 1955]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Christian Fittipaldi,Brazil,"[1992, 1993, 1994]",0.0,43.0,40.0,0.0,0.0,0.0,0.0,12.0,False,,1990,0.0,0.9302325581395349,0.0,0.0,0.0,0.27906976744186046,3,False
+Emerson Fittipaldi,Brazil,"[1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980]",2.0,149.0,144.0,6.0,14.0,35.0,6.0,281.0,False,"[1972, 1974]",1980,0.040268456375838924,0.9664429530201343,0.09395973154362416,0.2348993288590604,0.040268456375838924,1.8859060402684564,11,True
+Pietro Fittipaldi,Brazil,[2020],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Wilson Fittipaldi,Brazil,"[1972, 1973, 1975]",0.0,38.0,35.0,0.0,0.0,0.0,0.0,3.0,False,,1970,0.0,0.9210526315789473,0.0,0.0,0.0,0.07894736842105263,3,False
+Theo Fitzau,East Germany,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Pat Flaherty,United States,"[1950, 1953, 1954, 1955, 1956, 1959]",0.0,6.0,6.0,1.0,1.0,1.0,0.0,8.0,False,,1950,0.16666666666666666,1.0,0.16666666666666666,0.16666666666666666,0.0,1.3333333333333333,6,False
+Jan Flinterman,Netherlands,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ron Flockhart,United Kingdom,"[1954, 1956, 1957, 1958, 1959, 1960]",0.0,14.0,14.0,0.0,0.0,1.0,0.0,5.0,False,,1960,0.0,1.0,0.0,0.07142857142857142,0.0,0.35714285714285715,6,False
+Myron Fohr,United States,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Gregor Foitek,Switzerland,"[1989, 1990]",0.0,22.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.3181818181818182,0.0,0.0,0.0,0.0,2,False
+George Follmer,United States,[1973],0.0,13.0,12.0,0.0,0.0,1.0,0.0,5.0,False,,1970,0.0,0.9230769230769231,0.0,0.07692307692307693,0.0,0.38461538461538464,1,False
+George Fonder,United States,"[1952, 1954]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Norberto Fontana,Argentina,[1997],0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Asdrúbal Fontes Bayardo,Uruguay,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Carl Forberg,United States,[1951],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Gene Force,United States,"[1951, 1960]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Franco Forini,Switzerland,[1987],0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,1,False
+Philip Fotheringham-Parker,United Kingdom,[1951],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+A. J. Foyt,United States,"[1958, 1959, 1960]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Giorgio Francia,Italy,"[1977, 1981]",0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Don Freeland,United States,"[1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,8.0,8.0,0.0,0.0,1.0,0.0,4.0,False,,1960,0.0,1.0,0.0,0.125,0.0,0.5,8,False
+Heinz-Harald Frentzen,Germany,"[1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003]",0.0,160.0,156.0,2.0,3.0,18.0,6.0,174.0,False,,2000,0.0125,0.975,0.01875,0.1125,0.0375,1.0875,10,False
+Paul Frère,Belgium,"[1952, 1953, 1954, 1955, 1956]",0.0,11.0,11.0,0.0,0.0,1.0,0.0,11.0,False,,1950,0.0,1.0,0.0,0.09090909090909091,0.0,1.0,5,False
+Patrick Friesacher,Austria,[2005],0.0,11.0,11.0,0.0,0.0,0.0,0.0,3.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.2727272727272727,1,False
+Joe Fry,United Kingdom,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Hiroshi Fushida,Japan,[1975],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Beppe Gabbiani,Italy,"[1978, 1981]",0.0,17.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.17647058823529413,0.0,0.0,0.0,0.0,2,False
+Bertrand Gachot,Belgium France,"[1989, 1990, 1991, 1992, 1994, 1995]",0.0,84.0,47.0,0.0,0.0,0.0,1.0,5.0,False,,1990,0.0,0.5595238095238095,0.0,0.0,0.011904761904761904,0.05952380952380952,6,False
+Patrick Gaillard,France,[1979],0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.4,0.0,0.0,0.0,0.0,1,False
+Divina Galica,United Kingdom,"[1976, 1978]",0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Nanni Galli,Italy,"[1970, 1971, 1972, 1973]",0.0,20.0,17.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.85,0.0,0.0,0.0,0.0,4,False
+Oscar Alfredo Gálvez,Argentina,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,1.0,0.0,0.0,0.0,2.0,1,False
+Fred Gamble,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Howden Ganley,New Zealand,"[1971, 1972, 1973, 1974]",0.0,41.0,35.0,0.0,0.0,0.0,0.0,10.0,False,,1970,0.0,0.8536585365853658,0.0,0.0,0.0,0.24390243902439024,4,False
+Giedo van der Garde,Netherlands,[2013],0.0,19.0,19.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Frank Gardner,Australia,"[1964, 1965, 1968]",0.0,9.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.8888888888888888,0.0,0.0,0.0,0.0,3,False
+Billy Garrett,United States,"[1956, 1958]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Jo Gartner,Austria,[1984],0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Pierre Gasly,France,"[2017, 2018, 2019, 2020, 2021, 2022]",0.0,109.0,109.0,0.0,1.0,3.0,3.0,334.0,True,,2020,0.0,1.0,0.009174311926605505,0.027522935779816515,0.027522935779816515,3.0642201834862384,6,False
+Tony Gaze,Australia,[1952],0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.75,0.0,0.0,0.0,0.0,1,False
+Geki,Italy,"[1964, 1965, 1966]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,3,False
+Olivier Gendebien,Belgium,"[1956, 1958, 1959, 1960, 1961]",0.0,15.0,14.0,0.0,0.0,2.0,0.0,18.0,False,,1960,0.0,0.9333333333333333,0.0,0.13333333333333333,0.0,1.2,5,False
+Marc Gené,Spain,"[1999, 2000, 2003, 2004]",0.0,36.0,36.0,0.0,0.0,0.0,0.0,5.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.1388888888888889,4,False
+Elmer George,United States,[1957],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Bob Gerard,United Kingdom,"[1950, 1951, 1953, 1954, 1956, 1957]",0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,6,False
+Gerino Gerini,Italy,"[1956, 1958]",0.0,7.0,6.0,0.0,0.0,0.0,0.0,1.5,False,,1960,0.0,0.8571428571428571,0.0,0.0,0.0,0.21428571428571427,2,False
+Peter Gethin,United Kingdom,"[1970, 1971, 1972, 1973, 1974]",0.0,31.0,30.0,0.0,1.0,1.0,0.0,11.0,False,,1970,0.0,0.967741935483871,0.03225806451612903,0.03225806451612903,0.0,0.3548387096774194,5,False
+Piercarlo Ghinzani,Italy,"[1981, 1983, 1984, 1985, 1986, 1987, 1988, 1989]",0.0,111.0,74.0,0.0,0.0,0.0,0.0,2.0,False,,1990,0.0,0.6666666666666666,0.0,0.0,0.0,0.018018018018018018,8,False
+Bruno Giacomelli,Italy,"[1977, 1978, 1979, 1980, 1981, 1982, 1983, 1990]",0.0,82.0,69.0,1.0,0.0,1.0,0.0,14.0,False,,1980,0.012195121951219513,0.8414634146341463,0.0,0.012195121951219513,0.0,0.17073170731707318,8,False
+Dick Gibson,United Kingdom,"[1957, 1958]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Gimax,Italy,[1978],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Richie Ginther,United States,"[1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967]",0.0,54.0,52.0,0.0,1.0,14.0,3.0,102.0,False,,1960,0.0,0.9629629629629629,0.018518518518518517,0.25925925925925924,0.05555555555555555,1.8888888888888888,8,False
+Antonio Giovinazzi,Italy,"[2017, 2019, 2020, 2021]",0.0,62.0,62.0,0.0,0.0,0.0,0.0,21.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.3387096774193548,4,False
+Yves Giraud-Cabantous,France,"[1950, 1951, 1952, 1953]",0.0,13.0,13.0,0.0,0.0,0.0,0.0,5.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.38461538461538464,4,False
+Ignazio Giunti,Italy,[1970],0.0,4.0,4.0,0.0,0.0,0.0,0.0,3.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.75,1,False
+Timo Glock,Germany,"[2004, 2008, 2009, 2010, 2011, 2012]",0.0,95.0,91.0,0.0,0.0,3.0,1.0,51.0,False,,2010,0.0,0.9578947368421052,0.0,0.031578947368421054,0.010526315789473684,0.5368421052631579,6,False
+Helm Glöckler,West Germany,[1953],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Paco Godia,Spain,"[1951, 1954, 1956, 1957, 1958]",0.0,14.0,13.0,0.0,0.0,0.0,0.0,6.0,False,,1960,0.0,0.9285714285714286,0.0,0.0,0.0,0.42857142857142855,5,False
+Carel Godin de Beaufort,Netherlands,"[1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964]",0.0,31.0,28.0,0.0,0.0,0.0,0.0,4.0,False,,1960,0.0,0.9032258064516129,0.0,0.0,0.0,0.12903225806451613,8,False
+Christian Goethals,Belgium,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Paul Goldsmith,United States,"[1958, 1959, 1960]",0.0,3.0,3.0,0.0,0.0,1.0,0.0,6.0,False,,1960,0.0,1.0,0.0,0.3333333333333333,0.0,2.0,3,False
+José Froilán González,Argentina,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1960]",0.0,26.0,26.0,3.0,2.0,15.0,6.0,72.14,False,,1950,0.11538461538461539,1.0,0.07692307692307693,0.5769230769230769,0.23076923076923078,2.7746153846153847,9,False
+Óscar González,Uruguay,[1956],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Aldo Gordini,France,[1951],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Horace Gould,United Kingdom,"[1954, 1955, 1956, 1957, 1958, 1960]",0.0,18.0,14.0,0.0,0.0,0.0,0.0,2.0,False,,1960,0.0,0.7777777777777778,0.0,0.0,0.0,0.1111111111111111,6,False
+Jean-Marc Gounon,France,"[1993, 1994]",0.0,9.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Emmanuel de Graffenried,Switzerland,"[1950, 1951, 1952, 1953, 1954, 1956]",0.0,23.0,22.0,0.0,0.0,0.0,0.0,9.0,False,,1950,0.0,0.9565217391304348,0.0,0.0,0.0,0.391304347826087,6,False
+Lucas di Grassi,Brazil,[2010],0.0,19.0,18.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,0.9473684210526315,0.0,0.0,0.0,0.0,1,False
+Cecil Green,United States,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,1.0,0.0,0.0,0.0,1.5,2,False
+Keith Greene,United Kingdom,"[1959, 1960, 1961, 1962]",0.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,4,False
+Masten Gregory,United States,"[1957, 1958, 1959, 1960, 1961, 1962, 1963, 1965]",0.0,43.0,38.0,0.0,0.0,3.0,0.0,21.0,False,,1960,0.0,0.8837209302325582,0.0,0.06976744186046512,0.0,0.4883720930232558,8,False
+Cliff Griffith,United States,"[1951, 1952, 1956]",0.0,7.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.42857142857142855,0.0,0.0,0.0,0.0,3,False
+Georges Grignard,France,[1951],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bobby Grim,United States,"[1959, 1960]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Romain Grosjean,France,"[2009, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]",0.0,181.0,179.0,0.0,0.0,10.0,1.0,391.0,False,,2020,0.0,0.988950276243094,0.0,0.055248618784530384,0.0055248618784530384,2.160220994475138,10,False
+Olivier Grouillard,France,"[1989, 1990, 1991, 1992]",0.0,62.0,41.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,0.6612903225806451,0.0,0.0,0.0,0.016129032258064516,4,False
+Brian Gubby,United Kingdom,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+André Guelfi,France,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Miguel Ángel Guerra,Argentina,[1981],0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.25,0.0,0.0,0.0,0.0,1,False
+Roberto Guerrero,Colombia,"[1982, 1983]",0.0,29.0,21.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.7241379310344828,0.0,0.0,0.0,0.0,2,False
+Maurício Gugelmin,Brazil,"[1988, 1989, 1990, 1991, 1992]",0.0,80.0,74.0,0.0,0.0,1.0,1.0,10.0,False,,1990,0.0,0.925,0.0,0.0125,0.0125,0.125,5,False
+Dan Gurney,United States,"[1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1970]",0.0,87.0,86.0,3.0,4.0,19.0,6.0,133.0,False,,1960,0.034482758620689655,0.9885057471264368,0.04597701149425287,0.21839080459770116,0.06896551724137931,1.528735632183908,11,False
+Esteban Gutiérrez,Mexico,"[2013, 2014, 2016]",0.0,59.0,59.0,0.0,0.0,0.0,1.0,6.0,False,,2010,0.0,1.0,0.0,0.0,0.01694915254237288,0.1016949152542373,3,False
+Hubert Hahne,West Germany,"[1967, 1968, 1970]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,3,False
+Mike Hailwood,United Kingdom,"[1963, 1964, 1965, 1971, 1972, 1973, 1974]",0.0,50.0,50.0,0.0,0.0,2.0,1.0,29.0,False,,1970,0.0,1.0,0.0,0.04,0.02,0.58,7,False
+Mika Häkkinen,Finland,"[1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001]",2.0,165.0,161.0,26.0,20.0,51.0,25.0,420.0,False,"[1998, 1999]",2000,0.15757575757575756,0.9757575757575757,0.12121212121212122,0.3090909090909091,0.15151515151515152,2.5454545454545454,11,True
+Bruce Halford,United Kingdom,"[1956, 1957, 1959, 1960]",0.0,9.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8888888888888888,0.0,0.0,0.0,0.0,4,False
+Jim Hall,United States,"[1960, 1961, 1962, 1963]",0.0,12.0,11.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,0.9166666666666666,0.0,0.0,0.0,0.25,4,False
+Duncan Hamilton,United Kingdom,"[1951, 1952, 1953]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Lewis Hamilton,United Kingdom,"[2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",7.0,311.0,311.0,103.0,103.0,191.0,61.0,4415.5,True,"[2008, 2014, 2015, 2017, 2018, 2019, 2020]",2010,0.3311897106109325,1.0,0.3311897106109325,0.6141479099678456,0.19614147909967847,14.19774919614148,16,True
+David Hampshire,United Kingdom,[1950],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Sam Hanks,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957]",0.0,8.0,8.0,0.0,1.0,4.0,0.0,20.0,False,,1950,0.0,1.0,0.125,0.5,0.0,2.5,8,False
+Walt Hansgen,United States,"[1961, 1964]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,2.0,False,,1960,0.0,1.0,0.0,0.0,0.0,1.0,2,False
+Mike Harris,South Africa,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Cuth Harrison,United Kingdom,[1950],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Brian Hart,United Kingdom,[1967],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Brendon Hartley,New Zealand,"[2017, 2018]",0.0,25.0,25.0,0.0,0.0,0.0,0.0,4.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.16,2,False
+Gene Hartley,United States,"[1950, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,10.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,10,False
+Rio Haryanto,Indonesia,[2016],0.0,12.0,12.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Masahiro Hasemi,Japan,[1976],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Naoki Hattori,Japan,[1991],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Paul Hawkins,Australia,[1965],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Mike Hawthorn,United Kingdom,"[1952, 1953, 1954, 1955, 1956, 1957, 1958]",1.0,47.0,45.0,4.0,3.0,18.0,6.0,112.64,False,[1958],1960,0.0851063829787234,0.9574468085106383,0.06382978723404255,0.3829787234042553,0.1276595744680851,2.3965957446808512,7,True
+Boy Hayje,Netherlands,"[1976, 1977]",0.0,7.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.42857142857142855,0.0,0.0,0.0,0.0,2,False
+Willi Heeks,West Germany,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Nick Heidfeld,Germany,"[2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011]",0.0,185.0,183.0,1.0,0.0,13.0,2.0,259.0,False,,2010,0.005405405405405406,0.9891891891891892,0.0,0.07027027027027027,0.010810810810810811,1.4,12,False
+Theo Helfrich,West Germany,"[1952, 1953, 1954]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Mack Hellings,United States,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Brian Henton,United Kingdom,"[1975, 1977, 1981, 1982]",0.0,37.0,19.0,0.0,0.0,0.0,1.0,0.0,False,,1980,0.0,0.5135135135135135,0.0,0.0,0.02702702702702703,0.0,4,False
+Johnny Herbert,United Kingdom,"[1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000]",0.0,165.0,160.0,0.0,3.0,7.0,0.0,98.0,False,,1990,0.0,0.9696969696969697,0.01818181818181818,0.04242424242424243,0.0,0.593939393939394,12,False
+Al Herman,United States,"[1955, 1956, 1957, 1959, 1960]",0.0,8.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.625,0.0,0.0,0.0,0.0,5,False
+Hans Herrmann,West Germany,"[1953, 1954, 1955, 1957, 1958, 1959, 1960, 1961]",0.0,19.0,17.0,0.0,0.0,1.0,1.0,10.0,False,,1960,0.0,0.8947368421052632,0.0,0.05263157894736842,0.05263157894736842,0.5263157894736842,8,False
+François Hesnault,France,"[1984, 1985]",0.0,21.0,19.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.9047619047619048,0.0,0.0,0.0,0.0,2,False
+Hans Heyer,West Germany,[1977],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Damon Hill,United Kingdom,"[1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999]",1.0,122.0,115.0,20.0,22.0,42.0,19.0,360.0,False,[1996],2000,0.16393442622950818,0.9426229508196722,0.18032786885245902,0.3442622950819672,0.1557377049180328,2.9508196721311477,8,True
+Graham Hill,United Kingdom,"[1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975]",2.0,179.0,176.0,13.0,14.0,36.0,10.0,270.0,False,"[1962, 1968]",1970,0.07262569832402235,0.9832402234636871,0.0782122905027933,0.2011173184357542,0.055865921787709494,1.5083798882681565,18,True
+Phil Hill,United States,"[1958, 1959, 1960, 1961, 1962, 1963, 1964, 1966]",1.0,52.0,49.0,6.0,3.0,16.0,6.0,94.0,False,[1961],1960,0.11538461538461539,0.9423076923076923,0.057692307692307696,0.3076923076923077,0.11538461538461539,1.8076923076923077,8,True
+Peter Hirt,Switzerland,"[1951, 1952, 1953]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+David Hobbs,United Kingdom,"[1967, 1968, 1971, 1974]",0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,4,False
+Gary Hocking,Rhodesia and Nyasaland,[1962],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Ingo Hoffmann,Brazil,"[1976, 1977]",0.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Bill Holland,United States,"[1950, 1953]",0.0,3.0,2.0,0.0,0.0,1.0,0.0,6.0,False,,1950,0.0,0.6666666666666666,0.0,0.3333333333333333,0.0,2.0,2,False
+Jackie Holmes,United States,"[1950, 1953]",0.0,4.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Bill Homeier,United States,"[1954, 1955, 1960]",0.0,6.0,3.0,0.0,0.0,0.0,0.0,1.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.16666666666666666,3,False
+Kazuyoshi Hoshino,Japan,"[1976, 1977]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Jerry Hoyt,United States,"[1950, 1953, 1954, 1955]",0.0,4.0,4.0,1.0,0.0,0.0,0.0,0.0,False,,1950,0.25,1.0,0.0,0.0,0.0,0.0,4,False
+Nico Hülkenberg,Germany,"[2010, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2022]",0.0,185.0,182.0,1.0,0.0,0.0,2.0,521.0,True,,2020,0.005405405405405406,0.9837837837837838,0.0,0.0,0.010810810810810811,2.8162162162162163,11,False
+Denny Hulme,New Zealand,"[1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974]",1.0,112.0,112.0,1.0,8.0,33.0,9.0,248.0,False,[1967],1970,0.008928571428571428,1.0,0.07142857142857142,0.29464285714285715,0.08035714285714286,2.2142857142857144,10,True
+James Hunt,United Kingdom,"[1973, 1974, 1975, 1976, 1977, 1978, 1979]",1.0,93.0,92.0,14.0,10.0,23.0,8.0,179.0,False,[1976],1980,0.15053763440860216,0.989247311827957,0.10752688172043011,0.24731182795698925,0.08602150537634409,1.924731182795699,7,True
+Jim Hurtubise,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Gus Hutchison,United States,[1970],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jacky Ickx,Belgium,"[1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979]",0.0,120.0,114.0,13.0,8.0,25.0,14.0,181.0,False,,1970,0.10833333333333334,0.95,0.06666666666666667,0.20833333333333334,0.11666666666666667,1.5083333333333333,13,False
+Yuji Ide,Japan,[2006],0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jesús Iglesias,Argentina,[1955],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Taki Inoue,Japan,"[1994, 1995]",0.0,18.0,18.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Innes Ireland,United Kingdom,"[1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966]",0.0,53.0,50.0,0.0,1.0,4.0,1.0,47.0,False,,1960,0.0,0.9433962264150944,0.018867924528301886,0.07547169811320754,0.018867924528301886,0.8867924528301887,8,False
+Eddie Irvine,United Kingdom,"[1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002]",0.0,148.0,145.0,0.0,4.0,26.0,1.0,191.0,False,,2000,0.0,0.9797297297297297,0.02702702702702703,0.17567567567567569,0.006756756756756757,1.2905405405405406,10,False
+Chris Irwin,United Kingdom,"[1966, 1967]",0.0,10.0,10.0,0.0,0.0,0.0,0.0,2.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.2,2,False
+Jean-Pierre Jabouille,France,"[1974, 1975, 1977, 1978, 1979, 1980, 1981]",0.0,55.0,49.0,6.0,2.0,2.0,0.0,21.0,False,,1980,0.10909090909090909,0.8909090909090909,0.03636363636363636,0.03636363636363636,0.0,0.38181818181818183,7,False
+Jimmy Jackson,United States,"[1950, 1954]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Joe James,United States,"[1951, 1952]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+John James,United Kingdom,[1951],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jean-Pierre Jarier,France,"[1971, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983]",0.0,143.0,135.0,3.0,0.0,3.0,3.0,31.5,False,,1980,0.02097902097902098,0.9440559440559441,0.0,0.02097902097902098,0.02097902097902098,0.2202797202797203,12,False
+Max Jean[w],France,[1971],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Stefan Johansson,Sweden,"[1980, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991]",0.0,103.0,79.0,0.0,0.0,12.0,0.0,88.0,False,,1990,0.0,0.7669902912621359,0.0,0.11650485436893204,0.0,0.8543689320388349,10,False
+Eddie Johnson,United States,"[1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,9.0,9.0,0.0,0.0,0.0,0.0,1.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.1111111111111111,9,False
+Leslie Johnson,United Kingdom,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bruce Johnstone,South Africa,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Alan Jones,Australia,"[1975, 1976, 1977, 1978, 1979, 1980, 1981, 1983, 1985, 1986]",1.0,117.0,116.0,6.0,12.0,24.0,13.0,199.0,False,[1980],1980,0.05128205128205128,0.9914529914529915,0.10256410256410256,0.20512820512820512,0.1111111111111111,1.7008547008547008,10,True
+Tom Jones,United States,[1967],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Juan Jover,Spain,[1951],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Oswald Karch,West Germany,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Narain Karthikeyan,India,"[2005, 2011, 2012]",0.0,48.0,46.0,0.0,0.0,0.0,0.0,5.0,False,,2010,0.0,0.9583333333333334,0.0,0.0,0.0,0.10416666666666667,3,False
+Ukyo Katayama,Japan,"[1992, 1993, 1994, 1995, 1996, 1997]",0.0,97.0,95.0,0.0,0.0,0.0,0.0,5.0,False,,1990,0.0,0.979381443298969,0.0,0.0,0.0,0.05154639175257732,6,False
+Ken Kavanagh,Australia,[1958],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Rupert Keegan,United Kingdom,"[1977, 1978, 1980, 1982]",0.0,37.0,25.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6756756756756757,0.0,0.0,0.0,0.0,4,False
+Eddie Keizan,South Africa,"[1973, 1974, 1975]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Al Keller,United States,"[1955, 1956, 1957, 1958, 1959]",0.0,6.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,5,False
+Joe Kelly,Ireland,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+David Kennedy,Ireland,[1980],0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Loris Kessel,Switzerland,"[1976, 1977]",0.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Bruce Kessler,United States,[1958],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Nicolas Kiesa,Denmark,[2003],0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Leo Kinnunen,Finland,[1974],0.0,6.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,1,False
+Danny Kladis,United States,[1954],0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.2,0.0,0.0,0.0,0.0,1,False
+Hans Klenk,West Germany,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Peter de Klerk,South Africa,"[1963, 1965, 1969, 1970]",0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,4,False
+Christian Klien,Austria,"[2004, 2005, 2006, 2010]",0.0,51.0,49.0,0.0,0.0,0.0,0.0,14.0,False,,2010,0.0,0.9607843137254902,0.0,0.0,0.0,0.27450980392156865,4,False
+Karl Kling,West Germany,"[1954, 1955]",0.0,11.0,11.0,0.0,0.0,2.0,1.0,17.0,False,,1950,0.0,1.0,0.0,0.18181818181818182,0.09090909090909091,1.5454545454545454,2,False
+Ernst Klodwig,East Germany,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Kamui Kobayashi,Japan,"[2009, 2010, 2011, 2012, 2014]",0.0,76.0,75.0,0.0,0.0,1.0,1.0,125.0,False,,2010,0.0,0.9868421052631579,0.0,0.013157894736842105,0.013157894736842105,1.644736842105263,5,False
+Helmuth Koinigg,Austria,[1974],0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,1,False
+Heikki Kovalainen,Finland,"[2007, 2008, 2009, 2010, 2011, 2012, 2013]",0.0,112.0,111.0,1.0,1.0,4.0,2.0,105.0,False,,2010,0.008928571428571428,0.9910714285714286,0.008928571428571428,0.03571428571428571,0.017857142857142856,0.9375,7,False
+Mikko Kozarowitzky,Finland,[1977],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Willi Krakau,West Germany,[1952],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Rudolf Krause,East Germany,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Robert Kubica,Poland,"[2006, 2007, 2008, 2009, 2010, 2019, 2021]",0.0,99.0,99.0,1.0,1.0,12.0,1.0,274.0,False,,2010,0.010101010101010102,1.0,0.010101010101010102,0.12121212121212122,0.010101010101010102,2.7676767676767677,7,False
+Kurt Kuhnke,West Germany,[1963],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Masami Kuwashima,Japan,[1976],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Daniil Kvyat,Russia,"[2014, 2015, 2016, 2017, 2019, 2020]",0.0,112.0,110.0,0.0,0.0,3.0,1.0,202.0,False,,2020,0.0,0.9821428571428571,0.0,0.026785714285714284,0.008928571428571428,1.8035714285714286,6,False
+Robert La Caze,Morocco,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jacques Laffite,France,"[1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986]",0.0,180.0,176.0,7.0,6.0,32.0,7.0,228.0,False,,1980,0.03888888888888889,0.9777777777777777,0.03333333333333333,0.17777777777777778,0.03888888888888889,1.2666666666666666,13,False
+Franck Lagorce,France,[1994],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jan Lammers,Netherlands,"[1979, 1980, 1981, 1982, 1992]",0.0,41.0,23.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5609756097560976,0.0,0.0,0.0,0.0,5,False
+Pedro Lamy,Portugal,"[1993, 1994, 1995, 1996]",0.0,32.0,32.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.03125,4,False
+Chico Landi,Brazil,"[1951, 1952, 1953, 1956]",0.0,6.0,6.0,0.0,0.0,0.0,0.0,1.5,False,,1950,0.0,1.0,0.0,0.0,0.0,0.25,4,False
+Hermann Lang,West Germany,"[1953, 1954]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,1.0,0.0,0.0,0.0,1.0,2,False
+Claudio Langes,Italy,[1990],0.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Nicola Larini,Italy,"[1987, 1988, 1989, 1990, 1991, 1992, 1994, 1997]",0.0,75.0,49.0,0.0,0.0,1.0,0.0,7.0,False,,1990,0.0,0.6533333333333333,0.0,0.013333333333333334,0.0,0.09333333333333334,8,False
+Oscar Larrauri,Argentina,"[1988, 1989]",0.0,21.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.38095238095238093,0.0,0.0,0.0,0.0,2,False
+Gérard Larrousse,France,[1974],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jud Larson,United States,"[1958, 1959]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Nicholas Latifi,Canada,"[2020, 2021, 2022]",0.0,61.0,61.0,0.0,0.0,0.0,0.0,9.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.14754098360655737,3,False
+Niki Lauda,Austria,"[1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1982, 1983, 1984, 1985]",3.0,177.0,171.0,24.0,25.0,54.0,24.0,420.5,False,"[1975, 1977, 1984]",1980,0.13559322033898305,0.9661016949152542,0.14124293785310735,0.3050847457627119,0.13559322033898305,2.3757062146892656,13,True
+Roger Laurent,Belgium,[1952],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Giovanni Lavaggi,Italy,"[1995, 1996]",0.0,10.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.7,0.0,0.0,0.0,0.0,2,False
+Chris Lawrence,United Kingdom,[1966],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Charles Leclerc,Monaco,"[2018, 2019, 2020, 2021, 2022]",0.0,104.0,103.0,18.0,5.0,24.0,7.0,868.0,True,,2020,0.17307692307692307,0.9903846153846154,0.04807692307692308,0.23076923076923078,0.0673076923076923,8.346153846153847,5,False
+Michel Leclère,France,"[1975, 1976]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.875,0.0,0.0,0.0,0.0,2,False
+Neville Lederle,South Africa,"[1962, 1965]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,1.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.5,2,False
+Geoff Lees,United Kingdom,"[1978, 1979, 1980, 1982]",0.0,12.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.4166666666666667,0.0,0.0,0.0,0.0,4,False
+Gijs van Lennep,Netherlands,"[1971, 1973, 1974, 1975]",0.0,10.0,8.0,0.0,0.0,0.0,0.0,2.0,False,,1970,0.0,0.8,0.0,0.0,0.0,0.2,4,False
+Arthur Legat,Belgium,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+JJ Lehto,Finland,"[1989, 1990, 1991, 1992, 1993, 1994]",0.0,70.0,62.0,0.0,0.0,1.0,0.0,10.0,False,,1990,0.0,0.8857142857142857,0.0,0.014285714285714285,0.0,0.14285714285714285,6,False
+Lamberto Leoni,Italy,"[1977, 1978]",0.0,5.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.2,0.0,0.0,0.0,0.0,2,False
+Les Leston,United Kingdom,"[1956, 1957]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Pierre Levegh,France,"[1950, 1951]",0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Bayliss Levrett,United States,[1950],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Jackie Lewis,United Kingdom,"[1961, 1962]",0.0,10.0,9.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,0.9,0.0,0.0,0.0,0.3,2,False
+Stuart Lewis-Evans,United Kingdom,"[1957, 1958]",0.0,14.0,14.0,2.0,0.0,2.0,0.0,16.0,False,,1960,0.14285714285714285,1.0,0.0,0.14285714285714285,0.0,1.1428571428571428,2,False
+Guy Ligier,France,"[1966, 1967]",0.0,13.0,12.0,0.0,0.0,0.0,0.0,1.0,False,,1970,0.0,0.9230769230769231,0.0,0.0,0.0,0.07692307692307693,2,False
+Andy Linden,United States,"[1951, 1952, 1953, 1954, 1955, 1956, 1957]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,5.0,False,,1950,0.0,0.875,0.0,0.0,0.0,0.625,7,False
+Roberto Lippi,Italy,"[1961, 1962, 1963]",0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,3,False
+Vitantonio Liuzzi,Italy,"[2005, 2006, 2007, 2009, 2010, 2011]",0.0,81.0,80.0,0.0,0.0,0.0,0.0,26.0,False,,2010,0.0,0.9876543209876543,0.0,0.0,0.0,0.32098765432098764,6,False
+Dries van der Lof,Netherlands,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Lella Lombardi,Italy,"[1974, 1975, 1976]",0.0,17.0,12.0,0.0,0.0,0.0,0.0,0.5,False,,1980,0.0,0.7058823529411765,0.0,0.0,0.0,0.029411764705882353,3,False
+Ricardo Londoño,Colombia,[1981],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Ernst Loof,West Germany,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+André Lotterer,Germany,[2014],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Henri Louveau,France,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+John Love,Rhodesia,"[1962, 1963, 1964, 1965, 1967, 1968, 1969, 1970, 1971, 1972]",0.0,10.0,9.0,0.0,0.0,1.0,0.0,6.0,False,,1970,0.0,0.9,0.0,0.1,0.0,0.6,10,False
+Pete Lovely,United States,"[1959, 1960, 1969, 1970, 1971]",0.0,11.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6363636363636364,0.0,0.0,0.0,0.0,5,False
+Roger Loyer,France,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jean Lucas,France,[1955],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jean Lucienbonnet,France,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Erik Lundgren,Sweden,[1951],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Brett Lunger,United States,"[1975, 1976, 1977, 1978]",0.0,43.0,34.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.7906976744186046,0.0,0.0,0.0,0.0,4,False
+Mike MacDowel,United Kingdom,[1957],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Herbert MacKay-Fraser,United States,[1957],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bill Mackey,United States,[1951],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Lance Macklin,United Kingdom,"[1952, 1953, 1954, 1955]",0.0,15.0,13.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.8666666666666667,0.0,0.0,0.0,0.0,4,False
+Damien Magee,United Kingdom,"[1975, 1976]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Tony Maggs,South Africa,"[1961, 1962, 1963, 1964, 1965]",0.0,27.0,25.0,0.0,0.0,3.0,0.0,26.0,False,,1960,0.0,0.9259259259259259,0.0,0.1111111111111111,0.0,0.9629629629629629,5,False
+Mike Magill,United States,"[1957, 1958, 1959]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.75,0.0,0.0,0.0,0.0,3,False
+Umberto Maglioli,Italy,"[1953, 1954, 1955, 1956, 1957]",0.0,10.0,10.0,0.0,0.0,2.0,0.0,3.33,False,,1960,0.0,1.0,0.0,0.2,0.0,0.333,5,False
+Jan Magnussen,Denmark,"[1995, 1997, 1998]",0.0,25.0,24.0,0.0,0.0,0.0,0.0,1.0,False,,2000,0.0,0.96,0.0,0.0,0.0,0.04,3,False
+Kevin Magnussen,Denmark,"[2014, 2015, 2016, 2017, 2018, 2019, 2020, 2022]",0.0,143.0,142.0,1.0,0.0,1.0,2.0,183.0,True,,2020,0.006993006993006993,0.993006993006993,0.0,0.006993006993006993,0.013986013986013986,1.2797202797202798,8,False
+Guy Mairesse,France,"[1950, 1951]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Willy Mairesse,Belgium,"[1960, 1961, 1962, 1963, 1965]",0.0,13.0,12.0,0.0,0.0,1.0,0.0,7.0,False,,1960,0.0,0.9230769230769231,0.0,0.07692307692307693,0.0,0.5384615384615384,5,False
+Pastor Maldonado,Venezuela,"[2011, 2012, 2013, 2014, 2015]",0.0,96.0,95.0,1.0,1.0,1.0,0.0,76.0,False,,2010,0.010416666666666666,0.9895833333333334,0.010416666666666666,0.010416666666666666,0.0,0.7916666666666666,5,False
+Nigel Mansell,United Kingdom,"[1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1994, 1995]",1.0,191.0,187.0,32.0,31.0,59.0,30.0,480.0,False,[1992],1990,0.16753926701570682,0.9790575916230366,0.16230366492146597,0.3089005235602094,0.15706806282722513,2.513089005235602,15,True
+Sergio Mantovani,Italy,"[1953, 1954, 1955]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,4.0,False,,1950,0.0,0.875,0.0,0.0,0.0,0.5,3,False
+Johnny Mantz,United States,[1953],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Robert Manzon,France,"[1950, 1951, 1952, 1953, 1954, 1955, 1956]",0.0,29.0,28.0,0.0,0.0,2.0,0.0,16.0,False,,1950,0.0,0.9655172413793104,0.0,0.06896551724137931,0.0,0.5517241379310345,7,False
+Onofre Marimón,Argentina,"[1951, 1953, 1954]",0.0,12.0,11.0,0.0,0.0,2.0,1.0,8.14,False,,1950,0.0,0.9166666666666666,0.0,0.16666666666666666,0.08333333333333333,0.6783333333333333,3,False
+Helmut Marko,Austria,"[1971, 1972]",0.0,10.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Tarso Marques,Brazil,"[1996, 1997, 2001]",0.0,26.0,24.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.9230769230769231,0.0,0.0,0.0,0.0,3,False
+Leslie Marr,United Kingdom,"[1954, 1955]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Tony Marsh,United Kingdom,"[1957, 1958, 1961]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,3,False
+Eugène Martin,France,[1950],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Pierluigi Martini,Italy,"[1984, 1985, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995]",0.0,124.0,118.0,0.0,0.0,0.0,0.0,18.0,False,,1990,0.0,0.9516129032258065,0.0,0.0,0.0,0.14516129032258066,10,False
+Jochen Mass,West Germany,"[1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1982]",0.0,114.0,105.0,0.0,1.0,8.0,2.0,71.0,False,,1980,0.0,0.9210526315789473,0.008771929824561403,0.07017543859649122,0.017543859649122806,0.6228070175438597,9,False
+Felipe Massa,Brazil,"[2002, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017]",0.0,272.0,269.0,16.0,11.0,41.0,15.0,1167.0,False,,2010,0.058823529411764705,0.9889705882352942,0.04044117647058824,0.15073529411764705,0.05514705882352941,4.290441176470588,15,False
+Cristiano da Matta,Brazil,"[2003, 2004]",0.0,28.0,28.0,0.0,0.0,0.0,0.0,13.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.4642857142857143,2,False
+Michael May,Switzerland,[1961],0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,1,False
+Timmy Mayer,United States,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Nikita Mazepin,RAF,[2021],0.0,22.0,21.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,0.9545454545454546,0.0,0.0,0.0,0.0,1,False
+François Mazet,France,[1971],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Gastón Mazzacane,Argentina,"[2000, 2001]",0.0,21.0,21.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Kenneth McAlpine,United Kingdom,"[1952, 1953, 1955]",0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Perry McCarthy,United Kingdom,[1992],0.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Ernie McCoy,United States,"[1953, 1954]",0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,2,False
+Johnny McDowell,United States,"[1950, 1951, 1952]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Jack McGrath,United States,"[1950, 1951, 1952, 1953, 1954, 1955]",0.0,6.0,6.0,1.0,0.0,2.0,1.0,9.0,False,,1950,0.16666666666666666,1.0,0.0,0.3333333333333333,0.16666666666666666,1.5,6,False
+Brian McGuire,Australia,[1977],0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Bruce McLaren,New Zealand,"[1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970]",0.0,104.0,100.0,0.0,4.0,27.0,3.0,188.5,False,,1960,0.0,0.9615384615384616,0.038461538461538464,0.25961538461538464,0.028846153846153848,1.8125,13,False
+Allan McNish,United Kingdom,[2002],0.0,17.0,16.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.9411764705882353,0.0,0.0,0.0,0.0,1,False
+Graham McRae,New Zealand,[1973],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jim McWithey,United States,"[1959, 1960]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Carlos Menditeguy,Argentina,"[1953, 1954, 1955, 1956, 1957, 1958, 1960]",0.0,11.0,10.0,0.0,0.0,1.0,0.0,9.0,False,,1960,0.0,0.9090909090909091,0.0,0.09090909090909091,0.0,0.8181818181818182,7,False
+Roberto Merhi,Spain,[2015],0.0,14.0,13.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,0.9285714285714286,0.0,0.0,0.0,0.0,1,False
+Harry Merkel,West Germany,[1952],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Arturo Merzario,Italy,"[1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979]",0.0,85.0,57.0,0.0,0.0,0.0,0.0,11.0,False,,1980,0.0,0.6705882352941176,0.0,0.0,0.0,0.12941176470588237,8,False
+Roberto Mieres,Argentina,"[1953, 1954, 1955]",0.0,17.0,17.0,0.0,0.0,0.0,1.0,13.0,False,,1950,0.0,1.0,0.0,0.0,0.058823529411764705,0.7647058823529411,3,False
+François Migault,France,"[1972, 1974, 1975]",0.0,16.0,13.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.8125,0.0,0.0,0.0,0.0,3,False
+John Miles,United Kingdom,"[1969, 1970]",0.0,15.0,12.0,0.0,0.0,0.0,0.0,2.0,False,,1970,0.0,0.8,0.0,0.0,0.0,0.13333333333333333,2,False
+Ken Miles,United Kingdom,[1961],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+André Milhoux,Belgium,[1956],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Chet Miller,United States,"[1951, 1952]",0.0,4.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Gerhard Mitter,West Germany,"[1963, 1964, 1965]",0.0,7.0,5.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,0.7142857142857143,0.0,0.0,0.0,0.42857142857142855,3,False
+Stefano Modena,Italy,"[1987, 1988, 1989, 1990, 1991, 1992]",0.0,81.0,70.0,0.0,0.0,2.0,0.0,17.0,False,,1990,0.0,0.8641975308641975,0.0,0.024691358024691357,0.0,0.20987654320987653,6,False
+Thomas Monarch,United States,[1963],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Franck Montagny,France,[2006],0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Tiago Monteiro,Portugal,"[2005, 2006]",0.0,37.0,37.0,0.0,0.0,1.0,0.0,7.0,False,,2010,0.0,1.0,0.0,0.02702702702702703,0.0,0.1891891891891892,2,False
+Andrea Montermini,Italy,"[1994, 1995, 1996]",0.0,29.0,19.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.6551724137931034,0.0,0.0,0.0,0.0,3,False
+Peter Monteverdi,Switzerland,[1961],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Robin Montgomerie-Charrington,United Kingdom,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Juan Pablo Montoya,Colombia,"[2001, 2002, 2003, 2004, 2005, 2006]",0.0,95.0,94.0,13.0,7.0,30.0,12.0,307.0,False,,2000,0.1368421052631579,0.9894736842105263,0.07368421052631578,0.3157894736842105,0.12631578947368421,3.231578947368421,6,False
+Gianni Morbidelli,Italy,"[1990, 1991, 1992, 1994, 1995, 1997]",0.0,70.0,67.0,0.0,0.0,1.0,0.0,8.5,False,,1990,0.0,0.9571428571428572,0.0,0.014285714285714285,0.0,0.12142857142857143,6,False
+Roberto Moreno,Brazil,"[1982, 1987, 1989, 1990, 1991, 1992, 1995]",0.0,77.0,41.0,0.0,0.0,1.0,1.0,15.0,False,,1990,0.0,0.5324675324675324,0.0,0.012987012987012988,0.012987012987012988,0.19480519480519481,7,False
+Dave Morgan,United Kingdom,[1975],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Silvio Moser,Switzerland,"[1967, 1968, 1969, 1970, 1971]",0.0,20.0,12.0,0.0,0.0,0.0,0.0,3.0,False,,1970,0.0,0.6,0.0,0.0,0.0,0.15,5,False
+Bill Moss,United Kingdom,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Stirling Moss,United Kingdom,"[1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961]",0.0,67.0,66.0,16.0,16.0,24.0,19.0,185.64,False,,1960,0.23880597014925373,0.9850746268656716,0.23880597014925373,0.3582089552238806,0.2835820895522388,2.7707462686567164,11,False
+Gino Munaron,Italy,[1960],0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+David Murray,United Kingdom,"[1950, 1951, 1952]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.8,0.0,0.0,0.0,0.0,3,False
+Luigi Musso,Italy,"[1953, 1954, 1955, 1956, 1957, 1958]",0.0,25.0,24.0,0.0,1.0,7.0,1.0,44.0,False,,1960,0.0,0.96,0.04,0.28,0.04,1.76,6,False
+Kazuki Nakajima,Japan,"[2007, 2008, 2009]",0.0,36.0,36.0,0.0,0.0,0.0,0.0,9.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.25,3,False
+Satoru Nakajima,Japan,"[1987, 1988, 1989, 1990, 1991]",0.0,80.0,74.0,0.0,0.0,0.0,1.0,16.0,False,,1990,0.0,0.925,0.0,0.0,0.0125,0.2,5,False
+Shinji Nakano,Japan,"[1997, 1998]",0.0,33.0,33.0,0.0,0.0,0.0,0.0,2.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.06060606060606061,2,False
+Duke Nalon,United States,"[1951, 1952, 1953]",0.0,5.0,3.0,1.0,0.0,0.0,0.0,0.0,False,,1950,0.2,0.6,0.0,0.0,0.0,0.0,3,False
+Alessandro Nannini,Italy,"[1986, 1987, 1988, 1989, 1990]",0.0,78.0,76.0,0.0,1.0,9.0,2.0,65.0,False,,1990,0.0,0.9743589743589743,0.01282051282051282,0.11538461538461539,0.02564102564102564,0.8333333333333334,5,False
+Emanuele Naspetti,Italy,"[1992, 1993]",0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Felipe Nasr,Brazil,"[2015, 2016]",0.0,40.0,39.0,0.0,0.0,0.0,0.0,29.0,False,,2020,0.0,0.975,0.0,0.0,0.0,0.725,2,False
+Massimo Natili,Italy,[1961],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Brian Naylor,United Kingdom,"[1957, 1958, 1959, 1960, 1961]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.875,0.0,0.0,0.0,0.0,5,False
+Mike Nazaruk,United States,"[1951, 1953, 1954]",0.0,4.0,3.0,0.0,0.0,1.0,0.0,8.0,False,,1950,0.0,0.75,0.0,0.25,0.0,2.0,3,False
+Tiff Needell,United Kingdom,[1980],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jac Nellemann,Denmark,[1976],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Patrick Nève,Belgium,"[1976, 1977, 1978]",0.0,14.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.7142857142857143,0.0,0.0,0.0,0.0,3,False
+John Nicholson,New Zealand,"[1974, 1975]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Cal Niday,United States,"[1953, 1954, 1955]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Helmut Niedermayr,West Germany,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Brausch Niemann,South Africa,"[1963, 1965]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Gunnar Nilsson,Sweden,"[1976, 1977]",0.0,32.0,31.0,0.0,1.0,4.0,1.0,31.0,False,,1980,0.0,0.96875,0.03125,0.125,0.03125,0.96875,2,False
+Hideki Noda,Japan,[1994],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Lando Norris,United Kingdom,"[2019, 2020, 2021, 2022]",0.0,83.0,83.0,1.0,0.0,6.0,5.0,428.0,True,,2020,0.012048192771084338,1.0,0.0,0.07228915662650602,0.060240963855421686,5.156626506024097,4,False
+Rodney Nuckey,United Kingdom,[1953],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Robert O'Brien,United States,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Esteban Ocon,France,"[2016, 2017, 2018, 2020, 2021, 2022]",0.0,112.0,112.0,0.0,1.0,2.0,0.0,364.0,True,,2020,0.0,1.0,0.008928571428571428,0.017857142857142856,0.0,3.25,6,False
+Pat O'Connor,United States,"[1954, 1955, 1956, 1957, 1958]",0.0,6.0,5.0,1.0,0.0,0.0,0.0,0.0,False,,1960,0.16666666666666666,0.8333333333333334,0.0,0.0,0.0,0.0,5,False
+Casimiro de Oliveira,Portugal,[1958],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Jackie Oliver,United Kingdom,"[1968, 1969, 1970, 1971, 1972, 1973, 1977]",0.0,52.0,50.0,0.0,0.0,2.0,1.0,13.0,False,,1970,0.0,0.9615384615384616,0.0,0.038461538461538464,0.019230769230769232,0.25,7,False
+Danny Ongais,United States,"[1977, 1978]",0.0,6.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Rikky von Opel,Liechtenstein,"[1973, 1974]",0.0,14.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.7142857142857143,0.0,0.0,0.0,0.0,2,False
+Karl Oppitzhauser,Austria,[1976],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Fritz d'Orey,Brazil,[1959],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Arthur Owen,United Kingdom,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Carlos Pace,Brazil,"[1972, 1973, 1974, 1975, 1976, 1977]",0.0,73.0,72.0,1.0,1.0,6.0,5.0,58.0,False,,1970,0.0136986301369863,0.9863013698630136,0.0136986301369863,0.0821917808219178,0.0684931506849315,0.7945205479452054,6,False
+Nello Pagani,Italy,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Riccardo Paletti,Italy,[1982],0.0,8.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.25,0.0,0.0,0.0,0.0,1,False
+Torsten Palm,Sweden,[1975],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jolyon Palmer,United Kingdom,"[2016, 2017]",0.0,37.0,35.0,0.0,0.0,0.0,0.0,9.0,False,,2020,0.0,0.9459459459459459,0.0,0.0,0.0,0.24324324324324326,2,False
+Jonathan Palmer,United Kingdom,"[1983, 1984, 1985, 1986, 1987, 1988, 1989]",0.0,88.0,83.0,0.0,0.0,0.0,1.0,14.0,False,,1990,0.0,0.9431818181818182,0.0,0.0,0.011363636363636364,0.1590909090909091,7,False
+Olivier Panis,France,"[1994, 1995, 1996, 1997, 1998, 1999, 2001, 2002, 2003, 2004]",0.0,158.0,157.0,0.0,1.0,5.0,0.0,76.0,False,,2000,0.0,0.9936708860759493,0.006329113924050633,0.03164556962025317,0.0,0.4810126582278481,10,False
+Giorgio Pantano,Italy,[2004],0.0,15.0,14.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.9333333333333333,0.0,0.0,0.0,0.0,1,False
+Massimiliano Papis,Italy,[1995],0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Mike Parkes,United Kingdom,"[1959, 1966, 1967]",0.0,7.0,6.0,1.0,0.0,2.0,0.0,14.0,False,,1960,0.14285714285714285,0.8571428571428571,0.0,0.2857142857142857,0.0,2.0,3,False
+Reg Parnell,United Kingdom,"[1950, 1951, 1952, 1954]",0.0,7.0,6.0,0.0,0.0,1.0,0.0,9.0,False,,1950,0.0,0.8571428571428571,0.0,0.14285714285714285,0.0,1.2857142857142858,4,False
+Tim Parnell,United Kingdom,"[1959, 1961, 1963]",0.0,4.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,3,False
+Johnnie Parsons,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958]",0.0,9.0,9.0,0.0,1.0,1.0,1.0,12.0,False,,1950,0.0,1.0,0.1111111111111111,0.1111111111111111,0.1111111111111111,1.3333333333333333,9,False
+Riccardo Patrese,Italy,"[1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993]",0.0,257.0,256.0,8.0,6.0,37.0,13.0,281.0,False,,1980,0.0311284046692607,0.9961089494163424,0.023346303501945526,0.14396887159533073,0.05058365758754864,1.093385214007782,17,False
+Al Pease,Canada,"[1967, 1968, 1969]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,3,False
+Roger Penske,United States,"[1961, 1962]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Cesare Perdisa,Italy,"[1955, 1956, 1957]",0.0,8.0,8.0,0.0,0.0,2.0,0.0,5.0,False,,1960,0.0,1.0,0.0,0.25,0.0,0.625,3,False
+Sergio Pérez,Mexico,"[2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",0.0,240.0,236.0,1.0,4.0,27.0,9.0,1219.0,True,,2020,0.004166666666666667,0.9833333333333333,0.016666666666666666,0.1125,0.0375,5.079166666666667,12,False
+Luis Pérez-Sala,Spain,"[1988, 1989]",0.0,32.0,26.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,0.8125,0.0,0.0,0.0,0.03125,2,False
+Larry Perkins,Australia,"[1974, 1976, 1977]",0.0,15.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.7333333333333333,0.0,0.0,0.0,0.0,3,False
+Henri Pescarolo,France,"[1968, 1970, 1971, 1972, 1973, 1974, 1976]",0.0,64.0,57.0,0.0,0.0,1.0,1.0,12.0,False,,1970,0.0,0.890625,0.0,0.015625,0.015625,0.1875,7,False
+Alessandro Pesenti-Rossi,Italy,[1976],0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.75,0.0,0.0,0.0,0.0,1,False
+Josef Peters,West Germany,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ronnie Peterson,Sweden,"[1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978]",0.0,123.0,123.0,14.0,10.0,26.0,9.0,206.0,False,,1970,0.11382113821138211,1.0,0.08130081300813008,0.21138211382113822,0.07317073170731707,1.6747967479674797,9,False
+Vitaly Petrov,Russia,"[2010, 2011, 2012]",0.0,58.0,57.0,0.0,0.0,1.0,1.0,64.0,False,,2010,0.0,0.9827586206896551,0.0,0.017241379310344827,0.017241379310344827,1.103448275862069,3,False
+Alfredo Pián,Argentina,[1950],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Oscar Piastri,Australia,[2023],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,True,,2020,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Charles Pic,France,"[2012, 2013]",0.0,39.0,39.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+François Picard,France,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ernie Pieterse,South Africa,"[1962, 1963, 1965]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,3,False
+Paul Pietsch,West Germany,"[1950, 1951, 1952]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+André Pilette,Belgium,"[1951, 1953, 1954, 1956, 1961, 1963, 1964]",0.0,14.0,9.0,0.0,0.0,0.0,0.0,2.0,False,,1960,0.0,0.6428571428571429,0.0,0.0,0.0,0.14285714285714285,7,False
+Teddy Pilette,Belgium,"[1974, 1977]",0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.25,0.0,0.0,0.0,0.0,2,False
+Luigi Piotti,Italy,"[1955, 1956, 1957, 1958]",0.0,8.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.625,0.0,0.0,0.0,0.0,4,False
+David Piper,United Kingdom,"[1959, 1960]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Nelson Piquet,Brazil,"[1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991]",3.0,207.0,204.0,24.0,23.0,60.0,23.0,481.5,False,"[1981, 1983, 1987]",1980,0.11594202898550725,0.9855072463768116,0.1111111111111111,0.2898550724637681,0.1111111111111111,2.3260869565217392,14,True
+Nelson Piquet Jr.,Brazil,"[2008, 2009]",0.0,28.0,28.0,0.0,0.0,1.0,0.0,19.0,False,,2010,0.0,1.0,0.0,0.03571428571428571,0.0,0.6785714285714286,2,False
+Renato Pirocchi,Italy,[1961],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Didier Pironi,France,"[1978, 1979, 1980, 1981, 1982]",0.0,72.0,70.0,4.0,3.0,13.0,5.0,101.0,False,,1980,0.05555555555555555,0.9722222222222222,0.041666666666666664,0.18055555555555555,0.06944444444444445,1.4027777777777777,5,False
+Emanuele Pirro,Italy,"[1989, 1990, 1991]",0.0,40.0,37.0,0.0,0.0,0.0,0.0,3.0,False,,1990,0.0,0.925,0.0,0.0,0.0,0.075,3,False
+Antônio Pizzonia,Brazil,"[2003, 2004, 2005]",0.0,20.0,20.0,0.0,0.0,0.0,0.0,8.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.4,3,False
+Eric van de Poele,Belgium,"[1991, 1992]",0.0,29.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.1724137931034483,0.0,0.0,0.0,0.0,2,False
+Jacques Pollet,France,"[1954, 1955]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Ben Pon,Netherlands,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Dennis Poore,United Kingdom,[1952],0.0,2.0,2.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,1.0,0.0,0.0,0.0,1.5,1,False
+Alfonso de Portago,Spain,"[1956, 1957]",0.0,5.0,5.0,0.0,0.0,1.0,0.0,4.0,False,,1960,0.0,1.0,0.0,0.2,0.0,0.8,2,False
+Sam Posey,United States,"[1971, 1972]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Charles Pozzi,France,[1950],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jackie Pretorius,South Africa,"[1965, 1968, 1971, 1973]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.75,0.0,0.0,0.0,0.0,4,False
+Ernesto Prinoth,Italy,[1962],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+David Prophet,United Kingdom,"[1963, 1965]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Alain Prost,France,"[1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1993]",4.0,202.0,199.0,33.0,51.0,106.0,41.0,768.5,False,"[1985, 1986, 1989, 1993]",1990,0.16336633663366337,0.9851485148514851,0.2524752475247525,0.5247524752475248,0.20297029702970298,3.8044554455445545,13,True
+Tom Pryce,United Kingdom,"[1974, 1975, 1976, 1977]",0.0,42.0,42.0,1.0,0.0,2.0,0.0,19.0,False,,1980,0.023809523809523808,1.0,0.0,0.047619047619047616,0.0,0.4523809523809524,4,False
+David Purley,United Kingdom,"[1973, 1974, 1977]",0.0,11.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6363636363636364,0.0,0.0,0.0,0.0,3,False
+Clive Puzey,Rhodesia,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Dieter Quester,Austria,"[1969, 1974]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Ian Raby,United Kingdom,"[1963, 1964, 1965]",0.0,7.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.42857142857142855,0.0,0.0,0.0,0.0,3,False
+Bobby Rahal,United States,[1978],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Kimi Räikkönen,Finland,"[2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021]",1.0,353.0,349.0,18.0,21.0,103.0,46.0,1873.0,False,[2007],2010,0.05099150141643059,0.9886685552407932,0.059490084985835696,0.29178470254957506,0.13031161473087818,5.305949008498583,19,True
+Hermano da Silva Ramos,Brazil,"[1955, 1956]",0.0,7.0,7.0,0.0,0.0,0.0,0.0,2.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.2857142857142857,2,False
+Pierre-Henri Raphanel,France,"[1988, 1989]",0.0,17.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.058823529411764705,0.0,0.0,0.0,0.0,2,False
+Dick Rathmann,United States,"[1950, 1956, 1958, 1959, 1960]",0.0,6.0,5.0,1.0,0.0,0.0,0.0,2.0,False,,1960,0.16666666666666666,0.8333333333333334,0.0,0.0,0.0,0.3333333333333333,5,False
+Jim Rathmann,United States,"[1950, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,10.0,10.0,0.0,1.0,4.0,2.0,29.0,False,,1960,0.0,1.0,0.1,0.4,0.2,2.9,10,False
+Roland Ratzenberger,Austria,[1994],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Héctor Rebaque,Mexico,"[1977, 1978, 1979, 1980, 1981]",0.0,58.0,41.0,0.0,0.0,0.0,0.0,13.0,False,,1980,0.0,0.7068965517241379,0.0,0.0,0.0,0.22413793103448276,5,False
+Brian Redman,United Kingdom,"[1968, 1970, 1971, 1972, 1973, 1974]",0.0,15.0,12.0,0.0,0.0,1.0,0.0,8.0,False,,1970,0.0,0.8,0.0,0.06666666666666667,0.0,0.5333333333333333,6,False
+Jimmy Reece,United States,"[1952, 1954, 1955, 1956, 1957, 1958]",0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,6,False
+Ray Reed,Rhodesia,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Alan Rees,United Kingdom,[1967],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Clay Regazzoni,Switzerland,"[1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980]",0.0,139.0,132.0,5.0,5.0,28.0,15.0,209.0,False,,1980,0.03597122302158273,0.9496402877697842,0.03597122302158273,0.2014388489208633,0.1079136690647482,1.5035971223021583,11,False
+Paul di Resta,United Kingdom,"[2011, 2012, 2013, 2017]",0.0,59.0,59.0,0.0,0.0,0.0,0.0,121.0,False,,2010,0.0,1.0,0.0,0.0,0.0,2.0508474576271185,4,False
+Carlos Reutemann,Argentina,"[1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982]",0.0,146.0,146.0,6.0,12.0,45.0,6.0,298.0,False,,1980,0.0410958904109589,1.0,0.0821917808219178,0.3082191780821918,0.0410958904109589,2.041095890410959,11,False
+Lance Reventlow,United States,[1960],0.0,4.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.25,0.0,0.0,0.0,0.0,1,False
+Peter Revson,United States,"[1964, 1971, 1972, 1973, 1974]",0.0,32.0,30.0,1.0,2.0,8.0,0.0,61.0,False,,1970,0.03125,0.9375,0.0625,0.25,0.0,1.90625,5,False
+John Rhodes,United Kingdom,[1965],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Alex Ribeiro,Brazil,"[1976, 1977, 1979]",0.0,20.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,3,False
+Daniel Ricciardo,Australia,"[2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",0.0,232.0,232.0,3.0,8.0,32.0,16.0,1311.0,False,,2020,0.01293103448275862,1.0,0.034482758620689655,0.13793103448275862,0.06896551724137931,5.650862068965517,12,False
+Ken Richardson,United Kingdom,[1951],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Fritz Riess,West Germany,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jim Rigsby,United States,[1952],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jochen Rindt,Austria,"[1964, 1965, 1966, 1967, 1968, 1969, 1970]",1.0,62.0,60.0,10.0,6.0,13.0,3.0,107.0,False,[1970],1970,0.16129032258064516,0.967741935483871,0.0967741935483871,0.20967741935483872,0.04838709677419355,1.7258064516129032,7,True
+John Riseley-Prichard,United Kingdom,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Giovanni de Riu,Italy,[1954],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Richard Robarts,United Kingdom,[1974],0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.75,0.0,0.0,0.0,0.0,1,False
+Pedro Rodríguez,Mexico,"[1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971]",0.0,54.0,54.0,0.0,2.0,7.0,1.0,71.0,False,,1970,0.0,1.0,0.037037037037037035,0.12962962962962962,0.018518518518518517,1.3148148148148149,9,False
+Ricardo Rodríguez,Mexico,"[1961, 1962]",0.0,6.0,5.0,0.0,0.0,0.0,0.0,4.0,False,,1960,0.0,0.8333333333333334,0.0,0.0,0.0,0.6666666666666666,2,False
+Alberto Rodriguez Larreta,Argentina,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Franco Rol,Italy,"[1950, 1951, 1952]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Alan Rollinson,United Kingdom,[1965],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Tony Rolt,United Kingdom,"[1950, 1953, 1955]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Bertil Roos,Sweden,[1974],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Pedro de la Rosa,Spain,"[1999, 2000, 2001, 2002, 2005, 2006, 2010, 2011, 2012]",0.0,107.0,104.0,0.0,0.0,1.0,1.0,35.0,False,,2010,0.0,0.9719626168224299,0.0,0.009345794392523364,0.009345794392523364,0.32710280373831774,9,False
+Keke Rosberg,Finland,"[1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986]",1.0,128.0,114.0,5.0,5.0,17.0,3.0,159.5,False,[1982],1980,0.0390625,0.890625,0.0390625,0.1328125,0.0234375,1.24609375,9,True
+Nico Rosberg,Germany,"[2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016]",1.0,206.0,206.0,30.0,23.0,57.0,20.0,1594.5,False,[2016],2010,0.14563106796116504,1.0,0.11165048543689321,0.2766990291262136,0.0970873786407767,7.740291262135922,11,True
+Mauri Rose,United States,"[1950, 1951]",0.0,2.0,2.0,0.0,0.0,1.0,0.0,4.0,False,,1950,0.0,1.0,0.0,0.5,0.0,2.0,2,False
+Louis Rosier,France,"[1950, 1951, 1952, 1953, 1954, 1955, 1956]",0.0,38.0,38.0,0.0,0.0,2.0,0.0,18.0,False,,1950,0.0,1.0,0.0,0.05263157894736842,0.0,0.47368421052631576,7,False
+Ricardo Rosset,Brazil,"[1996, 1997, 1998]",0.0,33.0,26.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.7878787878787878,0.0,0.0,0.0,0.0,3,False
+Alexander Rossi,United States,[2015],0.0,7.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,2020,0.0,0.7142857142857143,0.0,0.0,0.0,0.0,1,False
+Huub Rothengatter,Netherlands,"[1984, 1985, 1986]",0.0,30.0,25.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,3,False
+Basil van Rooyen,South Africa,"[1968, 1969]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Lloyd Ruby,United States,"[1960, 1961]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Jean-Claude Rudaz,Switzerland,[1964],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+George Russell,United Kingdom,"[2019, 2020, 2021, 2022]",0.0,83.0,83.0,1.0,1.0,9.0,5.0,300.0,True,,2020,0.012048192771084338,1.0,0.012048192771084338,0.10843373493975904,0.060240963855421686,3.6144578313253013,4,False
+Eddie Russo,United States,"[1955, 1956, 1957, 1960]",0.0,7.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5714285714285714,0.0,0.0,0.0,0.0,4,False
+Paul Russo,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,11.0,8.0,0.0,0.0,1.0,1.0,8.5,False,,1960,0.0,0.7272727272727273,0.0,0.09090909090909091,0.09090909090909091,0.7727272727272727,11,False
+Troy Ruttman,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1960]",0.0,12.0,8.0,0.0,1.0,1.0,0.0,9.5,False,,1950,0.0,0.6666666666666666,0.08333333333333333,0.08333333333333333,0.0,0.7916666666666666,10,False
+Peter Ryan,Canada,[1961],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Eddie Sachs,United States,"[1957, 1958, 1959, 1960]",0.0,7.0,4.0,1.0,0.0,0.0,0.0,0.0,False,,1960,0.14285714285714285,0.5714285714285714,0.0,0.0,0.0,0.0,4,False
+Bob Said,United States,[1959],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Carlos Sainz Jr.,Spain,"[2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",0.0,164.0,163.0,3.0,1.0,15.0,3.0,794.5,True,,2020,0.018292682926829267,0.9939024390243902,0.006097560975609756,0.09146341463414634,0.018292682926829267,4.844512195121951,8,False
+Eliseo Salazar,Chile,"[1981, 1982, 1983]",0.0,37.0,24.0,0.0,0.0,0.0,0.0,3.0,False,,1980,0.0,0.6486486486486487,0.0,0.0,0.0,0.08108108108108109,3,False
+Mika Salo,Finland,"[1994, 1995, 1996, 1997, 1998, 1999, 2000, 2002]",0.0,111.0,109.0,0.0,0.0,2.0,0.0,33.0,False,,2000,0.0,0.9819819819819819,0.0,0.018018018018018018,0.0,0.2972972972972973,8,False
+Roy Salvadori,United Kingdom,"[1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962]",0.0,50.0,47.0,0.0,0.0,2.0,0.0,19.0,False,,1960,0.0,0.94,0.0,0.04,0.0,0.38,11,False
+Consalvo Sanesi,Italy,"[1950, 1951]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.6,2,False
+Stéphane Sarrazin,France,[1999],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Logan Sargeant,United States,[2023],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,True,,2020,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Takuma Sato,Japan,"[2002, 2003, 2004, 2005, 2006, 2007, 2008]",0.0,92.0,90.0,0.0,0.0,1.0,0.0,44.0,False,,2000,0.0,0.9782608695652174,0.0,0.010869565217391304,0.0,0.4782608695652174,7,False
+Carl Scarborough,United States,"[1951, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Ludovico Scarfiotti,Italy,"[1963, 1964, 1965, 1966, 1967, 1968]",0.0,12.0,10.0,0.0,1.0,1.0,1.0,17.0,False,,1970,0.0,0.8333333333333334,0.08333333333333333,0.08333333333333333,0.08333333333333333,1.4166666666666667,6,False
+Giorgio Scarlatti,Italy,"[1956, 1957, 1958, 1959, 1960, 1961]",0.0,15.0,12.0,0.0,0.0,0.0,0.0,1.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.06666666666666667,6,False
+Ian Scheckter,South Africa,"[1974, 1975, 1976, 1977]",0.0,20.0,18.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.9,0.0,0.0,0.0,0.0,4,False
+Jody Scheckter,South Africa,"[1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980]",1.0,113.0,112.0,3.0,10.0,33.0,5.0,246.0,False,[1979],1980,0.02654867256637168,0.9911504424778761,0.08849557522123894,0.2920353982300885,0.04424778761061947,2.1769911504424777,9,True
+Harry Schell,United States,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,57.0,56.0,0.0,0.0,2.0,0.0,32.0,False,,1960,0.0,0.9824561403508771,0.0,0.03508771929824561,0.0,0.5614035087719298,11,False
+Tim Schenken,Australia,"[1970, 1971, 1972, 1973, 1974]",0.0,36.0,34.0,0.0,0.0,1.0,0.0,7.0,False,,1970,0.0,0.9444444444444444,0.0,0.027777777777777776,0.0,0.19444444444444445,5,False
+Albert Scherrer,Switzerland,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Domenico Schiattarella,Italy,"[1994, 1995]",0.0,7.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.8571428571428571,0.0,0.0,0.0,0.0,2,False
+Heinz Schiller,Switzerland,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bill Schindler,United States,"[1950, 1951, 1952]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Jean-Louis Schlesser,France,"[1983, 1988]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Jo Schlesser,France,[1968],0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bernd Schneider,West Germany,"[1988, 1989, 1990]",0.0,34.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.2647058823529412,0.0,0.0,0.0,0.0,3,False
+Rudolf Schoeller,Switzerland,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Rob Schroeder,United States,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Michael Schumacher,Germany,"[1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2010, 2011, 2012]",7.0,308.0,306.0,68.0,91.0,155.0,77.0,1566.0,False,"[1994, 1995, 2000, 2001, 2002, 2003, 2004]",2000,0.22077922077922077,0.9935064935064936,0.29545454545454547,0.5032467532467533,0.25,5.084415584415584,19,True
+Mick Schumacher,Germany,"[2021, 2022]",0.0,44.0,43.0,0.0,0.0,0.0,0.0,12.0,False,,2020,0.0,0.9772727272727273,0.0,0.0,0.0,0.2727272727272727,2,False
+Ralf Schumacher,Germany,"[1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007]",0.0,181.0,180.0,6.0,6.0,27.0,8.0,329.0,False,,2000,0.03314917127071823,0.994475138121547,0.03314917127071823,0.14917127071823205,0.04419889502762431,1.8176795580110496,11,False
+Vern Schuppan,Australia,"[1972, 1974, 1975, 1977]",0.0,13.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6923076923076923,0.0,0.0,0.0,0.0,4,False
+Adolfo Schwelm Cruz,Argentina,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bob Scott,United States,"[1952, 1953, 1954]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Archie Scott Brown,United Kingdom,[1956],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Piero Scotti,Italy,[1956],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Wolfgang Seidel,West Germany,"[1953, 1958, 1960, 1961, 1962]",0.0,12.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8333333333333334,0.0,0.0,0.0,0.0,5,False
+Günther Seiffert,West Germany,[1962],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Ayrton Senna,Brazil,"[1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994]",3.0,162.0,161.0,65.0,41.0,80.0,19.0,610.0,False,"[1988, 1990, 1991]",1990,0.4012345679012346,0.9938271604938271,0.25308641975308643,0.49382716049382713,0.11728395061728394,3.765432098765432,11,True
+Bruno Senna,Brazil,"[2010, 2011, 2012]",0.0,46.0,46.0,0.0,0.0,0.0,1.0,33.0,False,,2010,0.0,1.0,0.0,0.0,0.021739130434782608,0.717391304347826,3,False
+Dorino Serafini,Italy,[1950],0.0,1.0,1.0,0.0,0.0,1.0,0.0,3.0,False,,1950,0.0,1.0,0.0,1.0,0.0,3.0,1,False
+Chico Serra,Brazil,"[1981, 1982, 1983]",0.0,33.0,18.0,0.0,0.0,0.0,0.0,1.0,False,,1980,0.0,0.5454545454545454,0.0,0.0,0.0,0.030303030303030304,3,False
+Doug Serrurier,South Africa,"[1962, 1963, 1965]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,3,False
+Johnny Servoz-Gavin,France,"[1967, 1968, 1969, 1970]",0.0,13.0,12.0,0.0,0.0,1.0,0.0,9.0,False,,1970,0.0,0.9230769230769231,0.0,0.07692307692307693,0.0,0.6923076923076923,4,False
+Tony Settember,United States,"[1962, 1963]",0.0,7.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8571428571428571,0.0,0.0,0.0,0.0,2,False
+Hap Sharp,United States,"[1961, 1962, 1963, 1964]",0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,4,False
+Brian Shawe-Taylor,United Kingdom,"[1950, 1951]",0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,2,False
+Carroll Shelby,United States,"[1958, 1959]",0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Tony Shelly,New Zealand,[1962],0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,1,False
+Jo Siffert,Switzerland,"[1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971]",0.0,100.0,96.0,2.0,2.0,6.0,4.0,68.0,False,,1970,0.02,0.96,0.02,0.06,0.04,0.68,10,False
+André Simon,France,"[1951, 1952, 1955, 1956, 1957]",0.0,12.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.9166666666666666,0.0,0.0,0.0,0.0,5,False
+Sergey Sirotkin,Russia,[2018],0.0,21.0,21.0,0.0,0.0,0.0,0.0,1.0,False,,2020,0.0,1.0,0.0,0.0,0.0,0.047619047619047616,1,False
+Rob Slotemaker,Netherlands,[1962],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Moisés Solana,Mexico,"[1963, 1964, 1965, 1966, 1967, 1968]",0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,6,False
+Alex Soler-Roig,Spain,"[1970, 1971, 1972]",0.0,10.0,6.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Raymond Sommer,France,[1950],0.0,5.0,5.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.6,1,False
+Vincenzo Sospiri,Italy,[1997],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Stephen South,United Kingdom,[1980],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Mike Sparken,France,[1955],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Scott Speed,United States,"[2006, 2007]",0.0,28.0,28.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Mike Spence,United Kingdom,"[1963, 1964, 1965, 1966, 1967, 1968]",0.0,37.0,36.0,0.0,0.0,1.0,0.0,27.0,False,,1970,0.0,0.972972972972973,0.0,0.02702702702702703,0.0,0.7297297297297297,6,False
+Alan Stacey,United Kingdom,"[1958, 1959, 1960]",0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Gaetano Starrabba,Italy,[1961],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Will Stevens,United Kingdom,"[2014, 2015]",0.0,20.0,18.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,0.9,0.0,0.0,0.0,0.0,2,False
+Chuck Stevenson,United States,"[1951, 1952, 1953, 1954, 1960]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,5,False
+Ian Stewart,United Kingdom,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jackie Stewart,United Kingdom,"[1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973]",3.0,100.0,99.0,17.0,27.0,43.0,15.0,359.0,False,"[1969, 1971, 1973]",1970,0.17,0.99,0.27,0.43,0.15,3.59,9,True
+Jimmy Stewart,United Kingdom,[1953],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Siegfried Stohr,Italy,[1981],0.0,13.0,9.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.6923076923076923,0.0,0.0,0.0,0.0,1,False
+Rolf Stommelen,West Germany,"[1970, 1971, 1972, 1973, 1974, 1975, 1976, 1978]",0.0,63.0,54.0,0.0,0.0,1.0,0.0,14.0,False,,1970,0.0,0.8571428571428571,0.0,0.015873015873015872,0.0,0.2222222222222222,8,False
+Philippe Streiff,France,"[1984, 1985, 1986, 1987, 1988]",0.0,54.0,53.0,0.0,0.0,1.0,0.0,11.0,False,,1990,0.0,0.9814814814814815,0.0,0.018518518518518517,0.0,0.2037037037037037,5,False
+Lance Stroll,Canada,"[2017, 2018, 2019, 2020, 2021, 2022]",0.0,124.0,123.0,1.0,0.0,3.0,0.0,202.0,True,,2020,0.008064516129032258,0.9919354838709677,0.0,0.024193548387096774,0.0,1.6290322580645162,6,False
+Hans Stuck,West Germany,"[1951, 1952, 1953]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Hans-Joachim Stuck,West Germany,"[1974, 1975, 1976, 1977, 1978, 1979]",0.0,81.0,74.0,0.0,0.0,2.0,0.0,29.0,False,,1980,0.0,0.9135802469135802,0.0,0.024691358024691357,0.0,0.35802469135802467,6,False
+Otto Stuppacher,Austria,[1976],0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Danny Sullivan,United States,[1983],0.0,15.0,15.0,0.0,0.0,0.0,0.0,2.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.13333333333333333,1,False
+Marc Surer,Switzerland,"[1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986]",0.0,88.0,82.0,0.0,0.0,0.0,1.0,17.0,False,,1980,0.0,0.9318181818181818,0.0,0.0,0.011363636363636364,0.19318181818181818,8,False
+John Surtees,United Kingdom,"[1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972]",1.0,113.0,111.0,8.0,6.0,24.0,10.0,180.0,False,[1964],1970,0.07079646017699115,0.9823008849557522,0.05309734513274336,0.21238938053097345,0.08849557522123894,1.592920353982301,13,True
+Andy Sutcliffe,United Kingdom,[1977],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Adrian Sutil,Germany,"[2007, 2008, 2009, 2010, 2011, 2013, 2014]",0.0,128.0,128.0,0.0,0.0,0.0,1.0,124.0,False,,2010,0.0,1.0,0.0,0.0,0.0078125,0.96875,7,False
+Len Sutton,United States,"[1958, 1959, 1960]",0.0,4.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.75,0.0,0.0,0.0,0.0,3,False
+Aguri Suzuki,Japan,"[1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995]",0.0,88.0,65.0,0.0,0.0,1.0,0.0,8.0,False,,1990,0.0,0.7386363636363636,0.0,0.011363636363636364,0.0,0.09090909090909091,8,False
+Toshio Suzuki,Japan,[1993],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jacques Swaters,Belgium,"[1951, 1953, 1954]",0.0,8.0,7.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.875,0.0,0.0,0.0,0.0,3,False
+Bob Sweikert,United States,"[1952, 1953, 1954, 1955, 1956]",0.0,7.0,5.0,0.0,1.0,1.0,0.0,8.0,False,,1950,0.0,0.7142857142857143,0.14285714285714285,0.14285714285714285,0.0,1.1428571428571428,5,False
+Toranosuke Takagi,Japan,"[1998, 1999]",0.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Noritake Takahara,Japan,"[1976, 1977]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Kunimitsu Takahashi,Japan,[1977],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Patrick Tambay,France,"[1977, 1978, 1979, 1981, 1982, 1983, 1984, 1985, 1986]",0.0,123.0,114.0,5.0,2.0,11.0,2.0,103.0,False,,1980,0.04065040650406504,0.926829268292683,0.016260162601626018,0.08943089430894309,0.016260162601626018,0.8373983739837398,9,False
+Luigi Taramazzo,Italy,[1958],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Gabriele Tarquini,Italy,"[1987, 1988, 1989, 1990, 1991, 1992, 1995]",0.0,79.0,38.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,0.4810126582278481,0.0,0.0,0.0,0.012658227848101266,7,False
+Piero Taruffi,Italy,"[1950, 1951, 1952, 1954, 1955, 1956]",0.0,19.0,18.0,0.0,1.0,5.0,1.0,41.0,False,,1950,0.0,0.9473684210526315,0.05263157894736842,0.2631578947368421,0.05263157894736842,2.1578947368421053,6,False
+Dennis Taylor,United Kingdom,[1959],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Henry Taylor,United Kingdom,"[1959, 1960, 1961]",0.0,11.0,8.0,0.0,0.0,0.0,0.0,3.0,False,,1960,0.0,0.7272727272727273,0.0,0.0,0.0,0.2727272727272727,3,False
+John Taylor,United Kingdom,"[1964, 1966]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,1.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.2,2,False
+Mike Taylor,United Kingdom,"[1959, 1960]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Trevor Taylor,United Kingdom,"[1959, 1961, 1962, 1963, 1964, 1966]",0.0,29.0,27.0,0.0,0.0,1.0,0.0,8.0,False,,1960,0.0,0.9310344827586207,0.0,0.034482758620689655,0.0,0.27586206896551724,6,False
+Marshall Teague,United States,"[1953, 1954, 1957]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Shorty Templeman,United States,"[1955, 1958, 1960]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6,0.0,0.0,0.0,0.0,3,False
+Max de Terra,Switzerland,"[1952, 1953]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+André Testut,Monaco,"[1958, 1959]",0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Mike Thackwell,New Zealand,"[1980, 1984]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Alfonso Thiele,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Eric Thompson,United Kingdom,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,False,,1950,0.0,1.0,0.0,0.0,0.0,2.0,1,False
+Johnny Thomson,United States,"[1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960]",0.0,8.0,8.0,1.0,0.0,1.0,1.0,10.0,False,,1960,0.125,1.0,0.0,0.125,0.125,1.25,8,False
+Leslie Thorne,United Kingdom,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bud Tingelstad,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Sam Tingle,Rhodesia,"[1963, 1965, 1967, 1968, 1969]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,5,False
+Desmond Titterington,United Kingdom,[1956],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Johnnie Tolan,United States,"[1956, 1957, 1958]",0.0,7.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.42857142857142855,0.0,0.0,0.0,0.0,3,False
+Alejandro de Tomaso,Argentina,"[1957, 1959]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Charles de Tornaco,Belgium,"[1952, 1953]",0.0,4.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Tony Trimmer,United Kingdom,"[1975, 1976, 1977, 1978]",0.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,4,False
+Maurice Trintignant,France,"[1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964]",0.0,84.0,82.0,0.0,2.0,10.0,1.0,72.33,False,,1960,0.0,0.9761904761904762,0.023809523809523808,0.11904761904761904,0.011904761904761904,0.8610714285714286,15,False
+Wolfgang von Trips,West Germany,"[1956, 1957, 1958, 1959, 1960, 1961]",0.0,29.0,27.0,1.0,2.0,6.0,0.0,56.0,False,,1960,0.034482758620689655,0.9310344827586207,0.06896551724137931,0.20689655172413793,0.0,1.9310344827586208,6,False
+Jarno Trulli,Italy,"[1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011]",0.0,256.0,252.0,4.0,1.0,11.0,1.0,246.5,False,,2000,0.015625,0.984375,0.00390625,0.04296875,0.00390625,0.962890625,15,False
+Yuki Tsunoda,Japan,"[2021, 2022]",0.0,45.0,43.0,0.0,0.0,0.0,0.0,44.0,True,,2020,0.0,0.9555555555555556,0.0,0.0,0.0,0.9777777777777777,2,False
+Esteban Tuero,Argentina,[1998],0.0,16.0,16.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Guy Tunmer,South Africa,[1975],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Jack Turner,United States,"[1956, 1957, 1958, 1959]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,4,False
+Toni Ulmen,West Germany,[1952],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Bobby Unser,United States,[1968],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Jerry Unser Jr.,United States,[1958],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Alberto Uria,Uruguay,"[1955, 1956]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Nino Vaccarella,Italy,"[1961, 1962, 1965]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.8,0.0,0.0,0.0,0.0,3,False
+Stoffel Vandoorne,Belgium,"[2016, 2017, 2018]",0.0,42.0,41.0,0.0,0.0,0.0,0.0,26.0,False,,2020,0.0,0.9761904761904762,0.0,0.0,0.0,0.6190476190476191,3,False
+Bob Veith,United States,"[1956, 1957, 1958, 1959, 1960]",0.0,5.0,5.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,5,False
+Jean-Éric Vergne,France,"[2012, 2013, 2014]",0.0,58.0,58.0,0.0,0.0,0.0,0.0,51.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.8793103448275862,3,False
+Jos Verstappen,Netherlands,"[1994, 1995, 1996, 1997, 1998, 2000, 2001, 2003]",0.0,107.0,106.0,0.0,0.0,2.0,0.0,17.0,False,,2000,0.0,0.9906542056074766,0.0,0.018691588785046728,0.0,0.1588785046728972,8,False
+Max Verstappen,Netherlands,"[2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",2.0,164.0,164.0,21.0,36.0,78.0,21.0,2036.5,True,"[2021, 2022]",2020,0.12804878048780488,1.0,0.21951219512195122,0.47560975609756095,0.12804878048780488,12.417682926829269,8,True
+Sebastian Vettel,Germany,"[2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]",4.0,300.0,299.0,57.0,53.0,122.0,38.0,3098.0,False,"[2010, 2011, 2012, 2013]",2010,0.19,0.9966666666666667,0.17666666666666667,0.4066666666666667,0.12666666666666668,10.326666666666666,16,True
+Gilles Villeneuve,Canada,"[1977, 1978, 1979, 1980, 1981, 1982]",0.0,68.0,67.0,2.0,6.0,13.0,8.0,101.0,False,,1980,0.029411764705882353,0.9852941176470589,0.08823529411764706,0.19117647058823528,0.11764705882352941,1.4852941176470589,6,False
+Jacques Villeneuve,Canada,"[1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006]",1.0,165.0,163.0,13.0,11.0,23.0,9.0,235.0,False,[1997],2000,0.07878787878787878,0.9878787878787879,0.06666666666666667,0.1393939393939394,0.05454545454545454,1.4242424242424243,11,True
+Jacques Villeneuve Sr.,Canada,"[1981, 1983]",0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,2,False
+Luigi Villoresi,Italy,"[1950, 1951, 1952, 1953, 1954, 1955, 1956]",0.0,34.0,31.0,0.0,0.0,8.0,1.0,46.0,False,,1950,0.0,0.9117647058823529,0.0,0.23529411764705882,0.029411764705882353,1.3529411764705883,7,False
+Emilio de Villota,Spain,"[1976, 1977, 1978, 1981, 1982]",0.0,15.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.13333333333333333,0.0,0.0,0.0,0.0,5,False
+Ottorino Volonterio,Switzerland,"[1954, 1956, 1957]",0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Jo Vonlanthen,Switzerland,[1975],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Ernie de Vos,Canada,[1963],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Nyck de Vries,Netherlands,[2022],0.0,2.0,2.0,0.0,0.0,0.0,0.0,2.0,True,,2020,0.0,1.0,0.0,0.0,0.0,1.0,1,False
+Bill Vukovich,United States,"[1951, 1952, 1953, 1954, 1955]",0.0,6.0,5.0,1.0,2.0,2.0,3.0,19.0,False,,1950,0.16666666666666666,0.8333333333333334,0.3333333333333333,0.3333333333333333,0.5,3.1666666666666665,5,False
+Syd van der Vyver,South Africa,[1962],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Fred Wacker,United States,"[1953, 1954]",0.0,5.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.6,0.0,0.0,0.0,0.0,2,False
+David Walker,Australia,"[1971, 1972]",0.0,11.0,11.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Peter Walker,United Kingdom,"[1950, 1951, 1955]",0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Lee Wallard,United States,"[1950, 1951]",0.0,3.0,2.0,0.0,1.0,1.0,1.0,9.0,False,,1950,0.0,0.6666666666666666,0.3333333333333333,0.3333333333333333,0.3333333333333333,3.0,2,False
+Heini Walter,Switzerland,[1962],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Rodger Ward,United States,"[1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1963]",0.0,12.0,12.0,0.0,1.0,2.0,0.0,14.0,False,,1960,0.0,1.0,0.08333333333333333,0.16666666666666666,0.0,1.1666666666666667,11,False
+Derek Warwick,United Kingdom,"[1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1993]",0.0,162.0,147.0,0.0,0.0,4.0,2.0,71.0,False,,1990,0.0,0.9074074074074074,0.0,0.024691358024691357,0.012345679012345678,0.4382716049382716,11,False
+John Watson,United Kingdom,"[1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1985]",0.0,154.0,152.0,2.0,5.0,20.0,5.0,169.0,False,,1980,0.012987012987012988,0.987012987012987,0.032467532467532464,0.12987012987012986,0.032467532467532464,1.0974025974025974,12,False
+Spider Webb,United States,"[1950, 1952, 1953, 1954]",0.0,5.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,0.8,0.0,0.0,0.0,0.0,4,False
+Mark Webber,Australia,"[2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]",0.0,217.0,215.0,13.0,9.0,42.0,19.0,1047.5,False,,2010,0.059907834101382486,0.9907834101382489,0.041474654377880185,0.1935483870967742,0.08755760368663594,4.8271889400921655,12,False
+Pascal Wehrlein,Germany,"[2016, 2017]",0.0,40.0,39.0,0.0,0.0,0.0,0.0,6.0,False,,2020,0.0,0.975,0.0,0.0,0.0,0.15,2,False
+Volker Weidler,West Germany,[1989],0.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Wayne Weiler,United States,[1960],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Karl Wendlinger,Austria,"[1991, 1992, 1993, 1994, 1995]",0.0,42.0,41.0,0.0,0.0,0.0,0.0,14.0,False,,1990,0.0,0.9761904761904762,0.0,0.0,0.0,0.3333333333333333,5,False
+Peter Westbury,United Kingdom,[1970],0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Chuck Weyant,United States,"[1955, 1957, 1958, 1959]",0.0,6.0,4.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,4,False
+Ken Wharton,United Kingdom,"[1952, 1953, 1954, 1955]",0.0,16.0,15.0,0.0,0.0,0.0,0.0,3.0,False,,1950,0.0,0.9375,0.0,0.0,0.0,0.1875,4,False
+Ted Whiteaway,United Kingdom,[1955],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Graham Whitehead,United Kingdom,[1952],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Peter Whitehead,United Kingdom,"[1950, 1951, 1952, 1953, 1954]",0.0,12.0,10.0,0.0,0.0,1.0,0.0,4.0,False,,1950,0.0,0.8333333333333334,0.0,0.08333333333333333,0.0,0.3333333333333333,5,False
+Bill Whitehouse,United Kingdom,[1954],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1950,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Robin Widdows,United Kingdom,[1968],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Eppie Wietzes,Canada,"[1967, 1974]",0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,2,False
+Mike Wilds,United Kingdom,"[1974, 1975, 1976]",0.0,8.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.375,0.0,0.0,0.0,0.0,3,False
+Jonathan Williams,United Kingdom,[1967],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Roger Williamson,United Kingdom,[1973],0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1970,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Dempsey Wilson,United States,"[1958, 1960]",0.0,5.0,2.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.4,0.0,0.0,0.0,0.0,2,False
+Desiré Wilson,South Africa,[1980],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Justin Wilson,United Kingdom,[2003],0.0,16.0,16.0,0.0,0.0,0.0,0.0,1.0,False,,2000,0.0,1.0,0.0,0.0,0.0,0.0625,1,False
+Vic Wilson,United Kingdom,"[1960, 1966]",0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,1960,0.0,0.5,0.0,0.0,0.0,0.0,2,False
+Joachim Winkelhock,West Germany,[1989],0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1990,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Manfred Winkelhock,West Germany,"[1980, 1982, 1983, 1984, 1985]",0.0,56.0,47.0,0.0,0.0,0.0,0.0,2.0,False,,1980,0.0,0.8392857142857143,0.0,0.0,0.0,0.03571428571428571,5,False
+Markus Winkelhock,Germany,[2007],0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,1,False
+Reine Wisell,Sweden,"[1970, 1971, 1972, 1973, 1974]",0.0,23.0,22.0,0.0,0.0,1.0,0.0,13.0,False,,1970,0.0,0.9565217391304348,0.0,0.043478260869565216,0.0,0.5652173913043478,5,False
+Roelof Wunderink,Netherlands,[1975],0.0,6.0,3.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.5,0.0,0.0,0.0,0.0,1,False
+Alexander Wurz,Austria,"[1997, 1998, 1999, 2000, 2005, 2007]",0.0,69.0,69.0,0.0,0.0,3.0,1.0,45.0,False,,2000,0.0,1.0,0.0,0.043478260869565216,0.014492753623188406,0.6521739130434783,6,False
+Sakon Yamamoto,Japan,"[2006, 2007, 2010]",0.0,21.0,21.0,0.0,0.0,0.0,0.0,0.0,False,,2010,0.0,1.0,0.0,0.0,0.0,0.0,3,False
+Alex Yoong,Malaysia,"[2001, 2002]",0.0,18.0,14.0,0.0,0.0,0.0,0.0,0.0,False,,2000,0.0,0.7777777777777778,0.0,0.0,0.0,0.0,2,False
+Alessandro Zanardi,Italy,"[1991, 1992, 1993, 1994, 1999]",0.0,44.0,41.0,0.0,0.0,0.0,0.0,1.0,False,,1990,0.0,0.9318181818181818,0.0,0.0,0.0,0.022727272727272728,5,False
+Emilio Zapico,Spain,[1976],0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.0,0.0,0.0,0.0,0.0,1,False
+Zhou Guanyu,China,[2022],0.0,23.0,23.0,0.0,0.0,0.0,2.0,6.0,True,,2020,0.0,1.0,0.0,0.0,0.08695652173913043,0.2608695652173913,1,False
+Ricardo Zonta,Brazil,"[1999, 2000, 2001, 2004, 2005]",0.0,37.0,36.0,0.0,0.0,0.0,0.0,3.0,False,,2000,0.0,0.972972972972973,0.0,0.0,0.0,0.08108108108108109,5,False
+Renzo Zorzi,Italy,"[1975, 1976, 1977]",0.0,7.0,7.0,0.0,0.0,0.0,0.0,1.0,False,,1980,0.0,1.0,0.0,0.0,0.0,0.14285714285714285,3,False
+Ricardo Zunino,Argentina,"[1979, 1980, 1981]",0.0,11.0,10.0,0.0,0.0,0.0,0.0,0.0,False,,1980,0.0,0.9090909090909091,0.0,0.0,0.0,0.0,3,False
--- a/ilbekov_dmitriy_lab_3/README.md
+++ b/ilbekov_dmitriy_lab_3/README.md
@@ -0,0 +1,21 @@
+# Лабораторная работа 3
+### Вариант 10
+
+### Задание: 
+- Используя данные из "F1DriversDataset.csv" решает задачу классификации (с помощью дерева решений), в которой по различным характеристикам требуется найти для "Количества чемпионских титулов" два наиболее важных признака из трех: Количество поулов, Количество побед, количество подиумов
+### Запуск
+- Запустить файл lab3.py
+
+### Технологии
+- Язык - 'Python'
+- Библиотеки sklearn, numpy, pandas
+
+### Что делает
+Программа вычисляет оценку важности каждого признака с помощью атрибута `feature_importances_` классификатора. Важность признаков сохраняется в переменной `scores`, а также вычисляет оценку качества классификатора на тестовых данных `X_test` и `Y_test` с помощью метода `score`
+
+### Пример работы
+Пример работы представлен в виде скриншота:
+
+![Graphics](console.jpg)
+
+Наиболее важным признаком оказалось количество подиумов гонщика
--- a/ilbekov_dmitriy_lab_3/console.jpg
+++ b/ilbekov_dmitriy_lab_3/console.jpg
--- a/ilbekov_dmitriy_lab_3/lab3.py
+++ b/ilbekov_dmitriy_lab_3/lab3.py
@@ -0,0 +1,26 @@
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+import pandas as pd
+import numpy as np
+
+pd.options.mode.chained_assignment = None
+
+path = "F1DriversDataset.csv"
+required = ['Pole_Positions', 'Race_Wins', 'Podiums']
+target = 'Championships'
+
+data = pd.read_csv(path)
+X = data[required]
+y = data[target]
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+
+classifier_tree = DecisionTreeClassifier(random_state=42)
+classifier_tree.fit(X_train, Y_train)
+
+feature_names = required
+embarked_score = classifier_tree.feature_importances_[-3:].sum()
+scores = np.append(classifier_tree.feature_importances_[:2], embarked_score)
+scores = map(lambda score: round(score, 2), scores)
+print(dict(zip(feature_names, scores)))
+print("Оценка качества: ", classifier_tree.score(X_test, Y_test))
--- a/kurmyza_pavel_lab_1/README.md
+++ b/kurmyza_pavel_lab_1/README.md
@@ -0,0 +1,41 @@
+# Лабораторная работа №1
+
+## ПИбд-41, Курмыза Павел, Вариант 13
+
+### Данные:
+
+- make_moons (noise=0.3, random_state=rs)
+
+### Модели:
+
+- Линейную регрессию
+- Полиномиальную регрессию (со степенью 3)
+- Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, matplotlib, numpy
+
+## Что делает программа
+
+После генерации набора данных с помощью функции make_moons(), программа создает графики для моделей, которые указаны в
+задании. Затем она выводит в консоль качество данных для этих моделей.
+
+## Тесты
+
+### Консоль
+
+![Консольный вывод](console_output.jpg)
+
+### Графики
+
+![Графики](plots.jpg)
+
+### Вывод
+
+Исходя из этого, можно сделать вывод: лучший результат показала модель многослойного персептрона на 100 нейронах.
--- a/kurmyza_pavel_lab_1/console_output.jpg
+++ b/kurmyza_pavel_lab_1/console_output.jpg
--- a/kurmyza_pavel_lab_1/main.py
+++ b/kurmyza_pavel_lab_1/main.py
@@ -0,0 +1,91 @@
+from random import randrange
+import numpy as np
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score, mean_squared_error
+from matplotlib import pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.datasets import make_moons
+from sklearn.pipeline import make_pipeline
+
+RANDOM_STATE = randrange(50)
+
+# Генерация случайных данных на основе случайного состояния
+
+X, y = make_moons(noise=0.3, random_state=RANDOM_STATE)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RANDOM_STATE)
+
+# Полиномиальная регрессия (3 степень)
+
+DEGREE = 3
+
+poly_regression = make_pipeline(PolynomialFeatures(degree=DEGREE), LinearRegression())  # создание модели
+poly_regression.fit(X_train, y_train)  # обучение
+y_pred_poly_regression = poly_regression.predict(X_test)  # предсказание
+
+# Линейная регрессия
+
+linear_regression = LinearRegression()  # создание модели
+linear_regression.fit(X_train, y_train)  # обучение
+y_pred_linear_regression = linear_regression.predict(X_test)  # предсказание
+
+# Многослойный персептрон (100 нейронов)
+
+HIDDEN_LAYER_SIZES = 100
+ALPHA = 0.01
+
+perceptron_100 = MLPClassifier(hidden_layer_sizes=(HIDDEN_LAYER_SIZES,), alpha=ALPHA,
+                               random_state=RANDOM_STATE)  # создание модели
+perceptron_100.fit(X_train, y_train)  # обучение
+y_pred_perceptron_100 = perceptron_100.predict(X_test)  # предсказание
+
+# Оценка точности и вывод в консоль
+
+acc_linear_regression = mean_squared_error(y_test, y_pred_linear_regression)
+acc_poly_regression = mean_squared_error(y_test, y_pred_poly_regression)
+acc_perceptron_100 = accuracy_score(y_test, y_pred_perceptron_100)
+
+print(f"Оценка точности: "
+      f"\n Линейная регрессия: {acc_linear_regression}"
+      f"\n Полиномиальная регрессия (3 степень): {acc_poly_regression}"
+      f"\n Многослойный персептрон (100 нейронов): {acc_perceptron_100}")
+
+# Предсказание классов для точек графика для их визуализации
+
+x_min, y_min = X[:, 0].min() - 0.5, X[:, 1].min() - 0.5
+x_max, y_max = X[:, 0].max() + 0.5, X[:, 1].max() + 0.5
+
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
+
+prediction_data = np.c_[xx.ravel(), yy.ravel()]
+
+Z_poly_regression = poly_regression.predict(prediction_data)
+Z_poly_regression = Z_poly_regression.reshape(xx.shape)
+
+Z_linear_regression = linear_regression.predict(prediction_data)
+Z_linear_regression = Z_linear_regression.reshape(xx.shape)
+
+Z_perceptron_100 = perceptron_100.predict(prediction_data)
+Z_perceptron_100 = Z_perceptron_100.reshape(xx.shape)
+
+
+# Отрисовка графиков
+
+def draw_graphic(title, nrows, ncols, index, Z):
+    plt.subplot(nrows, ncols, index)
+    plt.contourf(xx, yy, Z, alpha=0.8)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, alpha=0.6)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train)
+    plt.title(title)
+    plt.xlabel('1 признак')
+    plt.ylabel('2 признак')
+
+
+draw_graphic('Линейная регрессия', 1, 3, 1, Z_linear_regression)
+draw_graphic('Полиномиальная регрессия', 1, 3, 2, Z_poly_regression)
+draw_graphic('Персептрон', 1, 3, 3, Z_perceptron_100)
+
+plt.tight_layout()
+plt.show()
--- a/kurmyza_pavel_lab_1/plots.jpg
+++ b/kurmyza_pavel_lab_1/plots.jpg
--- a/kurmyza_pavel_lab_2/README.md
+++ b/kurmyza_pavel_lab_2/README.md
@@ -0,0 +1,59 @@
+# Лабораторная работа №2
+
+## ПИбд-41, Курмыза Павел, Вариант 13
+
+## Как запустить ЛР
+
+- Запустить файл main.py
+
+## Используемые технологии
+
+- Язык программирования Python
+- Библиотеки: sklearn, numpy
+
+## Что делает программа
+
+Выполняет ранжирование 14 признаков для регрессионной проблемы Фридмана с помощью моделей:
+
+- Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE)
+- Сокращение признаков Случайными деревьями (Random Forest Regressor)
+- Линейная корреляция (f_regression)
+
+Отображение получившихся результатов: 4 самых важных признака по среднему значению, значения признаков для каждой
+модели.
+
+## Результаты
+
+### RFE
+
+{'x1': 1.0, 'x2': 1.0, 'x3': 1.0, 'x4': 1.0, 'x5': 1.0, 'x11': 1.0, 'x13': 1.0, 'x12': 0.86, 'x14': 0.71, 'x8': 0.57, '
+x6': 0.43, 'x10': 0.29, 'x7': 0.14, 'x9': 0.0}
+
+### RFR
+
+{'x14': 1.0, 'x2': 0.84, 'x4': 0.77, 'x1': 0.74, 'x11': 0.36, 'x12': 0.35, 'x5': 0.28, 'x3': 0.12, 'x13': 0.12, 'x6':
+0.01, 'x7': 0.01, 'x8': 0.01, 'x9': 0.01, 'x10': 0.0}
+
+### f_regression
+
+{'x4': 1.0, 'x14': 0.97, 'x2': 0.57, 'x12': 0.56, 'x1': 0.44, 'x11': 0.43, 'x5': 0.17, 'x8': 0.13, 'x7': 0.1, 'x9':
+0.08, 'x10': 0.05, 'x6': 0.04, 'x3': 0.01, 'x13': 0.0}
+
+### Средние значения
+
+{'x1': 0.33, 'x2': 0.33, 'x3': 0.33, 'x4': 0.33, 'x5': 0.33, 'x11': 0.33, 'x13': 0.33, 'x12': 0.29, 'x14': 0.24, 'x8':
+0.19, 'x6': 0.14, 'x10': 0.1, 'x7': 0.05, 'x9': 0.0}
+
+## Вывод
+
+По итогу тестирования было выявлено:
+
+1. Модель рекурсивного сокращения признаков отдала предпочтение многим важным параметрам таким как x1, x2, x3, x4, x5,
+   x11, x13, x12, x14.
+2. Модель сокращения признаков случайными деревьями выявила в качестве важных признаков x14, x2, x4, x1. Несмотря на то,
+   что признак x3 не был выявлен, его влияние может быть учтено через скоррелированный параметр x14.
+3. Метод линейной корреляции (f_regression) сделал наилучшее взвешивание, отдав предпочтение прзинакам x4, x14, x2, x12.
+   Несмотря на то, что признаки x1 и x3 не были выявлены, их влияние может быть учтено через скоррелированные параметры
+   x12 и x14.
+
+Согласно среднему значению, важными признаками являются: x1, x2, x3, x4, x5.
--- a/kurmyza_pavel_lab_2/main.py
+++ b/kurmyza_pavel_lab_2/main.py
@@ -0,0 +1,94 @@
+from sklearn.linear_model import LinearRegression
+from sklearn.feature_selection import RFE, f_regression
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from operator import itemgetter
+from sklearn.ensemble import RandomForestRegressor
+
+
+def rank_to_dict(ranks, names):
+    ranks = np.abs(ranks)
+
+    minmax = MinMaxScaler()
+
+    ranks = minmax.fit_transform(np.array(ranks).reshape(FEATURES_AMOUNT, 1)).ravel()
+    ranks = map(lambda x: round(x, 2), ranks)
+    return dict(zip(names, ranks))
+
+
+def flip_array(arr):
+    return -1 * arr + np.max(arr)
+
+
+def sort_by_desc(dictionary):
+    return dict(sorted(dictionary.items(), key=itemgetter(1), reverse=True))
+
+
+def calc_mean(ranks):
+    mean = {}
+
+    for key, value in ranks.items():
+        for item in value.items():
+            if item[0] not in mean:
+                mean[item[0]] = 0
+                mean[item[0]] += item[1]
+
+    for key, value in mean.items():
+        res = value / len(ranks)
+        mean[key] = round(res, 2)
+
+    return sort_by_desc(mean)
+
+
+# Исходные данные составляют 750 строк-наблюдений и 14 столбцов-признаков
+
+FEATURES_SIZE = 750
+FEATURES_AMOUNT = 14
+
+# Генерация случайных исходных данных
+
+np.random.seed(0)
+x = np.random.uniform(0, 1, (FEATURES_SIZE, 14))
+
+# Создание функции-выхода (постановка регрессионной проблемы Фридмана) и добавление зависимости признаков
+
+y = (10 * np.sin(np.pi * x[:, 0] * x[:, 1]) + 20 * (x[:, 2] - .5) ** 2 +
+     10 * x[:, 3] + 5 * x[:, 4] ** 5 + np.random.normal(0, 1))
+
+x[:, 10:] = x[:, :4] + np.random.normal(0, .025, (FEATURES_SIZE, 4))
+
+# Создаём модель рекурсивного сокращения признаков на основе линейной модели и обучаем её
+
+regression = LinearRegression()
+regression.fit(x, y)
+rfe = RFE(regression)
+rfe.fit(x, y)
+
+# Создаём модель сокращения признаков случайными деревьями и обучаем её
+
+rfr = RandomForestRegressor()
+rfr.fit(x, y)
+
+# Создаём модель линейной корреляции и обучаем её
+
+f, _ = f_regression(x, y, center=False)
+
+# Аккумулируем наименования признаков
+
+features_names = ["x%s" % i for i in range(1, FEATURES_AMOUNT + 1)]
+
+# Собираем отображения значений каждого признака каждой моделью
+
+features_ranks = {
+    'RFE': sort_by_desc(rank_to_dict(flip_array(rfe.ranking_), features_names)),
+    'RFR': sort_by_desc(rank_to_dict(rfr.feature_importances_, features_names)),
+    'f_regression': sort_by_desc(rank_to_dict(f, features_names))
+}
+
+# Подсчитываем среднюю оценку и выводим результаты
+
+print(f"Результаты:"
+      f"\n RFE \n{features_ranks['RFE']}"
+      f"\n RFR \n{features_ranks['RFR']}"
+      f"\n f_regression \n {features_ranks['f_regression']}"
+      f"\n Средние значения \n{calc_mean(features_ranks)}")
--- a/senkin_alexander_lab_1/Linear.png
+++ b/senkin_alexander_lab_1/Linear.png
--- a/senkin_alexander_lab_1/Polynomial.png
+++ b/senkin_alexander_lab_1/Polynomial.png
--- a/senkin_alexander_lab_1/README.md
+++ b/senkin_alexander_lab_1/README.md
@@ -0,0 +1,38 @@
+Вариант 2
+
+Задание на лабораторную работу:
+
+По данным построить графики 3 моделей:
+
+- Линейную регрессию 
+- Полиномиальную регрессию (со степенью 3) 
+- Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0) 
+
+Данные: make_circles (noise=0.2, factor=0.5, random_state=rs)
+
+Как запустить лабораторную работу:
+
+Чтобы увидеть работу программы, нужно запустить исполняемый питон файл senkin_alexander_lab_1.py, после чего будут отрисованы 3 графика, которые также можно увидеть в формате png в папке проекта.
+
+Библиотеки
+
+Matplotlib. Используется для создания графиков.
+
+Sklearn. Предоставляет инструменты и алгоритмы, которые упрощают задачи, связанные с машинным обучением.
+
+Описание программы:
+
+- Генерируем набор данных из 100 точек данных используя функцию make_circles
+- С помощью функции train_test_split разделяем данные на тестовые и обучающие в соотношении 20 к 80
+- Создаем 3 модели:
+- Линейную регрессию
+- Полиномиальную регрессию (со степенью 3)
+- Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+- Модели используем для предсказания классов с помощью метода predict
+- Оцениваем точность каждой модели
+- Строим графики для визуального представления и оценивая работ моделей
+- Сравниваем точности моделей и выбираем наиболее точную
+
+![Linear.png](Linear.png)![Polynomial.png](Polynomial.png)![Ridge.png](Ridge.png)
+
+Изходя из результатов: Линейная - 0.30, Полиномиальная - 0.45, Гребневая полиномиальная - 0.47, делаем вывод, что наиболее точная модель - Гребневая полиномиальная.
--- a/senkin_alexander_lab_1/Ridge.png
+++ b/senkin_alexander_lab_1/Ridge.png
--- a/senkin_alexander_lab_1/senkin_alexander_lab_1.py
+++ b/senkin_alexander_lab_1/senkin_alexander_lab_1.py
@@ -0,0 +1,60 @@
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_circles
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import Ridge
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+
+# Создаем данные
+rs = 42  #желаемый random_state
+X, y = make_circles(n_samples=100, noise=0.2, factor=0.5, random_state=rs)
+
+# Разделяем данные на обучающий и тестовый наборы
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rs)
+
+# Обучаем модели
+# Линейная регрессия
+lr = LinearRegression()
+lr.fit(X_train, y_train)
+y_lr_pred = lr.predict(X_test)
+r2_lr = r2_score(y_test, y_lr_pred)
+
+# Полиномиальная регрессия с полиномом 3-й степени
+poly = PolynomialFeatures(degree=3)
+X_poly = poly.fit_transform(X_train)
+lr_poly = LinearRegression()
+lr_poly.fit(X_poly, y_train)
+X_test_poly = poly.transform(X_test)
+y_poly_pred = lr_poly.predict(X_test_poly)
+r2_poly = r2_score(y_test, y_poly_pred)
+
+# Гребневая полиномиальная регрессия с полиномом 3-й степени и alpha = 1.0
+ridge = Ridge(alpha=1.0)
+ridge.fit(X_poly, y_train)
+y_ridge_pred = ridge.predict(X_test_poly)
+r2_ridge = r2_score(y_test, y_ridge_pred)
+
+# Функция для отображения точек на графике
+def plot_with_labels(X, y, title, xlabel, ylabel):
+    plt.figure(figsize=(12, 6))
+    plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis', marker='.', label='Тестовые данные')
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='viridis', marker='o', edgecolors='black', linewidths=0.5, label='Обучающие данные')
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.legend()
+    plt.show()
+
+
+# График для линейной регрессии
+plot_with_labels(X_test, y_lr_pred, 'Линейная регрессия', 'Признак 1', 'Признак 2')
+print(f'Линейная регрессия - Точность: {r2_lr:.2f}')
+
+# График для полиномиальной регрессии
+plot_with_labels(X_test, y_poly_pred, 'Полиномиальная регрессия', 'Признак 1', 'Признак 2')
+print(f'Полиномиальная регрессия - Точность: {r2_poly:.2f}')
+
+# График для гребневой полиномиальной регрессии
+plot_with_labels(X_test, y_ridge_pred, 'Гребневая полиномиальная регрессия', 'Признак 1', 'Признак 2')
+print(f'Гребневая полиномиальная регрессия - Точность: {r2_ridge:.2f}')
--- a/sergeev_evgenii_lab_3/decision_tree.png
+++ b/sergeev_evgenii_lab_3/decision_tree.png
--- a/sergeev_evgenii_lab_3/lab3.py
+++ b/sergeev_evgenii_lab_3/lab3.py
@@ -0,0 +1,76 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+from sklearn.tree import DecisionTreeClassifier, export_graphviz, plot_tree
+
+df = pd.read_csv("hotel_bookings_raw.csv", delimiter=',')
+
+
+# Тип питания в зависимости от инфляции и потребительских настроений по отношению к экономике
+# Функция для отображения дерева решений
+def tree_visual():
+    # Преобразование типов питания к числам
+    # Инициализация LabelEncoder
+    label_encoder = LabelEncoder()
+
+    # Преобразование столбцов в числовые значения для всего фрейма
+    # list_col = ['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'reserved_room_type', 'deposit_type', 'customer_type', 'reservation_status', 'assigned_room_type']
+    # for i in list_col:
+    #     df[i] = label_encoder.fit_transform(df[i])
+    #df[list_col] = label_encoder.fit_transform(df[list_col])
+
+    #для всего фрейма
+    # mean_value = round(df['agent'].mean())
+    # df['agent'].fillna(mean_value, inplace=True)
+
+    # Количество элементов для обучения (99%)
+    count_to_train = round(len(df) * 0.99)
+    # Количество элементов для тестирования (1%)
+    count_to_test = len(df) - count_to_train
+
+    # Набор данных для обучения для всего фрейма
+    # train_df = df.head(count_to_train).copy().drop(columns=['reservation_status_date', 'MO_YR'])
+    train_df = df.head(count_to_train).copy()
+
+    # Тип питания
+    y = train_df.copy()['meal']
+
+    # Уровень инфляции и потребительские настроения по отношению к экономике для всего фрейма
+    # x = train_df.copy().drop(columns=['meal'])
+    x = train_df.copy()[['INFLATION', 'CSMR_SENT']]
+    # Создание модели дерева решений
+    model = DecisionTreeClassifier()
+
+    # Обучение модели
+    model.fit(x, y)
+
+    # Проверка модели для всего фрейма
+    #test_df = df.tail(count_to_test).copy().drop(columns=['reservation_status_date', 'MO_YR'])
+    test_df = df.tail(count_to_test)[['INFLATION', 'CSMR_SENT', 'meal']]
+
+    y_test = test_df.copy()['meal']
+    x_test = test_df.copy().drop(columns=['meal'])
+
+    prediction = model.score(x_test, y_test)
+
+    print('Качество дерева решений: ', prediction * 100, '%')
+    # Визуализация дерева решений
+    plt.figure(figsize=(12, 8))
+    plot_tree(model, feature_names=['INFLATION', 'CSMR_SENT'], filled=True)
+
+    # Сохранение графика в файл .png
+    plt.savefig('decision_tree.png', dpi=300)
+    plt.show()
+
+    res = sorted(dict(zip(list(x.columns), model.feature_importances_)).items(),
+                 key=lambda el: el[1], reverse=True)
+
+    flag = 0
+    print('feature importance:')
+    for val in res:
+        print(val[0]+" - "+str(val[1]))
+
+    return
+
+
+tree_visual()
--- a/sergeev_evgenii_lab_3/readme.md
+++ b/sergeev_evgenii_lab_3/readme.md
@@ -0,0 +1,23 @@
+# Лабораторная работа 3. Деревья решений
+## Задание
+1. По данным курсовой работы с помощью дерева решений
+решить выбранную задачу: классификация - зависимость типа обеда
+от инфляции и потребительских настроений по отношению к экономике
+
+Ссылка на мой датасет: https://www.kaggle.com/datasets/mlardi/hotel-booking-demand-with-economic-indicators
+### Запуск программы
+Файл lab3.py содержит и запускает программу.
+
+### Описание программы
+Программа состоит из двух частей:
+1. Она считывает файл с данными о двух отелях: City Hotel и Resort Hotel. Содержит множество различных метрик
+2. Далее загружает необходимые столбцы и проводит создание модели
+3. Выводит % правильных предсказаний и создает изображение дерева решений
+### Результаты тестирования
+По результатам тестирования, можно сказать следующее:
+
+* Дерево решений показывает неплохие результаты, 66%.
+* Оценка важности признаков показывает наиболее важным признаком уровень инфляции, затем уже уровень потребительских настроений по отношению к экономике
+* В самом датафрейме есть еще много различных признаков, но они не так сильно влияют на дерево решений
+
+Итого. Дерево решений даёт неплохие результаты при классификации.
--- a/zhukova_alina_lab_1/flask-server.py
+++ b/zhukova_alina_lab_1/flask-server.py
@@ -0,0 +1,194 @@
+import array
+import math
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.colors import ListedColormap
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import make_moons, make_circles, make_classification
+from sklearn.neural_network import MLPClassifier
+from sklearn.linear_model import LinearRegression, Lasso, Ridge, Perceptron
+from sklearn.metrics import accuracy_score
+
+from flask import Flask
+
+app = Flask(__name__)
+
+@app.route("/")
+def home():
+    return "<html>" \
+           "<h1>Жукова Алина ПИбд-41</h1>" \
+           "<h1>Лабораторная работа №1</h1>" \
+           "<table>" \
+           "<td>" \
+           "<form Action='http://127.0.0.1:5000/k4_1_task_1' Method=get>" \
+           "<input type=submit value='Работа с типовыми наборами данных и различными моделями'>" \
+           "</form>" \
+           "</td>" \
+           "</table>" \
+           "</html>"
+
+
+# Работа с типовыми наборами данных и различными моделями
+# сгенерируйте определенный тип данных и сравните на нем 3 модели
+# 10.Данные: make_moons (noise=0.3, random_state=rs)
+# Модели:
+# · Линейную регрессию
+# · Многослойный персептрон с 10-ю нейронами в скрытом слое (alpha = 0.01)
+# · Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)
+@app.route("/k4_1_task_1", methods=['GET'])
+def k4_1_task_1():
+    X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
+                               random_state=0, n_clusters_per_class=1)
+    rng = np.random.RandomState(2)
+    X += 2 + rng.uniform(size=X.shape)
+    linearly_dataset = (X, y)
+    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
+    cm_bright2 = ListedColormap(['#FF000066', '#0000FF66'])
+    moon_dataset = make_moons(noise=0.3, random_state=0)
+    circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
+    datasets = [moon_dataset, circles_dataset, linearly_dataset]
+
+    X, y = moon_dataset
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)
+
+    ridge_regression = Ridge(alpha=3, random_state=240)
+    ridge_regression.fit(X_train, y_train)
+    linear_accuracy = str(ridge_regression.score(X_test, y_test))
+
+    plt.subplot(1, 3, 1)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
+
+    x_min = moon_dataset[0][:, 0].min()
+    g_min = None
+    y_min = None
+    x_max = moon_dataset[0][:, 0].max()
+    g_max = None
+    y_max = None
+    for k in range(-21, 50):
+        elem = np.array([[x_min, k / 20]])
+        getted = ridge_regression.predict(elem)
+        if (g_min == None or math.fabs(0.5 - getted) < math.fabs(0.5 - g_min)):
+            g_min = getted
+            y_min = elem[0][1]
+        else:
+            if(math.fabs(0.5 - getted) > math.fabs(0.5 - g_min)):
+                break
+
+    for k in range(-21, 50):
+        elem = np.array([[x_max, k / 20]])
+        getted = ridge_regression.predict(elem)
+        if (g_max == None or math.fabs(0.5 - getted) < math.fabs(0.5 - g_max)):
+            g_max = getted
+            y_max = elem[0][1]
+        else:
+            if(math.fabs(0.5 - getted) > math.fabs(0.5 - g_max)):
+                break
+
+    x = ridge_regression.predict(X_test)
+    plt.plot([x_min, x_max], [y_min, y_max], label="line", color="yellow")
+    # plt.show()
+
+    # Перцептрон 10 скрытых слоев
+    perceptr = MLPClassifier(random_state=1, max_iter=2000, n_iter_no_change=20, activation="tanh",
+                             alpha=0.01, hidden_layer_sizes=[10,], tol=0.00000001)
+    perceptr.fit(X_train, y_train)
+    prediction = perceptr.predict(X_test)
+    perceptron_accuracy = str(accuracy_score(y_test, prediction))
+    prediction = perceptr.predict(moon_dataset[0])
+    perceptron_accuracy_all = str(accuracy_score(moon_dataset[1], prediction))
+
+    params_set = []
+    y_elem = None
+    g_elem = None
+
+    for data_elem in moon_dataset[0]:
+        for k in range(-21, 50):
+            elem = np.array([[data_elem[0], k / 20]])
+            getted = perceptr.predict(elem)
+            if (g_elem == None and getted == 0):
+                params_set.append([data_elem[0], -21 / 20])
+                g_elem = None
+            else:
+                if(getted == 1 and (getted == g_elem or g_elem == None)):
+                    g_elem = getted
+                    y_elem = elem[0][1]
+                else:
+                    params_set.append([data_elem[0], y_elem])
+                    g_elem = None
+                    break
+
+        if (g_elem != None):
+            params_set.append([data_elem[0], 50 / 20])
+            g_elem = None
+
+
+    params_set.sort()
+    params_set = np.array(params_set)
+    plt.subplot(1, 3, 2)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
+    plt.plot(params_set[:, 0], params_set[:, 1], label="line", color="yellow")
+    # plt.show()
+
+    # Перцептрон 100 скрытых слоев
+    perceptr100 = MLPClassifier(random_state=1, max_iter=2000, n_iter_no_change=20, activation="tanh",
+                             alpha=0.01, hidden_layer_sizes=[100, ], tol=0.00000001)
+    perceptr100.fit(X_train, y_train)
+    prediction = perceptr100.predict(X_test)
+    perceptron100_accuracy = str(accuracy_score(y_test, prediction))
+    prediction = perceptr100.predict(moon_dataset[0])
+    perceptron100_accuracy_all = str(accuracy_score(moon_dataset[1], prediction))
+
+    params_set = []
+    y_elem = None
+    g_elem = None
+
+    for data_elem in moon_dataset[0]:
+        for k in range(-21, 30):
+            elem = np.array([[data_elem[0], k / 20]])
+            getted = perceptr100.predict(elem)
+            if (g_elem == None and getted == 0):
+                params_set.append([data_elem[0], -21 / 20])
+                g_elem = None
+            else:
+                if(getted == 1 and (getted == g_elem or g_elem == None)):
+                    g_elem = getted
+                    y_elem = elem[0][1]
+                else:
+                    params_set.append([data_elem[0], y_elem])
+                    g_elem = None
+                    break
+
+        if (g_elem != None):
+            params_set.append([data_elem[0], 30 / 20])
+            g_elem = None
+
+
+    params_set.sort()
+    params_set = np.array(params_set)
+    plt.subplot(1, 3, 3)
+    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
+    plt.plot(params_set[:, 0], params_set[:, 1], label="line", color="yellow")
+    plt.show()
+
+
+    return  "<html>" \
+            "<h1>Работа с типовыми наборами данных и различными моделями</h1>" \
+            "<h2>Вариант 10. Данные: make_moons (noise=0.3, random_state=rs)</h2>" \
+            "<h2>Модели:\n 1) Линейная регрессия" \
+            "\n 2) Многослойный персептрон с 10-ю нейронами в скрытом слое (alpha = 0.01)" \
+            "\n 3) Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)</h2>" \
+            "<h2>Оценка точности линейной регрессии: " + linear_accuracy + "</h2>" \
+            "<h2>Оценка точности (тестовые данные) перцептрона 10 нейронов в скрытом слое: " + perceptron_accuracy + "</h2>" \
+            "<h2>Оценка точности (тестовые данные) перцептрона 100 нейронов в скрытом слое: " + perceptron100_accuracy + "</h2>" \
+            "<h2>Оценка точности (все точки) перцептрона 10 нейронов в скрытом слое: " + perceptron_accuracy_all + "</h2>" \
+            "<h2>Оценка точности (все точки) перцептрона 100 нейронов в скрытом слое: " + perceptron100_accuracy_all + "</h2>" \
+            "</html>"
+
+
+if __name__ == "__main__":
+    app.run(debug=True)
--- a/zhukova_alina_lab_1/img_screen_1.png
+++ b/zhukova_alina_lab_1/img_screen_1.png
--- a/zhukova_alina_lab_1/img_screen_2.png
+++ b/zhukova_alina_lab_1/img_screen_2.png
--- a/zhukova_alina_lab_1/img_screen_3.png
+++ b/zhukova_alina_lab_1/img_screen_3.png
--- a/zhukova_alina_lab_1/readme.md
+++ b/zhukova_alina_lab_1/readme.md
@@ -0,0 +1,60 @@
+## Задание
+Работа с типовыми наборами данных и различными моделями.
+Сгенерируйте определенный тип данных и сравните на нем 3 модели
+
+Вариант №10
+
+Данные: make_moons (noise=0.3, random_state=rs)
+
+Модели:
+ Линейная регрессия
+ Многослойный персептрон с 10-ю нейронами в скрытом слое (alpha = 0.01)
+ Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)
+
+## Используемые технологии
+В лабораторной были использованы библиотеки:
+ numpy - позволяет работать с массивами и матрицами
+ matplotlib - используется для создания графиков
+ sklearn - используется для работы с моделями машинного обучения
+ Flask - предоставляет способ быстрого создания веб-страниц для визуализации работы приложения
+
+## Как запустить
+Запустить файл flask-server, который поднимет локальный сервер 
+и позволит обратиться к программе через браузер по ссылке [http://127.0.0.1:5000/](http://127.0.0.1:5000/)
+
+## Что делает программа
+Генерирует набор данных типа луны (moons), делит его на обучающую и тестовую выборки.
+По очереди обучает на данных обучающей выборки 3 модели:
+модель линейной регрессии, модель многослойного перцептрона с 10 нейронами в скрытом слое и 
+модель многослойного перцептрона со 100 нейронами в скрытом слое.
+
+После обучения проверяются предсказания моделей на тестовых данных. Строится три графика, по одному для каждой модели, 
+где `#FF0000`, `#0000FF` - точки обучающей выборки первого и второго типа.
+
+`#FF000066`, `#0000FF66` - точки тестовой выборки первого и второго типа
+
+`#FFFF00` - линия по которой модель разделила данные на группы
+
+Далее программа выведет оценки точности моделей. Полученные оценки:
+ Линейная регрессия - 0.68
+ Перцептрон с 10 нейронами в скрытом слое - 0.95
+ Перцоптрон со 100 нейронами в скрытом слое - 0.95
+
+Так как для двух последних моделей оценки оказались одинаковы, 
+я сравнила их точность на всех данных, а не только на тестовой выборке.
+
+ Точность Перцептрона с 10 нейронами в скрытом слое - 0.91
+ Точность Перцептрона со 100 нейронами в скрытом слое - 0.95
+
+## Скриншоты работы программы
+Главная страница в браузере (доступ по ссылке [http://127.0.0.1:5000/](http://127.0.0.1:5000/))
+![img.png](img_screen_1.png)
+
+Полученные графики разбиения точек на классы 
+
+Линейная регрессия - Перцептрон 10 нейронов - Перцептрон 100 нейронов 
+![img.png](img_screen_2.png)
+
+Вывод анализа точности работы моделей 
+![img.png](img_screen_3.png)
+
Author	SHA1	Message	Date
sergeevevgen	0d1e5a83f4	done	2023-11-06 23:11:22 +04:00
Евгений Сергеев	c4bd132891	+	2023-10-29 21:02:40 +04:00
Alexey	d575910860	Merge pull request 'gusev_vladislav_lab_3' (#62 ) from gusev_vladislav_lab_3 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/62	2023-10-24 16:48:23 +04:00
Alexey	5894881f24	Merge pull request 'abanin_daniil_lab_3' (#74 ) from abanin_daniil_lab_3 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/74	2023-10-24 16:48:10 +04:00
Alexey	92ec657bcd	Merge pull request 'ilbekov_dmitriy_lab_3' (#76 ) from ilbekov_dmitriy_lab_3 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/76	2023-10-24 16:47:50 +04:00
Alexey	346241253f	Merge pull request 'zhukova_alina_lab_1 is ready' (#64 ) from zhukova_alina_lab_1 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/64	2023-10-24 16:38:21 +04:00
Alexey	65b47c7d0e	Merge pull request 'kurmyza_pavel_lab_2 is ready' (#77 ) from kurmyza_pavel_lab_2 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/77	2023-10-24 12:47:55 +04:00
Alexey	f7af263316	Merge pull request 'belyaeva lab2 ready' (#68 ) from belyaeva_ekaterina_lab_2 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/68	2023-10-24 12:32:43 +04:00
Alexey	c45de91019	Merge pull request 'kurmyza_pavel_lab_1 is ready' (#75 ) from kurmyza_pavel_lab_1 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/75	2023-10-24 11:36:28 +04:00
Alexey	4fad5585c1	Merge pull request 'basharin_sevastyan_lab_1 is ready' (#73 ) from basharin_sevastyan_lab_1 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/73	2023-10-24 11:36:11 +04:00
Alexey	c9d485daca	Merge pull request 'senkin_alexander_lab_1 is ready' (#66 ) from senkin_alexander_lab_1 into main Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/66	2023-10-24 11:27:59 +04:00
tmrrwnxtsn	200d8dee7e	kurmyza_pavel_lab_2 is ready	2023-10-22 19:15:28 +04:00
Orthodoxal	4e1980e638	lab3 done	2023-10-22 18:42:36 +04:00
tmrrwnxtsn	a43eb72079	kurmyza_pavel_lab_1 is ready	2023-10-22 16:37:16 +04:00
BossMouseFire	464b437c69	lab3	2023-10-22 11:51:04 +04:00
acidmikk	0b422e70f9	basharin_sevastyan_lab_1 is ready	2023-10-20 22:11:35 +04:00
Kate	145b7336b8	belyaeva lab2 ready	2023-10-20 16:12:55 +04:00
Mamoru777	bea977d84c	Lab1	2023-10-19 23:39:38 +04:00
Alina5757	ad5ed23a4c	zhukova_alina_lab_1 is ready	2023-10-18 20:09:27 +04:00
vladg	226dd4efe9	gusev_vladislav_lab_3 is ready	2023-10-18 13:48:11 +04:00
vladg	c0217ad0d3	gusev_vladislav_lab_3 is ready	2023-10-18 13:47:11 +04:00
vladg	caab9f2f8b	gusev_vladislav_lab_3 is ready	2023-10-18 13:46:28 +04:00
vladg	d2580ffa9e	gusev_vladislav_lab_3 is ready	2023-10-18 13:14:11 +04:00