arzamaskina_milana_lab_4 is ready

Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/137

.gitignore

@@ -0,0 +1,141 @@
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+.idea

abanin_daniil_lab_4/README.md

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+## Лабораторная работа №4
+
+### Ранжирование признаков
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, pandas, matplotlib, scipy
+* запустить проект (стартовая точка lab4)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки pandas, matplotlib, scipy
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+Программа читает данные из csv файла. На основе имеющейся информации кластеризует заявителей на различные группы по риску выдачи кредита.
+При кластеризации используются такие признаки, как: ApplicantIncome - доход заявителя, LoanAmount - сумма займа в тысячах, Credit_History - 
+статус кредитной истории заявителя (соответствие рекомендациям), Self_Employed - самозанятость (Да/Нет), Education - наличие образования
+
+### Тест
+
+![Result](result.png)
+
+По результатам кластеризации дендрограммой видно, что было проведено эффективное разбиение данных. На диаграмме показаны различные группы заявителей по рискам выдачи кредита 

abanin_daniil_lab_4/lab4.py

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+from scipy.cluster import hierarchy
+import pandas as pd
+from matplotlib import pyplot as plt
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'LoanAmount', 'Credit_History', 'Self_Employed', 'Education']]
+    plt.figure(1, figsize=(16, 9))
+    plt.title('Дендрограмма кластеризации заявителей')
+
+    hierarchy.dendrogram(hierarchy.linkage(x, method='single'),
+                         truncate_mode='lastp',
+                         p=20,
+                         orientation='top',
+                         leaf_rotation=90,
+                         leaf_font_size=8,
+                         show_contracted=True)
+
+    plt.show()
+
+
+start()

abanin_daniil_lab_4/loan.csv

@@ -0,0 +1,615 @@
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+LP001508,Male,Yes,2,1,0.0,11757,0.0,187.0,180.0,1,Urban,1.0
+LP001514,Female,Yes,0,1,0.0,2330,4486.0,100.0,360.0,1,Semiurban,1.0
+LP001516,Female,Yes,2,1,0.0,14866,0.0,70.0,360.0,1,Urban,1.0
+LP001518,Male,Yes,1,1,0.0,1538,1425.0,30.0,360.0,1,Urban,1.0
+LP001519,Female,No,0,1,0.0,10000,1666.0,225.0,360.0,1,Rural,0.0
+LP001520,Male,Yes,0,1,0.0,4860,830.0,125.0,360.0,1,Semiurban,1.0
+LP001528,Male,No,0,1,0.0,6277,0.0,118.0,360.0,0,Rural,0.0
+LP001529,Male,Yes,0,1,1.0,2577,3750.0,152.0,360.0,1,Rural,1.0
+LP001531,Male,No,0,1,0.0,9166,0.0,244.0,360.0,1,Urban,0.0
+LP001532,Male,Yes,2,0,0.0,2281,0.0,113.0,360.0,1,Rural,0.0
+LP001535,Male,No,0,1,0.0,3254,0.0,50.0,360.0,1,Urban,1.0
+LP001536,Male,Yes,3+,1,0.0,39999,0.0,600.0,180.0,0,Semiurban,1.0
+LP001541,Male,Yes,1,1,0.0,6000,0.0,160.0,360.0,0,Rural,1.0
+LP001543,Male,Yes,1,1,0.0,9538,0.0,187.0,360.0,1,Urban,1.0
+LP001546,Male,No,0,1,0.0,2980,2083.0,120.0,360.0,1,Rural,1.0
+LP001552,Male,Yes,0,1,0.0,4583,5625.0,255.0,360.0,1,Semiurban,1.0
+LP001560,Male,Yes,0,0,0.0,1863,1041.0,98.0,360.0,1,Semiurban,1.0
+LP001562,Male,Yes,0,1,0.0,7933,0.0,275.0,360.0,1,Urban,0.0
+LP001565,Male,Yes,1,1,0.0,3089,1280.0,121.0,360.0,0,Semiurban,0.0
+LP001570,Male,Yes,2,1,0.0,4167,1447.0,158.0,360.0,1,Rural,1.0
+LP001572,Male,Yes,0,1,0.0,9323,0.0,75.0,180.0,1,Urban,1.0
+LP001574,Male,Yes,0,1,0.0,3707,3166.0,182.0,,1,Rural,1.0
+LP001577,Female,Yes,0,1,0.0,4583,0.0,112.0,360.0,1,Rural,0.0
+LP001578,Male,Yes,0,1,0.0,2439,3333.0,129.0,360.0,1,Rural,1.0
+LP001579,Male,No,0,1,0.0,2237,0.0,63.0,480.0,0,Semiurban,0.0
+LP001580,Male,Yes,2,1,0.0,8000,0.0,200.0,360.0,1,Semiurban,1.0
+LP001581,Male,Yes,0,0,0.0,1820,1769.0,95.0,360.0,1,Rural,1.0
+LP001585,,Yes,3+,1,0.0,51763,0.0,700.0,300.0,1,Urban,1.0
+LP001586,Male,Yes,3+,0,0.0,3522,0.0,81.0,180.0,1,Rural,0.0
+LP001594,Male,Yes,0,1,0.0,5708,5625.0,187.0,360.0,1,Semiurban,1.0
+LP001603,Male,Yes,0,0,1.0,4344,736.0,87.0,360.0,1,Semiurban,0.0
+LP001606,Male,Yes,0,1,0.0,3497,1964.0,116.0,360.0,1,Rural,1.0
+LP001608,Male,Yes,2,1,0.0,2045,1619.0,101.0,360.0,1,Rural,1.0
+LP001610,Male,Yes,3+,1,0.0,5516,11300.0,495.0,360.0,0,Semiurban,0.0
+LP001616,Male,Yes,1,1,0.0,3750,0.0,116.0,360.0,1,Semiurban,1.0
+LP001630,Male,No,0,0,0.0,2333,1451.0,102.0,480.0,0,Urban,0.0
+LP001633,Male,Yes,1,1,0.0,6400,7250.0,180.0,360.0,0,Urban,0.0
+LP001634,Male,No,0,1,0.0,1916,5063.0,67.0,360.0,0,Rural,0.0
+LP001636,Male,Yes,0,1,0.0,4600,0.0,73.0,180.0,1,Semiurban,1.0
+LP001637,Male,Yes,1,1,0.0,33846,0.0,260.0,360.0,1,Semiurban,0.0
+LP001639,Female,Yes,0,1,0.0,3625,0.0,108.0,360.0,1,Semiurban,1.0
+LP001640,Male,Yes,0,1,1.0,39147,4750.0,120.0,360.0,1,Semiurban,1.0
+LP001641,Male,Yes,1,1,1.0,2178,0.0,66.0,300.0,0,Rural,0.0
+LP001643,Male,Yes,0,1,0.0,2383,2138.0,58.0,360.0,0,Rural,1.0
+LP001644,,Yes,0,1,1.0,674,5296.0,168.0,360.0,1,Rural,1.0
+LP001647,Male,Yes,0,1,0.0,9328,0.0,188.0,180.0,1,Rural,1.0
+LP001653,Male,No,0,0,0.0,4885,0.0,48.0,360.0,1,Rural,1.0
+LP001656,Male,No,0,1,0.0,12000,0.0,164.0,360.0,1,Semiurban,0.0
+LP001657,Male,Yes,0,0,0.0,6033,0.0,160.0,360.0,1,Urban,0.0
+LP001658,Male,No,0,1,0.0,3858,0.0,76.0,360.0,1,Semiurban,1.0
+LP001664,Male,No,0,1,0.0,4191,0.0,120.0,360.0,1,Rural,1.0
+LP001665,Male,Yes,1,1,0.0,3125,2583.0,170.0,360.0,1,Semiurban,0.0
+LP001666,Male,No,0,1,0.0,8333,3750.0,187.0,360.0,1,Rural,1.0
+LP001669,Female,No,0,0,0.0,1907,2365.0,120.0,,1,Urban,1.0
+LP001671,Female,Yes,0,1,0.0,3416,2816.0,113.0,360.0,0,Semiurban,1.0
+LP001673,Male,No,0,1,1.0,11000,0.0,83.0,360.0,1,Urban,0.0
+LP001674,Male,Yes,1,0,0.0,2600,2500.0,90.0,360.0,1,Semiurban,1.0
+LP001677,Male,No,2,1,0.0,4923,0.0,166.0,360.0,0,Semiurban,1.0
+LP001682,Male,Yes,3+,0,0.0,3992,0.0,0.0,180.0,1,Urban,0.0
+LP001688,Male,Yes,1,0,0.0,3500,1083.0,135.0,360.0,1,Urban,1.0
+LP001691,Male,Yes,2,0,0.0,3917,0.0,124.0,360.0,1,Semiurban,1.0
+LP001692,Female,No,0,0,0.0,4408,0.0,120.0,360.0,1,Semiurban,1.0
+LP001693,Female,No,0,1,0.0,3244,0.0,80.0,360.0,1,Urban,1.0
+LP001698,Male,No,0,0,0.0,3975,2531.0,55.0,360.0,1,Rural,1.0
+LP001699,Male,No,0,1,0.0,2479,0.0,59.0,360.0,1,Urban,1.0
+LP001702,Male,No,0,1,0.0,3418,0.0,127.0,360.0,1,Semiurban,0.0
+LP001708,Female,No,0,1,0.0,10000,0.0,214.0,360.0,1,Semiurban,0.0
+LP001711,Male,Yes,3+,1,0.0,3430,1250.0,128.0,360.0,0,Semiurban,0.0
+LP001713,Male,Yes,1,1,1.0,7787,0.0,240.0,360.0,1,Urban,1.0
+LP001715,Male,Yes,3+,0,1.0,5703,0.0,130.0,360.0,1,Rural,1.0
+LP001716,Male,Yes,0,1,0.0,3173,3021.0,137.0,360.0,1,Urban,1.0
+LP001720,Male,Yes,3+,0,0.0,3850,983.0,100.0,360.0,1,Semiurban,1.0
+LP001722,Male,Yes,0,1,0.0,150,1800.0,135.0,360.0,1,Rural,0.0
+LP001726,Male,Yes,0,1,0.0,3727,1775.0,131.0,360.0,1,Semiurban,1.0
+LP001732,Male,Yes,2,1,0.0,5000,0.0,72.0,360.0,0,Semiurban,0.0
+LP001734,Female,Yes,2,1,0.0,4283,2383.0,127.0,360.0,0,Semiurban,1.0
+LP001736,Male,Yes,0,1,0.0,2221,0.0,60.0,360.0,0,Urban,0.0
+LP001743,Male,Yes,2,1,0.0,4009,1717.0,116.0,360.0,1,Semiurban,1.0
+LP001744,Male,No,0,1,0.0,2971,2791.0,144.0,360.0,1,Semiurban,1.0
+LP001749,Male,Yes,0,1,0.0,7578,1010.0,175.0,,1,Semiurban,1.0
+LP001750,Male,Yes,0,1,0.0,6250,0.0,128.0,360.0,1,Semiurban,1.0
+LP001751,Male,Yes,0,1,0.0,3250,0.0,170.0,360.0,1,Rural,0.0
+LP001754,Male,Yes,,0,1.0,4735,0.0,138.0,360.0,1,Urban,0.0
+LP001758,Male,Yes,2,1,0.0,6250,1695.0,210.0,360.0,1,Semiurban,1.0
+LP001760,Male,,,1,0.0,4758,0.0,158.0,480.0,1,Semiurban,1.0
+LP001761,Male,No,0,1,1.0,6400,0.0,200.0,360.0,1,Rural,1.0
+LP001765,Male,Yes,1,1,0.0,2491,2054.0,104.0,360.0,1,Semiurban,1.0
+LP001768,Male,Yes,0,1,0.0,3716,0.0,42.0,180.0,1,Rural,1.0
+LP001770,Male,No,0,0,0.0,3189,2598.0,120.0,,1,Rural,1.0
+LP001776,Female,No,0,1,0.0,8333,0.0,280.0,360.0,1,Semiurban,1.0
+LP001778,Male,Yes,1,1,0.0,3155,1779.0,140.0,360.0,1,Semiurban,1.0
+LP001784,Male,Yes,1,1,0.0,5500,1260.0,170.0,360.0,1,Rural,1.0
+LP001786,Male,Yes,0,1,0.0,5746,0.0,255.0,360.0,0,Urban,0.0
+LP001788,Female,No,0,1,1.0,3463,0.0,122.0,360.0,0,Urban,1.0
+LP001790,Female,No,1,1,0.0,3812,0.0,112.0,360.0,1,Rural,1.0
+LP001792,Male,Yes,1,1,0.0,3315,0.0,96.0,360.0,1,Semiurban,1.0
+LP001798,Male,Yes,2,1,0.0,5819,5000.0,120.0,360.0,1,Rural,1.0
+LP001800,Male,Yes,1,0,0.0,2510,1983.0,140.0,180.0,1,Urban,0.0
+LP001806,Male,No,0,1,0.0,2965,5701.0,155.0,60.0,1,Urban,1.0
+LP001807,Male,Yes,2,1,1.0,6250,1300.0,108.0,360.0,1,Rural,1.0
+LP001811,Male,Yes,0,0,0.0,3406,4417.0,123.0,360.0,1,Semiurban,1.0
+LP001813,Male,No,0,1,1.0,6050,4333.0,120.0,180.0,1,Urban,0.0
+LP001814,Male,Yes,2,1,0.0,9703,0.0,112.0,360.0,1,Urban,1.0
+LP001819,Male,Yes,1,0,0.0,6608,0.0,137.0,180.0,1,Urban,1.0
+LP001824,Male,Yes,1,1,0.0,2882,1843.0,123.0,480.0,1,Semiurban,1.0
+LP001825,Male,Yes,0,1,0.0,1809,1868.0,90.0,360.0,1,Urban,1.0
+LP001835,Male,Yes,0,0,0.0,1668,3890.0,201.0,360.0,0,Semiurban,0.0
+LP001836,Female,No,2,1,0.0,3427,0.0,138.0,360.0,1,Urban,0.0
+LP001841,Male,No,0,0,1.0,2583,2167.0,104.0,360.0,1,Rural,1.0
+LP001843,Male,Yes,1,0,0.0,2661,7101.0,279.0,180.0,1,Semiurban,1.0
+LP001844,Male,No,0,1,1.0,16250,0.0,192.0,360.0,0,Urban,0.0
+LP001846,Female,No,3+,1,0.0,3083,0.0,255.0,360.0,1,Rural,1.0
+LP001849,Male,No,0,0,0.0,6045,0.0,115.0,360.0,0,Rural,0.0
+LP001854,Male,Yes,3+,1,0.0,5250,0.0,94.0,360.0,1,Urban,0.0
+LP001859,Male,Yes,0,1,0.0,14683,2100.0,304.0,360.0,1,Rural,0.0
+LP001864,Male,Yes,3+,0,0.0,4931,0.0,128.0,360.0,0,Semiurban,0.0
+LP001865,Male,Yes,1,1,0.0,6083,4250.0,330.0,360.0,0,Urban,1.0
+LP001868,Male,No,0,1,0.0,2060,2209.0,134.0,360.0,1,Semiurban,1.0
+LP001870,Female,No,1,1,0.0,3481,0.0,155.0,36.0,1,Semiurban,0.0
+LP001871,Female,No,0,1,0.0,7200,0.0,120.0,360.0,1,Rural,1.0
+LP001872,Male,No,0,1,1.0,5166,0.0,128.0,360.0,1,Semiurban,1.0
+LP001875,Male,No,0,1,0.0,4095,3447.0,151.0,360.0,1,Rural,1.0
+LP001877,Male,Yes,2,1,0.0,4708,1387.0,150.0,360.0,1,Semiurban,1.0
+LP001882,Male,Yes,3+,1,0.0,4333,1811.0,160.0,360.0,0,Urban,1.0
+LP001883,Female,No,0,1,0.0,3418,0.0,135.0,360.0,1,Rural,0.0
+LP001884,Female,No,1,1,0.0,2876,1560.0,90.0,360.0,1,Urban,1.0
+LP001888,Female,No,0,1,0.0,3237,0.0,30.0,360.0,1,Urban,1.0
+LP001891,Male,Yes,0,1,0.0,11146,0.0,136.0,360.0,1,Urban,1.0
+LP001892,Male,No,0,1,0.0,2833,1857.0,126.0,360.0,1,Rural,1.0
+LP001894,Male,Yes,0,1,0.0,2620,2223.0,150.0,360.0,1,Semiurban,1.0
+LP001896,Male,Yes,2,1,0.0,3900,0.0,90.0,360.0,1,Semiurban,1.0
+LP001900,Male,Yes,1,1,0.0,2750,1842.0,115.0,360.0,1,Semiurban,1.0
+LP001903,Male,Yes,0,1,0.0,3993,3274.0,207.0,360.0,1,Semiurban,1.0
+LP001904,Male,Yes,0,1,0.0,3103,1300.0,80.0,360.0,1,Urban,1.0
+LP001907,Male,Yes,0,1,0.0,14583,0.0,436.0,360.0,1,Semiurban,1.0
+LP001908,Female,Yes,0,0,0.0,4100,0.0,124.0,360.0,0,Rural,1.0
+LP001910,Male,No,1,0,1.0,4053,2426.0,158.0,360.0,0,Urban,0.0
+LP001914,Male,Yes,0,1,0.0,3927,800.0,112.0,360.0,1,Semiurban,1.0
+LP001915,Male,Yes,2,1,0.0,2301,985.7999878,78.0,180.0,1,Urban,1.0
+LP001917,Female,No,0,1,0.0,1811,1666.0,54.0,360.0,1,Urban,1.0
+LP001922,Male,Yes,0,1,0.0,20667,0.0,0.0,360.0,1,Rural,0.0
+LP001924,Male,No,0,1,0.0,3158,3053.0,89.0,360.0,1,Rural,1.0
+LP001925,Female,No,0,1,1.0,2600,1717.0,99.0,300.0,1,Semiurban,0.0
+LP001926,Male,Yes,0,1,0.0,3704,2000.0,120.0,360.0,1,Rural,1.0
+LP001931,Female,No,0,1,0.0,4124,0.0,115.0,360.0,1,Semiurban,1.0
+LP001935,Male,No,0,1,0.0,9508,0.0,187.0,360.0,1,Rural,1.0
+LP001936,Male,Yes,0,1,0.0,3075,2416.0,139.0,360.0,1,Rural,1.0
+LP001938,Male,Yes,2,1,0.0,4400,0.0,127.0,360.0,0,Semiurban,0.0
+LP001940,Male,Yes,2,1,0.0,3153,1560.0,134.0,360.0,1,Urban,1.0
+LP001945,Female,No,,1,0.0,5417,0.0,143.0,480.0,0,Urban,0.0
+LP001947,Male,Yes,0,1,0.0,2383,3334.0,172.0,360.0,1,Semiurban,1.0
+LP001949,Male,Yes,3+,1,0.0,4416,1250.0,110.0,360.0,1,Urban,1.0
+LP001953,Male,Yes,1,1,0.0,6875,0.0,200.0,360.0,1,Semiurban,1.0
+LP001954,Female,Yes,1,1,0.0,4666,0.0,135.0,360.0,1,Urban,1.0
+LP001955,Female,No,0,1,0.0,5000,2541.0,151.0,480.0,1,Rural,0.0
+LP001963,Male,Yes,1,1,0.0,2014,2925.0,113.0,360.0,1,Urban,0.0
+LP001964,Male,Yes,0,0,0.0,1800,2934.0,93.0,360.0,0,Urban,0.0
+LP001972,Male,Yes,,0,0.0,2875,1750.0,105.0,360.0,1,Semiurban,1.0
+LP001974,Female,No,0,1,0.0,5000,0.0,132.0,360.0,1,Rural,1.0
+LP001977,Male,Yes,1,1,0.0,1625,1803.0,96.0,360.0,1,Urban,1.0
+LP001978,Male,No,0,1,0.0,4000,2500.0,140.0,360.0,1,Rural,1.0
+LP001990,Male,No,0,0,0.0,2000,0.0,0.0,360.0,1,Urban,0.0
+LP001993,Female,No,0,1,0.0,3762,1666.0,135.0,360.0,1,Rural,1.0
+LP001994,Female,No,0,1,0.0,2400,1863.0,104.0,360.0,0,Urban,0.0
+LP001996,Male,No,0,1,0.0,20233,0.0,480.0,360.0,1,Rural,0.0
+LP001998,Male,Yes,2,0,0.0,7667,0.0,185.0,360.0,0,Rural,1.0
+LP002002,Female,No,0,1,0.0,2917,0.0,84.0,360.0,1,Semiurban,1.0
+LP002004,Male,No,0,0,0.0,2927,2405.0,111.0,360.0,1,Semiurban,1.0
+LP002006,Female,No,0,1,0.0,2507,0.0,56.0,360.0,1,Rural,1.0
+LP002008,Male,Yes,2,1,1.0,5746,0.0,144.0,84.0,0,Rural,1.0
+LP002024,,Yes,0,1,0.0,2473,1843.0,159.0,360.0,1,Rural,0.0
+LP002031,Male,Yes,1,0,0.0,3399,1640.0,111.0,180.0,1,Urban,1.0
+LP002035,Male,Yes,2,1,0.0,3717,0.0,120.0,360.0,1,Semiurban,1.0
+LP002036,Male,Yes,0,1,0.0,2058,2134.0,88.0,360.0,0,Urban,1.0
+LP002043,Female,No,1,1,0.0,3541,0.0,112.0,360.0,0,Semiurban,1.0
+LP002050,Male,Yes,1,1,1.0,10000,0.0,155.0,360.0,1,Rural,0.0
+LP002051,Male,Yes,0,1,0.0,2400,2167.0,115.0,360.0,1,Semiurban,1.0
+LP002053,Male,Yes,3+,1,0.0,4342,189.0,124.0,360.0,1,Semiurban,1.0
+LP002054,Male,Yes,2,0,0.0,3601,1590.0,0.0,360.0,1,Rural,1.0
+LP002055,Female,No,0,1,0.0,3166,2985.0,132.0,360.0,0,Rural,1.0
+LP002065,Male,Yes,3+,1,0.0,15000,0.0,300.0,360.0,1,Rural,1.0
+LP002067,Male,Yes,1,1,1.0,8666,4983.0,376.0,360.0,0,Rural,0.0
+LP002068,Male,No,0,1,0.0,4917,0.0,130.0,360.0,0,Rural,1.0
+LP002082,Male,Yes,0,1,1.0,5818,2160.0,184.0,360.0,1,Semiurban,1.0
+LP002086,Female,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002087,Female,No,0,1,0.0,2500,0.0,67.0,360.0,1,Urban,1.0
+LP002097,Male,No,1,1,0.0,4384,1793.0,117.0,360.0,1,Urban,1.0
+LP002098,Male,No,0,1,0.0,2935,0.0,98.0,360.0,1,Semiurban,1.0
+LP002100,Male,No,,1,0.0,2833,0.0,71.0,360.0,1,Urban,1.0
+LP002101,Male,Yes,0,1,0.0,63337,0.0,490.0,180.0,1,Urban,1.0
+LP002103,,Yes,1,1,1.0,9833,1833.0,182.0,180.0,1,Urban,1.0
+LP002106,Male,Yes,,1,1.0,5503,4490.0,70.0,,1,Semiurban,1.0
+LP002110,Male,Yes,1,1,0.0,5250,688.0,160.0,360.0,1,Rural,1.0
+LP002112,Male,Yes,2,1,1.0,2500,4600.0,176.0,360.0,1,Rural,1.0
+LP002113,Female,No,3+,0,0.0,1830,0.0,0.0,360.0,0,Urban,0.0
+LP002114,Female,No,0,1,0.0,4160,0.0,71.0,360.0,1,Semiurban,1.0
+LP002115,Male,Yes,3+,0,0.0,2647,1587.0,173.0,360.0,1,Rural,0.0
+LP002116,Female,No,0,1,0.0,2378,0.0,46.0,360.0,1,Rural,0.0
+LP002119,Male,Yes,1,0,0.0,4554,1229.0,158.0,360.0,1,Urban,1.0
+LP002126,Male,Yes,3+,0,0.0,3173,0.0,74.0,360.0,1,Semiurban,1.0
+LP002128,Male,Yes,2,1,0.0,2583,2330.0,125.0,360.0,1,Rural,1.0
+LP002129,Male,Yes,0,1,0.0,2499,2458.0,160.0,360.0,1,Semiurban,1.0
+LP002130,Male,Yes,,0,0.0,3523,3230.0,152.0,360.0,0,Rural,0.0
+LP002131,Male,Yes,2,0,0.0,3083,2168.0,126.0,360.0,1,Urban,1.0
+LP002137,Male,Yes,0,1,0.0,6333,4583.0,259.0,360.0,0,Semiurban,1.0
+LP002138,Male,Yes,0,1,0.0,2625,6250.0,187.0,360.0,1,Rural,1.0
+LP002139,Male,Yes,0,1,0.0,9083,0.0,228.0,360.0,1,Semiurban,1.0
+LP002140,Male,No,0,1,0.0,8750,4167.0,308.0,360.0,1,Rural,0.0
+LP002141,Male,Yes,3+,1,0.0,2666,2083.0,95.0,360.0,1,Rural,1.0
+LP002142,Female,Yes,0,1,1.0,5500,0.0,105.0,360.0,0,Rural,0.0
+LP002143,Female,Yes,0,1,0.0,2423,505.0,130.0,360.0,1,Semiurban,1.0
+LP002144,Female,No,,1,0.0,3813,0.0,116.0,180.0,1,Urban,1.0
+LP002149,Male,Yes,2,1,0.0,8333,3167.0,165.0,360.0,1,Rural,1.0
+LP002151,Male,Yes,1,1,0.0,3875,0.0,67.0,360.0,1,Urban,0.0
+LP002158,Male,Yes,0,0,0.0,3000,1666.0,100.0,480.0,0,Urban,0.0
+LP002160,Male,Yes,3+,1,0.0,5167,3167.0,200.0,360.0,1,Semiurban,1.0
+LP002161,Female,No,1,1,0.0,4723,0.0,81.0,360.0,1,Semiurban,0.0
+LP002170,Male,Yes,2,1,0.0,5000,3667.0,236.0,360.0,1,Semiurban,1.0
+LP002175,Male,Yes,0,1,0.0,4750,2333.0,130.0,360.0,1,Urban,1.0
+LP002178,Male,Yes,0,1,0.0,3013,3033.0,95.0,300.0,0,Urban,1.0
+LP002180,Male,No,0,1,1.0,6822,0.0,141.0,360.0,1,Rural,1.0
+LP002181,Male,No,0,0,0.0,6216,0.0,133.0,360.0,1,Rural,0.0
+LP002187,Male,No,0,1,0.0,2500,0.0,96.0,480.0,1,Semiurban,0.0
+LP002188,Male,No,0,1,0.0,5124,0.0,124.0,,0,Rural,0.0
+LP002190,Male,Yes,1,1,0.0,6325,0.0,175.0,360.0,1,Semiurban,1.0
+LP002191,Male,Yes,0,1,0.0,19730,5266.0,570.0,360.0,1,Rural,0.0
+LP002194,Female,No,0,1,1.0,15759,0.0,55.0,360.0,1,Semiurban,1.0
+LP002197,Male,Yes,2,1,0.0,5185,0.0,155.0,360.0,1,Semiurban,1.0
+LP002201,Male,Yes,2,1,1.0,9323,7873.0,380.0,300.0,1,Rural,1.0
+LP002205,Male,No,1,1,0.0,3062,1987.0,111.0,180.0,0,Urban,0.0
+LP002209,Female,No,0,1,0.0,2764,1459.0,110.0,360.0,1,Urban,1.0
+LP002211,Male,Yes,0,1,0.0,4817,923.0,120.0,180.0,1,Urban,1.0
+LP002219,Male,Yes,3+,1,0.0,8750,4996.0,130.0,360.0,1,Rural,1.0
+LP002223,Male,Yes,0,1,0.0,4310,0.0,130.0,360.0,0,Semiurban,1.0
+LP002224,Male,No,0,1,0.0,3069,0.0,71.0,480.0,1,Urban,0.0
+LP002225,Male,Yes,2,1,0.0,5391,0.0,130.0,360.0,1,Urban,1.0
+LP002226,Male,Yes,0,1,0.0,3333,2500.0,128.0,360.0,1,Semiurban,1.0
+LP002229,Male,No,0,1,0.0,5941,4232.0,296.0,360.0,1,Semiurban,1.0
+LP002231,Female,No,0,1,0.0,6000,0.0,156.0,360.0,1,Urban,1.0
+LP002234,Male,No,0,1,1.0,7167,0.0,128.0,360.0,1,Urban,1.0
+LP002236,Male,Yes,2,1,0.0,4566,0.0,100.0,360.0,1,Urban,0.0
+LP002237,Male,No,1,1,0.0,3667,0.0,113.0,180.0,1,Urban,1.0
+LP002239,Male,No,0,0,0.0,2346,1600.0,132.0,360.0,1,Semiurban,1.0
+LP002243,Male,Yes,0,0,0.0,3010,3136.0,0.0,360.0,0,Urban,0.0
+LP002244,Male,Yes,0,1,0.0,2333,2417.0,136.0,360.0,1,Urban,1.0
+LP002250,Male,Yes,0,1,0.0,5488,0.0,125.0,360.0,1,Rural,1.0
+LP002255,Male,No,3+,1,0.0,9167,0.0,185.0,360.0,1,Rural,1.0
+LP002262,Male,Yes,3+,1,0.0,9504,0.0,275.0,360.0,1,Rural,1.0
+LP002263,Male,Yes,0,1,0.0,2583,2115.0,120.0,360.0,0,Urban,1.0
+LP002265,Male,Yes,2,0,0.0,1993,1625.0,113.0,180.0,1,Semiurban,1.0
+LP002266,Male,Yes,2,1,0.0,3100,1400.0,113.0,360.0,1,Urban,1.0
+LP002272,Male,Yes,2,1,0.0,3276,484.0,135.0,360.0,0,Semiurban,1.0
+LP002277,Female,No,0,1,0.0,3180,0.0,71.0,360.0,0,Urban,0.0
+LP002281,Male,Yes,0,1,0.0,3033,1459.0,95.0,360.0,1,Urban,1.0
+LP002284,Male,No,0,0,0.0,3902,1666.0,109.0,360.0,1,Rural,1.0
+LP002287,Female,No,0,1,0.0,1500,1800.0,103.0,360.0,0,Semiurban,0.0
+LP002288,Male,Yes,2,0,0.0,2889,0.0,45.0,180.0,0,Urban,0.0
+LP002296,Male,No,0,0,0.0,2755,0.0,65.0,300.0,1,Rural,0.0
+LP002297,Male,No,0,1,0.0,2500,20000.0,103.0,360.0,1,Semiurban,1.0
+LP002300,Female,No,0,0,0.0,1963,0.0,53.0,360.0,1,Semiurban,1.0
+LP002301,Female,No,0,1,1.0,7441,0.0,194.0,360.0,1,Rural,0.0
+LP002305,Female,No,0,1,0.0,4547,0.0,115.0,360.0,1,Semiurban,1.0
+LP002308,Male,Yes,0,0,0.0,2167,2400.0,115.0,360.0,1,Urban,1.0
+LP002314,Female,No,0,0,0.0,2213,0.0,66.0,360.0,1,Rural,1.0
+LP002315,Male,Yes,1,1,0.0,8300,0.0,152.0,300.0,0,Semiurban,0.0
+LP002317,Male,Yes,3+,1,0.0,81000,0.0,360.0,360.0,0,Rural,0.0
+LP002318,Female,No,1,0,1.0,3867,0.0,62.0,360.0,1,Semiurban,0.0
+LP002319,Male,Yes,0,1,0.0,6256,0.0,160.0,360.0,0,Urban,1.0
+LP002328,Male,Yes,0,0,0.0,6096,0.0,218.0,360.0,0,Rural,0.0
+LP002332,Male,Yes,0,0,0.0,2253,2033.0,110.0,360.0,1,Rural,1.0
+LP002335,Female,Yes,0,0,0.0,2149,3237.0,178.0,360.0,0,Semiurban,0.0
+LP002337,Female,No,0,1,0.0,2995,0.0,60.0,360.0,1,Urban,1.0
+LP002341,Female,No,1,1,0.0,2600,0.0,160.0,360.0,1,Urban,0.0
+LP002342,Male,Yes,2,1,1.0,1600,20000.0,239.0,360.0,1,Urban,0.0
+LP002345,Male,Yes,0,1,0.0,1025,2773.0,112.0,360.0,1,Rural,1.0
+LP002347,Male,Yes,0,1,0.0,3246,1417.0,138.0,360.0,1,Semiurban,1.0
+LP002348,Male,Yes,0,1,0.0,5829,0.0,138.0,360.0,1,Rural,1.0
+LP002357,Female,No,0,0,0.0,2720,0.0,80.0,,0,Urban,0.0
+LP002361,Male,Yes,0,1,0.0,1820,1719.0,100.0,360.0,1,Urban,1.0
+LP002362,Male,Yes,1,1,0.0,7250,1667.0,110.0,,0,Urban,0.0
+LP002364,Male,Yes,0,1,0.0,14880,0.0,96.0,360.0,1,Semiurban,1.0
+LP002366,Male,Yes,0,1,0.0,2666,4300.0,121.0,360.0,1,Rural,1.0
+LP002367,Female,No,1,0,0.0,4606,0.0,81.0,360.0,1,Rural,0.0
+LP002368,Male,Yes,2,1,0.0,5935,0.0,133.0,360.0,1,Semiurban,1.0
+LP002369,Male,Yes,0,1,0.0,2920,16.12000084,87.0,360.0,1,Rural,1.0
+LP002370,Male,No,0,0,0.0,2717,0.0,60.0,180.0,1,Urban,1.0
+LP002377,Female,No,1,1,1.0,8624,0.0,150.0,360.0,1,Semiurban,1.0
+LP002379,Male,No,0,1,0.0,6500,0.0,105.0,360.0,0,Rural,0.0
+LP002386,Male,No,0,1,0.0,12876,0.0,405.0,360.0,1,Semiurban,1.0
+LP002387,Male,Yes,0,1,0.0,2425,2340.0,143.0,360.0,1,Semiurban,1.0
+LP002390,Male,No,0,1,0.0,3750,0.0,100.0,360.0,1,Urban,1.0
+LP002393,Female,,,1,0.0,10047,0.0,0.0,240.0,1,Semiurban,1.0
+LP002398,Male,No,0,1,0.0,1926,1851.0,50.0,360.0,1,Semiurban,1.0
+LP002401,Male,Yes,0,1,0.0,2213,1125.0,0.0,360.0,1,Urban,1.0
+LP002403,Male,No,0,1,1.0,10416,0.0,187.0,360.0,0,Urban,0.0
+LP002407,Female,Yes,0,0,1.0,7142,0.0,138.0,360.0,1,Rural,1.0
+LP002408,Male,No,0,1,0.0,3660,5064.0,187.0,360.0,1,Semiurban,1.0
+LP002409,Male,Yes,0,1,0.0,7901,1833.0,180.0,360.0,1,Rural,1.0
+LP002418,Male,No,3+,0,0.0,4707,1993.0,148.0,360.0,1,Semiurban,1.0
+LP002422,Male,No,1,1,0.0,37719,0.0,152.0,360.0,1,Semiurban,1.0
+LP002424,Male,Yes,0,1,0.0,7333,8333.0,175.0,300.0,0,Rural,1.0
+LP002429,Male,Yes,1,1,1.0,3466,1210.0,130.0,360.0,1,Rural,1.0
+LP002434,Male,Yes,2,0,0.0,4652,0.0,110.0,360.0,1,Rural,1.0
+LP002435,Male,Yes,0,1,0.0,3539,1376.0,55.0,360.0,1,Rural,0.0
+LP002443,Male,Yes,2,1,0.0,3340,1710.0,150.0,360.0,0,Rural,0.0
+LP002444,Male,No,1,0,1.0,2769,1542.0,190.0,360.0,0,Semiurban,0.0
+LP002446,Male,Yes,2,0,0.0,2309,1255.0,125.0,360.0,0,Rural,0.0
+LP002447,Male,Yes,2,0,0.0,1958,1456.0,60.0,300.0,0,Urban,1.0
+LP002448,Male,Yes,0,1,0.0,3948,1733.0,149.0,360.0,0,Rural,0.0
+LP002449,Male,Yes,0,1,0.0,2483,2466.0,90.0,180.0,0,Rural,1.0
+LP002453,Male,No,0,1,1.0,7085,0.0,84.0,360.0,1,Semiurban,1.0
+LP002455,Male,Yes,2,1,0.0,3859,0.0,96.0,360.0,1,Semiurban,1.0
+LP002459,Male,Yes,0,1,0.0,4301,0.0,118.0,360.0,1,Urban,1.0
+LP002467,Male,Yes,0,1,0.0,3708,2569.0,173.0,360.0,1,Urban,0.0
+LP002472,Male,No,2,1,0.0,4354,0.0,136.0,360.0,1,Rural,1.0
+LP002473,Male,Yes,0,1,0.0,8334,0.0,160.0,360.0,1,Semiurban,0.0
+LP002478,,Yes,0,1,1.0,2083,4083.0,160.0,360.0,0,Semiurban,1.0
+LP002484,Male,Yes,3+,1,0.0,7740,0.0,128.0,180.0,1,Urban,1.0
+LP002487,Male,Yes,0,1,0.0,3015,2188.0,153.0,360.0,1,Rural,1.0
+LP002489,Female,No,1,0,0.0,5191,0.0,132.0,360.0,1,Semiurban,1.0
+LP002493,Male,No,0,1,0.0,4166,0.0,98.0,360.0,0,Semiurban,0.0
+LP002494,Male,No,0,1,0.0,6000,0.0,140.0,360.0,1,Rural,1.0
+LP002500,Male,Yes,3+,0,0.0,2947,1664.0,70.0,180.0,0,Urban,0.0
+LP002501,,Yes,0,1,0.0,16692,0.0,110.0,360.0,1,Semiurban,1.0
+LP002502,Female,Yes,2,0,0.0,210,2917.0,98.0,360.0,1,Semiurban,1.0
+LP002505,Male,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002515,Male,Yes,1,1,1.0,3450,2079.0,162.0,360.0,1,Semiurban,1.0
+LP002517,Male,Yes,1,0,0.0,2653,1500.0,113.0,180.0,0,Rural,0.0
+LP002519,Male,Yes,3+,1,0.0,4691,0.0,100.0,360.0,1,Semiurban,1.0
+LP002522,Female,No,0,1,1.0,2500,0.0,93.0,360.0,0,Urban,1.0
+LP002524,Male,No,2,1,0.0,5532,4648.0,162.0,360.0,1,Rural,1.0
+LP002527,Male,Yes,2,1,1.0,16525,1014.0,150.0,360.0,1,Rural,1.0
+LP002529,Male,Yes,2,1,0.0,6700,1750.0,230.0,300.0,1,Semiurban,1.0
+LP002530,,Yes,2,1,0.0,2873,1872.0,132.0,360.0,0,Semiurban,0.0
+LP002531,Male,Yes,1,1,1.0,16667,2250.0,86.0,360.0,1,Semiurban,1.0
+LP002533,Male,Yes,2,1,0.0,2947,1603.0,0.0,360.0,1,Urban,0.0
+LP002534,Female,No,0,0,0.0,4350,0.0,154.0,360.0,1,Rural,1.0
+LP002536,Male,Yes,3+,0,0.0,3095,0.0,113.0,360.0,1,Rural,1.0
+LP002537,Male,Yes,0,1,0.0,2083,3150.0,128.0,360.0,1,Semiurban,1.0
+LP002541,Male,Yes,0,1,0.0,10833,0.0,234.0,360.0,1,Semiurban,1.0
+LP002543,Male,Yes,2,1,0.0,8333,0.0,246.0,360.0,1,Semiurban,1.0
+LP002544,Male,Yes,1,0,0.0,1958,2436.0,131.0,360.0,1,Rural,1.0
+LP002545,Male,No,2,1,0.0,3547,0.0,80.0,360.0,0,Rural,0.0
+LP002547,Male,Yes,1,1,0.0,18333,0.0,500.0,360.0,1,Urban,0.0
+LP002555,Male,Yes,2,1,1.0,4583,2083.0,160.0,360.0,1,Semiurban,1.0
+LP002556,Male,No,0,1,0.0,2435,0.0,75.0,360.0,1,Urban,0.0
+LP002560,Male,No,0,0,0.0,2699,2785.0,96.0,360.0,0,Semiurban,1.0
+LP002562,Male,Yes,1,0,0.0,5333,1131.0,186.0,360.0,0,Urban,1.0
+LP002571,Male,No,0,0,0.0,3691,0.0,110.0,360.0,1,Rural,1.0
+LP002582,Female,No,0,0,1.0,17263,0.0,225.0,360.0,1,Semiurban,1.0
+LP002585,Male,Yes,0,1,0.0,3597,2157.0,119.0,360.0,0,Rural,0.0
+LP002586,Female,Yes,1,1,0.0,3326,913.0,105.0,84.0,1,Semiurban,1.0
+LP002587,Male,Yes,0,0,0.0,2600,1700.0,107.0,360.0,1,Rural,1.0
+LP002588,Male,Yes,0,1,0.0,4625,2857.0,111.0,12.0,0,Urban,1.0
+LP002600,Male,Yes,1,1,1.0,2895,0.0,95.0,360.0,1,Semiurban,1.0
+LP002602,Male,No,0,1,0.0,6283,4416.0,209.0,360.0,0,Rural,0.0
+LP002603,Female,No,0,1,0.0,645,3683.0,113.0,480.0,1,Rural,1.0
+LP002606,Female,No,0,1,0.0,3159,0.0,100.0,360.0,1,Semiurban,1.0
+LP002615,Male,Yes,2,1,0.0,4865,5624.0,208.0,360.0,1,Semiurban,1.0
+LP002618,Male,Yes,1,0,0.0,4050,5302.0,138.0,360.0,0,Rural,0.0
+LP002619,Male,Yes,0,0,0.0,3814,1483.0,124.0,300.0,1,Semiurban,1.0
+LP002622,Male,Yes,2,1,0.0,3510,4416.0,243.0,360.0,1,Rural,1.0
+LP002624,Male,Yes,0,1,0.0,20833,6667.0,480.0,360.0,0,Urban,1.0
+LP002625,,No,0,1,0.0,3583,0.0,96.0,360.0,1,Urban,0.0
+LP002626,Male,Yes,0,1,1.0,2479,3013.0,188.0,360.0,1,Urban,1.0
+LP002634,Female,No,1,1,0.0,13262,0.0,40.0,360.0,1,Urban,1.0
+LP002637,Male,No,0,0,0.0,3598,1287.0,100.0,360.0,1,Rural,0.0
+LP002640,Male,Yes,1,1,0.0,6065,2004.0,250.0,360.0,1,Semiurban,1.0
+LP002643,Male,Yes,2,1,0.0,3283,2035.0,148.0,360.0,1,Urban,1.0
+LP002648,Male,Yes,0,1,0.0,2130,6666.0,70.0,180.0,1,Semiurban,0.0
+LP002652,Male,No,0,1,0.0,5815,3666.0,311.0,360.0,1,Rural,0.0
+LP002659,Male,Yes,3+,1,0.0,3466,3428.0,150.0,360.0,1,Rural,1.0
+LP002670,Female,Yes,2,1,0.0,2031,1632.0,113.0,480.0,1,Semiurban,1.0
+LP002682,Male,Yes,,0,0.0,3074,1800.0,123.0,360.0,0,Semiurban,0.0
+LP002683,Male,No,0,1,0.0,4683,1915.0,185.0,360.0,1,Semiurban,0.0
+LP002684,Female,No,0,0,0.0,3400,0.0,95.0,360.0,1,Rural,0.0
+LP002689,Male,Yes,2,0,0.0,2192,1742.0,45.0,360.0,1,Semiurban,1.0
+LP002690,Male,No,0,1,0.0,2500,0.0,55.0,360.0,1,Semiurban,1.0
+LP002692,Male,Yes,3+,1,1.0,5677,1424.0,100.0,360.0,1,Rural,1.0
+LP002693,Male,Yes,2,1,1.0,7948,7166.0,480.0,360.0,1,Rural,1.0
+LP002697,Male,No,0,1,0.0,4680,2087.0,0.0,360.0,1,Semiurban,0.0
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+LP002706,Male,Yes,1,0,0.0,5285,1430.0,161.0,360.0,0,Semiurban,1.0
+LP002714,Male,No,1,0,0.0,2679,1302.0,94.0,360.0,1,Semiurban,1.0
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+LP002720,Male,Yes,3+,1,0.0,4281,0.0,100.0,360.0,1,Urban,1.0
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+LP002757,Female,Yes,0,0,0.0,3017,663.0,102.0,360.0,0,Semiurban,1.0
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+LP002768,Male,No,0,0,0.0,3358,0.0,80.0,36.0,1,Semiurban,0.0
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+LP002794,Female,No,0,1,0.0,2667,1625.0,84.0,360.0,0,Urban,1.0
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+LP002888,Male,No,0,1,0.0,3182,2917.0,161.0,360.0,1,Urban,1.0
+LP002892,Male,Yes,2,1,0.0,6540,0.0,205.0,360.0,1,Semiurban,1.0
+LP002893,Male,No,0,1,0.0,1836,33837.0,90.0,360.0,1,Urban,0.0
+LP002894,Female,Yes,0,1,0.0,3166,0.0,36.0,360.0,1,Semiurban,1.0
+LP002898,Male,Yes,1,1,0.0,1880,0.0,61.0,360.0,0,Rural,0.0
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abanin_daniil_lab_5/README.md

@@ -0,0 +1,38 @@
+## Лабораторная работа №5
+
+### Ранжирование признаков
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, pandas, matplotlib, sklearn
+* запустить проект (стартовая точка lab4)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки pandas, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+Программа решает задачу регрессии, используя полиномиальную регрессию.
+Цель - предсказать сумму займа (LoanAmount), используя имеющиеся признаки: ApplicantIncome - доход заявителя, Credit_History - статус соответствия кредитной истории стандартам банка,
+Education - наличие образования, Married - заявитель женат/замужем (Да/Нет), Self_Employed - самозанятый (Да/Нет)
+
+### Тест
+Зелёные маркеры на графике - тестовые результаты
+Красные маркеры на графике - предсказанные результаты
+
+При небольшом объёме тестовых данных, алгоритм показывает неплохие результаты обучения
+
+![Result](grade_1.png)
+
+![Result](result_1.png)
+
+Но при увеличении объёма данных, алгоритм теряет свою эффективность
+
+![Result](grade_2.png)
+
+![Result](result_2.png)
+
+Вывод: На малых объёмах данных алгоритм показывает свою эффективность. Но при большем объём стоит использовать другие методы для данного набора информации

abanin_daniil_lab_5/lab5.py

@@ -0,0 +1,33 @@
+from matplotlib import pyplot as plt
+from sklearn import metrics
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import Pipeline
+import pandas as pd
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'Credit_History', 'Education', 'Married', 'Self_Employed']]
+    y = data[['LoanAmount']]
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+
+    poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
+                     ('linear', LinearRegression())])
+    poly.fit(x_train, y_train)
+
+    y_predicted = poly.predict(x_test)
+
+    print('Оценка обучения:')
+    print(metrics.r2_score(y_test, y_predicted))
+
+    plt.figure(1, figsize=(16, 9))
+    plt.title('Сравнение результатов обучения')
+    plt.scatter(x=[i for i in range(len(x_test))], y=y_test, c='green', s=5)
+    plt.scatter(x=[i for i in range(len(x_test))], y=y_predicted, c='red', s=5)
+    plt.show()
+
+
+start()

abanin_daniil_lab_5/loan.csv

@@ -0,0 +1,615 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
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+LP002974,Male,1.0,0,1,0.0,3232,1950.0,108.0,360.0,1,Rural,1.0
+LP002978,Female,0.0,0,1,0.0,2900,0.0,71.0,360.0,1,Rural,1.0
+LP002979,Male,1.0,3+,1,0.0,4106,0.0,40.0,180.0,1,Rural,1.0
+LP002983,Male,1.0,1,1,0.0,8072,240.0,253.0,360.0,1,Urban,1.0
+LP002984,Male,1.0,2,1,0.0,7583,0.0,187.0,360.0,1,Urban,1.0
+LP002990,Female,0.0,0,1,1.0,4583,0.0,133.0,360.0,0,Semiurban,0.0

abanin_daniil_lab_7/README.md

@@ -0,0 +1,39 @@
+# Лабораторная работа №7
+
+### Рекуррентная нейронная сеть и задача генерации текста
+
+## ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, keras, tensorflow
+* запустить проект (стартовая точка lab7)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, keras, tensorflow
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+* На основе выбранных художественных текстов происходит обучение рекуррентной нейронной сети для решения задачи генерации. 
+* Необходимо подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.
+
+### Тест
+* Чтение текста из файлов .txt (eng_text.txt, rus_text.txt)
+* Вызов функция get_model_data, из которой мы получаем входные, выходные данные (X, y), размер словаря и токенайзер. Используем Tokenizer с настройкой char_level=True, что позволяет упразднить использование Embedding слоя далее
+* Создание объекта Sequential (последовательная рекуррентная нейронная сеть) и добавление двух слоёв LSTM. LSTM (Long Short-Term Memory) представляет собой разновидность рекуррентной нейронной сети, которая эффективно работает с последовательными данными. Использование нескольких слоёв даёт большую гибкость. Dropout — это метод регуляризации для нейронных сетей и моделей глубокого обучения, решение проблемы переобучения. Слой Dense с функцией активации softmax используется для предсказания следующего слова
+* Компилирование модели с использованием sparse_categorical_crossentropy
+* Обучение модели на 100 эпохах (оптимальный вариант)
+* Генерация текста
+
+Сгенерированные тексты
+
+* ENG: I must be getting somewhere near the centre of the earth. how funny it'll seem to come out among the people that walk with their heads downward! the antipathies, i think—' (for, you see, alice had learnt several things of this
+
+* RUS: господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. чемодан внесли кучер селифан отправился на конюшню вози
+
+![Rus](result_rus.png)
+
+![Eng](result_eng.png)
+
+По итогу, программа способна сгенерировать осмысленный текст в каждом из случаев

abanin_daniil_lab_7/eng_text.txt

@@ -0,0 +1,7 @@
+Either the well was very deep, or she fell very slowly, for she had plenty of time as she went down to look about her and to wonder what was going to happen next. First, she tried to look down and make out what she was coming to, but it was too dark to see anything; then she looked at the sides of the well, and noticed that they were filled with cupboards and book-shelves; here and there she saw maps and pictures hung upon pegs. She took down a jar from one of the shelves as she passed; it was labelled 'ORANGE MARMALADE', but to her great disappointment it was empty: she did not like to drop the jar for fear of killing somebody, so managed to put it into one of the cupboards as she fell past it.
+
+'Well!' thought Alice to herself, 'after such a fall as this, I shall think nothing of tumbling down stairs! How brave they'll all think me at home! Why, I wouldn't say anything about it, even if I fell off the top of the house!' (Which was very likely true.)
+
+Down, down, down. Would the fall NEVER come to an end! 'I wonder how many miles I've fallen by this time?' she said aloud. 'I must be getting somewhere near the centre of the earth. Let me see: that would be four thousand miles down, I think—' (for, you see, Alice had learnt several things of this sort in her lessons in the schoolroom, and though this was not a VERY good opportunity for showing off her knowledge, as there was no one to listen to her, still it was good practice to say it over) '—yes, that's about the right distance—but then I wonder what Latitude or Longitude I've got to?' (Alice had no idea what Latitude was, or Longitude either, but thought they were nice grand words to say.)
+
+Presently she began again. 'I wonder if I shall fall right THROUGH the earth! How funny it'll seem to come out among the people that walk with their heads downward! The Antipathies, I think—' (she was rather glad there WAS no one listening, this time, as it didn't sound at all the right word) '—but I shall have to ask them what the name of the country is, you know. Please, Ma'am, is this New Zealand or Australia?' (and she tried to curtsey as she spoke—fancy CURTSEYING as you're falling through the air!

abanin_daniil_lab_7/lab7.py

@@ -0,0 +1,75 @@
+from keras import Sequential
+from keras.layers import LSTM, Dense, Dropout
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+import numpy as np
+
+with open('rus_text.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+
+def create_sequences(text, seq_len):
+    sequences = []
+    next_chars = []
+    for i in range(0, len(text) - seq_len):
+        sequences.append(text[i:i + seq_len])
+        next_chars.append(text[i + seq_len])
+    return sequences, next_chars
+
+
+def get_model_data(seq_length):
+    tokenizer = Tokenizer(char_level=True)
+    tokenizer.fit_on_texts([text])
+
+    token_text = tokenizer.texts_to_sequences([text])[0]
+
+    sequences, next_chars = create_sequences(token_text, seq_length)
+
+    vocab_size = len(tokenizer.word_index) + 1
+    x = pad_sequences(sequences, maxlen=seq_length)
+    y = np.array(next_chars)
+
+    return x, y, vocab_size, tokenizer
+
+
+def model_build(model, vocab_size):
+    model.add(LSTM(256, input_shape=(seq_length, 1), return_sequences=True))
+    model.add(LSTM(128, input_shape=(seq_length, 1)))
+    model.add(Dropout(0.2, input_shape=(60,)))
+    model.add(Dense(vocab_size, activation='softmax'))
+
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+# Функция для генерации текста
+def generate_text(seed_text, gen_length, tokenizer, model):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+
+
+seq_length = 10
+seed_text = "господин осматривал свою"
+
+# Создание экземпляра Tokenizer и обучение на тексте
+
+X, y, vocab_size, tokenizer = get_model_data(seq_length)
+
+model = Sequential()
+
+model_build(model, vocab_size)
+
+model.fit(X, y, epochs=100, verbose=1)
+
+generated_text = generate_text(seed_text, 200, tokenizer, model)
+print(generated_text)

abanin_daniil_lab_7/rus_text.txt

@@ -0,0 +1,3 @@
+В ворота гостиницы губернского города NN въехала довольно красивая рессорная небольшая бричка, в какой ездят холостяки: отставные подполковники, штабс-капитаны, помещики, имеющие около сотни душ крестьян, — словом, все те, которых называют господами средней руки. В бричке сидел господин, не красавец, но и не дурной наружности, ни слишком толст, ни слишком тонок; нельзя сказать, чтобы стар, однако ж и не так чтобы слишком молод. Въезд его не произвел в городе совершенно никакого шума и не был сопровожден ничем особенным; только два русские мужика, стоявшие у дверей кабака против гостиницы, сделали кое-какие замечания, относившиеся, впрочем, более к экипажу, чем к сидевшему в нем. «Вишь ты, — сказал один другому, — вон какое колесо! что ты думаешь, доедет то колесо, если б случилось, в Москву или не доедет?» — «Доедет», — отвечал другой. «А в Казань-то, я думаю, не доедет?» — «В Казань не доедет», — отвечал другой. Этим разговор и кончился. Да еще, когда бричка подъехала к гостинице, встретился молодой человек в белых канифасовых панталонах, весьма узких и коротких, во фраке с покушеньями на моду, из-под которого видна была манишка, застегнутая тульскою булавкою с бронзовым пистолетом. Молодой человек оборотился назад, посмотрел экипаж, придержал рукою картуз, чуть не слетевший от ветра, и пошел своей дорогой.
+Когда экипаж въехал на двор, господин был встречен трактирным слугою, или половым, как их называют в русских трактирах, живым и вертлявым до такой степени, что даже нельзя было рассмотреть, какое у него было лицо. Он выбежал проворно, с салфеткой в руке, весь длинный и в длинном демикотонном сюртуке со спинкою чуть не на самом затылке, встряхнул волосами и повел проворно господина вверх по всей деревянной галдарее показывать ниспосланный ему Богом покой. Покой был известного рода, ибо гостиница была тоже известного рода, то есть именно такая, как бывают гостиницы в губернских городах, где за два рубля в сутки проезжающие получают покойную комнату с тараканами, выглядывающими, как чернослив, из всех углов, и дверью в соседнее помещение, всегда заставленную комодом, где устроивается сосед, молчаливый и спокойный человек, но чрезвычайно любопытный, интересующийся знать о всех подробностях проезжающего. Наружный фасад гостиницы отвечал ее внутренности: она была очень длинна, в два этажа; нижний не был выщекатурен и оставался в темно-красных кирпичиках, еще более потемневших от лихих погодных перемен и грязноватых уже самих по себе; верхний был выкрашен вечною желтою краскою; внизу были лавочки с хомутами, веревками и баранками. В угольной из этих лавочек, или, лучше, в окне, помещался сбитенщик с самоваром из красной меди и лицом так же красным, как самовар, так что издали можно бы подумать, что на окне стояло два самовара, если б один самовар не был с черною как смоль бородою.
+Пока приезжий господин осматривал свою комнату, внесены были его пожитки: прежде всего чемодан из белой кожи, несколько поистасканный, показывавший, что был не в первый раз в дороге. Чемодан внесли кучер Селифан, низенький человек в тулупчике, и лакей Петрушка, малый лет тридцати, в просторном подержанном сюртуке, как видно с барского плеча, малый немного суровый на взгляд, с очень крупными губами и носом. Вслед за чемоданом внесен был небольшой ларчик красного дерева с штучными выкладками из карельской березы, сапожные колодки и завернутая в синюю бумагу жареная курица. Когда все это было внесено, кучер Селифан отправился на конюшню возиться около лошадей, а лакей Петрушка стал устраиваться в маленькой передней, очень темной конурке, куда уже успел притащить свою шинель и вместе с нею какой-то свой собственный запах, который был сообщен и принесенному вслед за тем мешку с разным лакейским туалетом. В этой конурке он приладил к стене узенькую трехногую кровать, накрыв ее небольшим подобием тюфяка, убитым и плоским, как блин, и, может быть, так же замаслившимся, как блин, который удалось ему вытребовать у хозяина гостиницы.

abanin_danill_lab_6/README.md

@@ -0,0 +1,34 @@
+## Лабораторная работа №6
+
+### MLPClassifier
+
+## Cтудент группы ПИбд-41 Абанин Даниил
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (lab6)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* По данным "Eligibility Prediction for Loan" решает задачу классификации, в которой необходимо выявить риски выдачи кредита. В качестве исходных данных используются признаки: 
+Credit_History - соответствие кредитной истории стандартам банка, ApplicantIncome - доход заявителя, LoanAmount - сумма кредитаб, Self_Employed - самозанятость (Да/Нет), Education - наличие образования, Married - заявитель женат/замужем (Да/Нет).
+
+### Примеры работы:
+
+#### Результаты:
+* Было проведено несколько прогонов на разном количестве итераций (200, 400, 600, 800, 1000)
+
+![Result](score_1.png)
+![Result](score_2.png)
+
+Средняя точность находится в диапазоне 50-60%, что является недостаточным значением. Увеличение итераций не дало значительного улучшения результата, 
+максиальный прирост составляет 10%
+
+
+![Result](result_mean.jpg)

abanin_danill_lab_6/lab6.py

@@ -0,0 +1,46 @@
+from matplotlib import pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+import pandas as pd
+import numpy as np
+
+
+def test_iter(iters_num, x_train, x_test, y_train, y_test):
+
+    print("Количество итераций: ", iters_num)
+    scores = []
+
+    for i in range(10):
+        neuro = MLPClassifier(max_iter=iters_num)
+        neuro.fit(x_train, y_train.values.ravel())
+        score = neuro.score(x_test, y_test)
+        print(f'Оценка №{i + 1} - {score}')
+        scores.append(score)
+
+    mean_value = np.mean(scores)
+
+    print(f"Средняя оценка - {mean_value}")
+
+    return mean_value
+
+
+def start():
+    data = pd.read_csv('loan.csv')
+    x = data[['ApplicantIncome', 'LoanAmount', 'Credit_History', 'Self_Employed', 'Education', 'Married']]
+    y = data[['Loan_Status']]
+
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+
+    iters = [200, 400, 600, 800, 1000]
+    iters_means = []
+
+    for i in range(len(iters)):
+        mean_value = test_iter(iters[i], x_train, x_test, y_train, y_test)
+        iters_means.append(mean_value)
+
+    plt.figure(1, figsize=(16, 9))
+    plt.plot(iters, iters_means, c='r')
+    plt.show()
+
+
+start()

abanin_danill_lab_6/loan.csv

@@ -0,0 +1,615 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001002,Male,0.0,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
+LP001003,Male,1.0,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
+LP001005,Male,1.0,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
+LP001006,Male,1.0,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
+LP001008,Male,0.0,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
+LP001011,Male,1.0,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
+LP001013,Male,1.0,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
+LP001014,Male,1.0,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
+LP001018,Male,1.0,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
+LP001020,Male,1.0,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
+LP001024,Male,1.0,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
+LP001027,Male,1.0,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
+LP001028,Male,1.0,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
+LP001029,Male,0.0,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
+LP001030,Male,1.0,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
+LP001032,Male,0.0,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
+LP001034,Male,0.0,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
+LP001036,Female,0.0,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
+LP001038,Male,1.0,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
+LP001041,Male,1.0,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
+LP001043,Male,1.0,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
+LP001046,Male,1.0,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
+LP001047,Male,1.0,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
+LP001050,,1.0,2,0,0.0,3365,1917.0,112.0,360.0,0,Rural,0.0
+LP001052,Male,1.0,1,1,0.0,3717,2925.0,151.0,360.0,0,Semiurban,0.0
+LP001066,Male,1.0,0,1,1.0,9560,0.0,191.0,360.0,1,Semiurban,1.0
+LP001068,Male,1.0,0,1,0.0,2799,2253.0,122.0,360.0,1,Semiurban,1.0
+LP001073,Male,1.0,2,0,0.0,4226,1040.0,110.0,360.0,1,Urban,1.0
+LP001086,Male,0.0,0,0,0.0,1442,0.0,35.0,360.0,1,Urban,0.0
+LP001087,Female,0.0,2,1,0.0,3750,2083.0,120.0,360.0,1,Semiurban,1.0
+LP001091,Male,1.0,1,1,0.0,4166,3369.0,201.0,360.0,0,Urban,0.0
+LP001095,Male,0.0,0,1,0.0,3167,0.0,74.0,360.0,1,Urban,0.0
+LP001097,Male,0.0,1,1,1.0,4692,0.0,106.0,360.0,1,Rural,0.0
+LP001098,Male,1.0,0,1,0.0,3500,1667.0,114.0,360.0,1,Semiurban,1.0
+LP001100,Male,0.0,3+,1,0.0,12500,3000.0,320.0,360.0,1,Rural,0.0
+LP001106,Male,1.0,0,1,0.0,2275,2067.0,0.0,360.0,1,Urban,1.0
+LP001109,Male,1.0,0,1,0.0,1828,1330.0,100.0,,0,Urban,0.0
+LP001112,Female,1.0,0,1,0.0,3667,1459.0,144.0,360.0,1,Semiurban,1.0
+LP001114,Male,0.0,0,1,0.0,4166,7210.0,184.0,360.0,1,Urban,1.0
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+LP002911,Male,1.0,1,1,0.0,2787,1917.0,146.0,360.0,0,Rural,0.0
+LP002912,Male,1.0,1,1,0.0,4283,3000.0,172.0,84.0,1,Rural,0.0
+LP002916,Male,1.0,0,1,0.0,2297,1522.0,104.0,360.0,1,Urban,1.0
+LP002917,Female,0.0,0,0,0.0,2165,0.0,70.0,360.0,1,Semiurban,1.0
+LP002925,,0.0,0,1,0.0,4750,0.0,94.0,360.0,1,Semiurban,1.0
+LP002926,Male,1.0,2,1,1.0,2726,0.0,106.0,360.0,0,Semiurban,0.0
+LP002928,Male,1.0,0,1,0.0,3000,3416.0,56.0,180.0,1,Semiurban,1.0
+LP002931,Male,1.0,2,1,1.0,6000,0.0,205.0,240.0,1,Semiurban,0.0
+LP002933,,0.0,3+,1,1.0,9357,0.0,292.0,360.0,1,Semiurban,1.0
+LP002936,Male,1.0,0,1,0.0,3859,3300.0,142.0,180.0,1,Rural,1.0
+LP002938,Male,1.0,0,1,1.0,16120,0.0,260.0,360.0,1,Urban,1.0
+LP002940,Male,0.0,0,0,0.0,3833,0.0,110.0,360.0,1,Rural,1.0
+LP002941,Male,1.0,2,0,1.0,6383,1000.0,187.0,360.0,1,Rural,0.0
+LP002943,Male,0.0,,1,0.0,2987,0.0,88.0,360.0,0,Semiurban,0.0
+LP002945,Male,1.0,0,1,1.0,9963,0.0,180.0,360.0,1,Rural,1.0
+LP002948,Male,1.0,2,1,0.0,5780,0.0,192.0,360.0,1,Urban,1.0
+LP002949,Female,0.0,3+,1,0.0,416,41667.0,350.0,180.0,0,Urban,0.0
+LP002950,Male,1.0,0,0,0.0,2894,2792.0,155.0,360.0,1,Rural,1.0
+LP002953,Male,1.0,3+,1,0.0,5703,0.0,128.0,360.0,1,Urban,1.0
+LP002958,Male,0.0,0,1,0.0,3676,4301.0,172.0,360.0,1,Rural,1.0
+LP002959,Female,1.0,1,1,0.0,12000,0.0,496.0,360.0,1,Semiurban,1.0
+LP002960,Male,1.0,0,0,0.0,2400,3800.0,0.0,180.0,1,Urban,0.0
+LP002961,Male,1.0,1,1,0.0,3400,2500.0,173.0,360.0,1,Semiurban,1.0
+LP002964,Male,1.0,2,0,0.0,3987,1411.0,157.0,360.0,1,Rural,1.0
+LP002974,Male,1.0,0,1,0.0,3232,1950.0,108.0,360.0,1,Rural,1.0
+LP002978,Female,0.0,0,1,0.0,2900,0.0,71.0,360.0,1,Rural,1.0
+LP002979,Male,1.0,3+,1,0.0,4106,0.0,40.0,180.0,1,Rural,1.0
+LP002983,Male,1.0,1,1,0.0,8072,240.0,253.0,360.0,1,Urban,1.0
+LP002984,Male,1.0,2,1,0.0,7583,0.0,187.0,360.0,1,Urban,1.0
+LP002990,Female,0.0,0,1,1.0,4583,0.0,133.0,360.0,0,Semiurban,0.0

alexandrov_dmitrii_lab_6/lab6.py

@@ -0,0 +1,76 @@
+from matplotlib import pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+import pandas as pd
+import numpy as np
+
+data = pd.read_csv('sberbank_data.csv', index_col='id')
+x = data[['timestamp', 'full_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'material', 'kremlin_km']]
+
+x = x.replace('NA', 0)
+x.fillna(0, inplace=True)
+
+col_date = []
+
+for val in x['timestamp']:
+    col_date.append(val.split('-', 1)[0])
+
+x = x.drop(columns='timestamp')
+x['timestamp'] = col_date
+
+y = []
+for val in data['price_doc']:
+    if val < 1500000:
+        y.append('low')
+    elif val < 3000000:
+        y.append('medium')
+    elif val < 5500000:
+        y.append('high')
+    elif val < 10000000:
+        y.append('premium')
+    else:
+        y.append('oligarch')
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=42)
+
+min_scores = []
+med_scores = []
+max_scores = []
+
+
+def do_test(iters_num):
+    global x_train, x_test, y_train, y_test, min_scores, med_scores, max_scores
+
+    print("Testing iterations number "+str(iters_num)+":")
+    scores = []
+
+    for i in range(10):
+        neuro = MLPClassifier(max_iter=200)
+        neuro.fit(x_train, y_train)
+        scr = neuro.score(x_test, y_test)
+        print("res"+str(i+1)+": "+str(scr))
+        scores.append(scr)
+
+    print("Medium result: "+str(np.mean(scores)))
+
+    min_scores.append(np.min(scores))
+    med_scores.append(np.mean(scores))
+    max_scores.append(np.max(scores))
+
+
+def start():
+    global min_scores, med_scores, max_scores
+
+    iter_nums = [200, 400, 600, 800, 1000]
+
+    for num in iter_nums:
+        do_test(num)
+
+    plt.figure(1, figsize=(16, 9))
+    plt.plot(iter_nums, min_scores, c='r')
+    plt.plot(iter_nums, med_scores, c='b')
+    plt.plot(iter_nums, max_scores, c='b')
+    plt.show()
+
+
+start()

alexandrov_dmitrii_lab_6/readme.md

@@ -0,0 +1,149 @@
+### Задание
+Использовать нейронную сеть по варианту для выбранных данных по варианту, самостоятельно сформулировав задачу.
+Интерпретировать результаты и оценить, насколько хорошо она подходит для
+решения сформулированной вами задачи.
+
+Вариант 1: MLPClassifier
+
+Была сформулирована следующая задача: необходимо классифицировать жильё по стоимости на основе избранных признаков при помощи нейронной сети.
+
+### Запуск программы
+Файл lab6.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
+
+### Описание программы
+Программа считывает цены на жильё как выходные данные и следующие данные как входные: год размещения объявления, площадь, этаж, количество этажей, год постройки, количество комнат, материал, расстояние до кремля (условного центра).
+Далее она обрабатывает данные (цифровизирует нулевые данные), оставляет только год объявления. Цены распределяются по пяти классам.
+
+После обработки программа делит данные на 99% обучающего материала и 1% тестового.
+Эти данные обрабатываются по 10 раз для идентичных моделей нейронных сетей, использующих метод градиентного спуска "adam", с разной настройкой максимального количества поколений: 200, 400, 600, 800, 1000.
+Считаются оценка модели. Для каждой модели запоминаются минимальный, максимальный и средний результаты. В консоль выводятся все результаты.
+В конце программа показывает графики зависимости результатов от максимального количества поколений модели.
+
+### Результаты тестирования
+По результатам тестирования, можно сказать следующее:
+* В общем, модель даёт средний результат в районе 40-50% точности, что недостаточно.
+* Увеличение максимального количества поколений влияет сильнее всего на минимальные оценки, сужая разброс точности.
+* Нельзя сказать, что увеличение максимального количества поколений сильно улучшит модель: максимум на 10% точности.
+
+Пример консольного вывода:
+>Testing iterations number 200:
+> 
+>res1: 0.3806228373702422
+> 
+>res2: 0.6055363321799307
+> 
+>res3: 0.4809688581314879
+> 
+>res4: 0.4913494809688581
+> 
+>res5: 0.4844290657439446
+> 
+>res6: 0.2975778546712803
+> 
+>res7: 0.48788927335640137
+> 
+>res8: 0.06228373702422145
+> 
+>res9: 0.6193771626297578
+> 
+>res10: 0.47750865051903113
+> 
+>Medium result: 0.4387543252595155
+> 
+>Testing iterations number 400:
+> 
+>res1: 0.6124567474048442
+> 
+>res2: 0.4290657439446367
+> 
+>res3: 0.3217993079584775
+> 
+>res4: 0.5467128027681661
+> 
+>res5: 0.48788927335640137
+> 
+>res6: 0.40484429065743943
+> 
+>res7: 0.6020761245674741
+> 
+>res8: 0.4186851211072664
+> 
+>res9: 0.42214532871972316
+> 
+>res10: 0.370242214532872
+> 
+>Medium result: 0.46159169550173
+> 
+>Testing iterations number 600:
+> 
+>res1: 0.4359861591695502
+> 
+>res2: 0.2560553633217993
+> 
+>res3: 0.5363321799307958
+> 
+>res4: 0.5778546712802768
+> 
+>res5: 0.35986159169550175
+> 
+>res6: 0.356401384083045
+> 
+>res7: 0.49480968858131485
+> 
+>res8: 0.5121107266435986
+> 
+>res9: 0.5224913494809689
+> 
+>res10: 0.5190311418685121
+> 
+>Medium result: 0.4570934256055363
+> 
+>Testing iterations number 800:
+> 
+>res1: 0.25951557093425603
+> 
+>res2: 0.4083044982698962
+> 
+>res3: 0.5224913494809689
+> 
+>res4: 0.5986159169550173
+> 
+>res5: 0.24567474048442905
+> 
+>res6: 0.4013840830449827
+> 
+>res7: 0.21453287197231835
+> 
+>res8: 0.4671280276816609
+> 
+>res9: 0.40484429065743943
+> 
+>res10: 0.38408304498269896
+> 
+>Medium result: 0.3906574394463667
+> 
+>Testing iterations number 1000:
+> 
+>res1: 0.4186851211072664
+> 
+>res2: 0.5017301038062284
+> 
+>res3: 0.5121107266435986
+> 
+>res4: 0.3806228373702422
+> 
+>res5: 0.44982698961937717
+> 
+>res6: 0.5986159169550173
+> 
+>res7: 0.5570934256055363
+> 
+>res8: 0.4290657439446367
+> 
+>res9: 0.32525951557093424
+> 
+>res10: 0.41522491349480967
+> 
+>Medium result: 0.4588235294117647
+
+Итого: Для отобранных данных нейронная модель с методом градиентного спуска "adam" показала себя не лучшим образом. Возможно, другие методы могут выдать лучшие результаты, либо необходима более обширная модификация модели.

alexandrov_dmitrii_lab_7/lab7.py

@@ -0,0 +1,96 @@
+import numpy as np
+from keras_preprocessing.sequence import pad_sequences
+from keras_preprocessing.text import Tokenizer
+from keras.models import Sequential
+from keras.layers import Dense, LSTM, Embedding, Dropout
+from keras.callbacks import ModelCheckpoint
+
+
+def recreate_model(predictors, labels, model, filepath, epoch_num):
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    append_epochs(predictors, labels, model, epoch_num)
+
+
+def append_epochs(predictors, labels, model, filepath, epoch_num):
+    checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
+    desired_callbacks = [checkpoint]
+    model.fit(predictors, labels, epochs=epoch_num, verbose=1, callbacks=desired_callbacks)
+
+
+def generate_text(tokenizer, seed_text, next_words, model, max_seq_length):
+    for _ in range(next_words):
+        token_list = tokenizer.texts_to_sequences([seed_text])[0]
+        token_list = pad_sequences([token_list], maxlen=max_seq_length - 1, padding='pre')
+        predicted = np.argmax(model.predict(token_list), axis=-1)
+        output_word = ""
+        for word, index in tokenizer.word_index.items():
+            if index == predicted:
+                output_word = word
+                break
+        seed_text += " " + output_word
+    return seed_text
+
+
+def start():
+    flag = -1
+    while flag < 1 or flag > 2:
+        flag = int(input("Select model and text (1 - eng, 2 - ru): "))
+
+    if flag == 1:
+        file = open("data.txt").read()
+        filepath = "model_eng.hdf5"
+    elif flag == 2:
+        file = open("rus_data.txt").read()
+        filepath = "model_rus.hdf5"
+    else:
+        exit(1)
+
+    tokenizer = Tokenizer()
+    tokenizer.fit_on_texts([file])
+    words_count = len(tokenizer.word_index) + 1
+
+    input_sequences = []
+    for line in file.split('\n'):
+        token_list = tokenizer.texts_to_sequences([line])[0]
+        for i in range(1, len(token_list)):
+            n_gram_sequence = token_list[:i + 1]
+            input_sequences.append(n_gram_sequence)
+
+    max_seq_length = max([len(x) for x in input_sequences])
+    input_sequences = pad_sequences(input_sequences, maxlen=max_seq_length, padding='pre')
+
+    predictors, labels = input_sequences[:, :-1], input_sequences[:, -1]
+
+    model = Sequential()
+    model.add(Embedding(words_count, 100, input_length=max_seq_length - 1))
+    model.add(LSTM(150))
+    model.add(Dropout(0.15))
+    model.add(Dense(words_count, activation='softmax'))
+
+    flag = input("Do you want to recreate the model ? (print yes): ")
+    if flag == 'yes':
+        flag = input("Are you sure? (print yes): ")
+        if flag == 'yes':
+            num = int(input("Select number of epoch: "))
+            if 0 < num < 100:
+                recreate_model(predictors, labels, model, filepath, num)
+
+    model.load_weights(filepath)
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+    flag = input("Do you want to train the model ? (print yes): ")
+    if flag == 'yes':
+        flag = input("Are you sure? (print yes): ")
+        if flag == 'yes':
+            num = int(input("Select number of epoch: "))
+            if 0 < num < 100:
+                append_epochs(predictors, labels, model, filepath, num)
+
+    flag = 'y'
+    while flag == 'y':
+        seed = input("Enter seed: ")
+        print(generate_text(tokenizer, seed, 25, model, max_seq_length))
+        flag = input("Continue? (print \'y\'): ")
+
+
+start()

alexandrov_dmitrii_lab_7/readme.md

@@ -0,0 +1,49 @@
+### Задание
+Выбрать художественный текст(четные варианты – русскоязычный, нечетные – англоязычный)и обучить на нем рекуррентную нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом. 
+
+Вариант 1: первостепенно - английский текст. Кооперироваться, впрочем, не с кем.
+
+### Запуск программы
+Файл lab7.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
+
+### Описание программы
+Программа представляет собой консольное приложение-инструмент для работы с моделями. Она может создавать и обучать однородные модели для разных текстов.
+В файлах хранятся два текста: англоязычный data.txt (Остров сокровищ) и русскоязычный rus_data.txt (Хоббит). Также там хранятся две сохранённые обученные модели:
+* model_eng - модель, обученная на английском тексте. На текущий момент 27 эпох обучения.
+* model_rus - модель, обученная на русском тексте. На текущий момент 12 эпох обучения.
+Обучение проходило 1 день.
+
+В программе необходимо выбрать загружаемый текст и соответствующую модель, в данный момент подключается русскоязычная модель.
+
+Программа содержит методы пересоздания модели и дообучения модели (передаётся модель и количество эпох дообучения). Оба метода отключены и могут быть подключены обратно при необходимости.
+
+После возможных пересоздания и дообучения моделей программа запрашивает текст-кодовое слово, которое модели будет необходимо продолжить, сгенерировав свой текст.
+
+Сама модель имеет следующую архитектуру:
+* слой, преобразующий слова в векторы плотности, Embedding с входом, равным числу слов, с выходом 100, и с длиной ввода, равной длине максимального слова.
+* слой с блоками долгой краткосрочной памятью, составляющая рекуррентную сеть, LSTM со 150 блоками.
+* слой, задающий степень разрыва нейронных связей между соседними слоями, Dropout с процентом разрыва 15.
+* слой вычисления взвешенных сумм Dense с числом нейронов, равным числу слов в тексте и функцией активации 'softmax'
+
+### Результаты тестирования
+По результатам дневного обучения можно сказать следующее:
+
+Модель успешно генерирует бессмысленные последовательности слов, которые либо состоят из обрывков фраз, либо случайно (но достаточно часто) складываются в осмысленные словосочетания, но не более.
+
+Примеры генераций (первое слово - код генерации):
+
+Модель, обученная на 'Острове сокровищ', 27 эпох обучения:
+>ship that he said with the buccaneers a gentleman and neither can read and figure but what is it anyway ah 'deposed' that's it is a
+> 
+>chest said the doctor touching the black spot mind by the arm who is the ship there's long john now you are the first that were
+> 
+>silver said the doctor if you can get the treasure you can find the ship there's been a man that has lost his score out he
+
+Модель, обученная на 'Хоббите', 12 эпох обучения:
+>дракон и тут они услыхали про смога он понял что он стал видел и разозлился как слоны у гэндальфа хороши но все это было бы он
+>
+>поле он не мог сообразить что он делал то в живых и слышал бильбо как раз доедал пуще прежнего а бильбо все таки уж не мог
+> 
+>паук направился к нему толстому из свертков они добрались до рассвета и даже дальше не останавливаясь а именно что гоблины обидело бильбо они не мог ничего
+
+Итого: Даже такая простая модель с таким малым количеством эпох обучения может иногда сгенерировать нечто осмысленное. Однако для генерации нормального текста необходимо длительное обучение и более сложная модель, из нескольких слоёв LSTM и Dropout после них, что, однако, потребовало бы вычислительные мощности, которых у меня нет в наличии. Иначе следует взять очень маленький текст.

almukhammetov_bulat_lab_3/README.md

@@ -0,0 +1,64 @@
+Вариант 2
+
+Задание:
+Предсказание категории возраста дома (housingMedianAge) на основе других признаков, таких как широта, долгота, общее количество комнат и т.д.
+
+Данные: 
+Данный набор данных использовался во второй главе недавней книги Аурелиена Жерона "Практическое машинное обучение с помощью Scikit-Learn и TensorFlow". Он служит отличным введением в реализацию алгоритмов машинного обучения, потому что требует минимальной предварительной обработки данных, содержит легко понимаемый список переменных и находится в оптимальном размере, который не слишком мал и не слишком большой.
+
+Данные содержат информацию о домах в определенном районе Калифорнии и некоторую сводную статистику на основе данных переписи 1990 года. Следует отметить, что данные не прошли предварительную очистку, и для них требуются некоторые этапы предварительной обработки. Столбцы включают в себя следующие переменные, их названия весьма наглядно описывают их суть:
+
+долгота longitude
+
+широта latitude
+
+средний возраст жилья median_house_value
+
+общее количество комнат total_rooms
+
+общее количество спален total_bedrooms
+
+население population
+
+домохозяйства households
+
+медианный доход median_income
+
+Запуск:
+Запустите файл lab3.py
+
+Описание программы:
+
+1. Загружает набор данных из файла 'housing.csv', который содержит информацию о домах в Калифорнии, включая их координаты, возраст, количество комнат, население, доход и другие характеристики.
+
+2. Удаляет строки с нулевыми значениями из набора данных для чистоты анализа.
+
+3. Выбирает набор признаков (features) из данных, которые будут использоваться для обучения моделей регрессии и классификации.
+
+4. Определяет задачу регрессии, где целевой переменной (target) является 'housing_median_age', и задачу классификации, где целевой переменной является 'housing_median_age'.
+
+5. Разделяет данные на обучающий и тестовый наборы для обеих задач с использованием функции train_test_split. Тестовый набор составляет 1% от исходных данных.
+
+6. Создает и обучает дерево решений для регрессии и классификации с использованием моделей DecisionTreeRegressor и DecisionTreeClassifier.
+
+7. Предсказывает значения целевой переменной на тестовых наборах для обеих задач.
+
+8. Оценивает качество моделей с помощью среднеквадратичной ошибки (MSE) для регрессии и точности (accuracy) для классификации.
+
+9. Выводит среднеквадратичную ошибку для регрессии и точность для классификации, а также важности признаков для обеих задач.
+
+Результаты:
+
+![Alt text](1.png)
+
+Выводы: 
+
+Для задачи регрессии, где целью было предсказать возраст жилья (housing_median_age), модель дерева решений показала среднюю ошибку (MSE) равную 117.65. Это означает, что модель регрессии вполне приемлемо предсказывает возраст жилья на основе выбранных признаков.
+
+Для задачи классификации, где целью было предсказать стоимость жилья (housing_median_age), модель дерева решений показала низкую точность, всего 8.29%. Это свидетельствует о том, что модель классификации не справляется с предсказанием стоимости жилья на основе выбранных признаков. Низкая точность указывает на необходимость улучшения модели или выбора других методов для решения задачи классификации.
+
+Анализ важности признаков для задачи регрессии показал, что наибольший вклад в предсказание возраста жилья вносят признаки 'longitude', 'latitude' и 'total_rooms'. Эти признаки оказывают наибольшее влияние на результаты модели.
+
+Для задачи классификации наибольший вклад в предсказание стоимости жилья вносят признаки 'median_income', 'longitude' и 'latitude'. Эти признаки имеют наибольшее значение при определении классов стоимости жилья.
+
+В целом, результаты указывают на успешное решение задачи регрессии с использованием модели дерева решений. Однако задача классификации требует дополнительных улучшений.

almukhammetov_bulat_lab_3/lab3(old).py

@@ -0,0 +1,48 @@
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.tree import DecisionTreeClassifier
+
+# Загрузка данных
+data = pd.read_csv('titanic.csv', index_col='PassengerId')
+
+
+# Функция для преобразования пола в числовое значение
+def Sex_to_bool(sex):
+    if sex == "male":
+        return 0
+    return 1
+
+
+# Преобразование пола в числовое значение
+data['Sex'] = data['Sex'].apply(Sex_to_bool)
+
+# Отбор строк с непустыми значениями
+# Отбор строк с непустыми значениями
+data = data.loc[~data['Name'].isna()
+                & ~data['Age'].isna()
+                & ~data['Sex'].isna()
+                & ~data['Survived'].isna()]
+
+
+# Отбор нужных столбцов
+features = data[['Name', 'Sex', 'Age']]
+
+# Применение Label Encoding к столбцу 'Name'
+label_encoder = LabelEncoder()
+features['Name'] = label_encoder.fit_transform(features['Name'])
+
+# Определение целевой переменной
+y = data['Survived']
+
+# Создание и обучение дерева решений
+clf = DecisionTreeClassifier(random_state=241)
+clf.fit(features, y)
+
+# Получение важностей признаков
+importance = clf.feature_importances_
+
+# Печать важности каждого признака
+print("Важность 'Name':", importance[0])
+print("Важность 'Sex':", importance[1])
+print("Важность 'Age':", importance[2])
+

almukhammetov_bulat_lab_3/lab3.py

@@ -0,0 +1,77 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, accuracy_score
+
+# Загрузка данных
+data = pd.read_csv('housing.csv')
+data = data.dropna()
+
+# Отбор нужных столбцов
+features = data[
+    ['longitude', 'latitude', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income']]
+
+# Задача регрессии
+target_regression = data['housing_median_age']
+
+# Разделение данных на обучающий и тестовый наборы для регрессии
+X_train_regression, X_test_regression, y_train_regression, y_test_regression = train_test_split(features,
+                                                                                                target_regression,
+                                                                                                test_size=0.01,
+                                                                                                random_state=241)
+
+# Создание и обучение дерева решений для регрессии
+clf_regression = DecisionTreeRegressor(random_state=241)
+clf_regression.fit(X_train_regression, y_train_regression)
+
+# Предсказание на тестовом наборе для регрессии
+y_pred_regression = clf_regression.predict(X_test_regression)
+
+# Оценка качества модели для регрессии (MSE)
+mse_regression = mean_squared_error(y_test_regression, y_pred_regression)
+print("Средняя ошибка для регрессии:", mse_regression)
+
+# Задача классификации
+target_classification = data['median_house_value']
+
+# Разделение данных на обучающий и тестовый наборы для классификации
+X_train_classification, X_test_classification, y_train_classification, y_test_classification = train_test_split(
+    features, target_classification, test_size=0.01, random_state=241)
+
+# Создание и обучение дерева классификации
+clf_classification = DecisionTreeClassifier(random_state=241)
+clf_classification.fit(X_train_classification, y_train_classification)
+
+# Предсказание на тестовом наборе для классификации
+y_pred_classification = clf_classification.predict(X_test_classification)
+
+# Оценка качества модели для классификации (точность)
+accuracy_classification = accuracy_score(y_test_classification, y_pred_classification)
+print("Точность для классификации: {:.2f}%".format(accuracy_classification * 100))
+
+# Важности признаков для регрессии
+importance_regression = clf_regression.feature_importances_
+
+print("Важность для регрессии")
+# Печать важности каждого признака для регрессии
+print("Важность 'longitude':", importance_regression[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_regression[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_regression[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_regression[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_regression[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_regression[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_regression[6])  # За медианный доход домохозяйств в блоке
+
+# Важности признаков для классификации
+importance_classification = clf_classification.feature_importances_
+
+print()
+print("Важность для классификации")
+# Печать важности каждого признака для классификации
+print("Важность 'longitude':", importance_classification[0])  # За западную долготу дома
+print("Важность 'latitude':", importance_classification[1])  # За северную широту дома
+print("Важность 'total_rooms':", importance_classification[2])  # За общее количество комнат в блоке
+print("Важность 'total_bedrooms':", importance_classification[3])  # За общее количество спален в блоке
+print("Важность 'population':", importance_classification[4])  # За общее количество проживающих в блоке
+print("Важность 'households':", importance_classification[5])  # За общее количество домохозяйств в блоке
+print("Важность 'median_income':", importance_classification[6])  # За медианный доход домохозяйств в блоке

arzamaskina_milana_lab_4/README.md

@@ -0,0 +1,112 @@
+# Лабораторная работа №4
+
+## Кластеризация
+
+#### ПИбд-41 Арзамаскина Милана
+#### Вариант №2 (2 % 5 = 2)
+
+## Задание:
+
+Использовать метод кластеризации по варианту для данных из таблицы 1  по  варианту (таблица  9),
+самостоятельно  сформулировав  задачу. 
+Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вами задачи.
+
+#### Формулировка задачи: 
+Группировка стран на основе их характеристик:
+количество выбросов от добычи нефтепродуктов, газа, угля в 2020 году.
+С помощью алгоритма кластеризации: linkage.
+
+## Данные:
+
+Этот набор данных обеспечивает углубленный анализ глобальных выбросов CO2 на уровне страны, позволяя лучше понять, 
+какой вклад каждая страна вносит в глобальное совокупное воздействие человека на климат. 
+Он содержит информацию об общих выбросах, а также от добычи и сжигания угля, нефти, газа, цемента и других источников. 
+Данные также дают разбивку выбросов CO2 на душу населения по странам, показывая, 
+какие страны лидируют по уровням загрязнения, и определяют потенциальные области, 
+где следует сосредоточить усилия по сокращению выбросов. 
+Этот набор данных необходим всем, кто хочет получить информацию о своем воздействии на окружающую среду 
+или провести исследование тенденций международного развития.
+
+Данные организованы с использованием следующих столбцов: 
+
++ Country: название страны
++ ISO 3166-1 alpha-3: трехбуквенный код страны
++ Year: год данных исследования
++ Total: общее количество CO2, выброшенный страной в этом году
++ Coal: количество CO2, выброшенное углем в этом году
++ Oil: количество выбросов нефти
++ Gas: количество выбросов газа
++ Cement: количество выбросов цемента
++ Flaring: сжигание на факелах уровни выбросов
++ Other: другие формы, такие как промышленные процессы
++ Per Capita: столбец «на душу населения» 
+
+
+### Какие технологии использовались:
+
+Используемые библиотеки:
+* scipy.cluster.hierarchy
+* pandas
+* matplotlib
+* seaborn
+
+### Как запустить:
+
+* установить python, scipy.cluster.hierarchy, pandas, matplotlib, seaborn
+* запустить проект (стартовая точка - main.py)
+
+### Что делает программа:
+
+* Загружает набор данных из файла 'CO2.csv', который содержит информацию о выбросах странами CO2 в год от различной промышленной деятельности.
+* Очищает набор данных путём удаления строк с нулевыми значениями и глобальными значениями по всем странам (строки 'Global') из набора.
+* Отбирает данные по странам за 2020 год.
+* Выбирает набор признаков (features) из данных, которые будут использоваться для кластеризации.
+* Применяет алгоритм linkage.
+* Формирует 4 кластера данных с помощью функции fcluster.
+* Выводит таблицу со списком стран, их характеристиками и номером кластера, к которому они относятся.
+* Отображает диаграмму результатов кластеризации seaborn.
+
+
+#### Результаты работы программы:
+
+![Result](img_1.png)
+![Result](img.png)
+
+### Вывод:
+
+Для кластеризации данных было использовано 4 кластера для группировки стран на основе их характеристик:
+количество выбросов от добычи нефтепродуктов, газа, угля в 2020 году.
+На кластеры разбивались 47 стран.
+
+На диаграмме можно наблюдать, что практически все страны относятся к первому кластеру, 
+а 2, 3, 4 кластер содержит всего по одной стране.
+
++ Кластер 1: 
+
+Включает страны с маленьким объёмом выбросов от добычи газа, нефти и угля в 2020 году. 
+В данную группу входят такие страны, как Швейцария, Турция, Норвегия и др.
+Опираясь на результаты кластеризации, можно сказать, что страны имеют малое количество месторождений газа, нефти и угля,
+либо не занимаются их добычей, либо ведут мероприятия по смягчению последствий от загрязнений.
+
++ Кластер 2: 
+
+Включает одну страну - Россию. 
+По диаграмме можно сказать, что в стране выбросы от добычи нефти и угля также малы, как и в странах первого кластера. 
+Однако, выбросы от добычи газа в несколько раз больше.
+Страна, которая занимается преимущественно добычей газа.
+
++ Кластер 3: 
+
+Содержит также одну страну - США. У страны рекордный объём выбросов от добычи газа, также страна этого кластера 
+отличается бОльшим объемом выбросов от добычи нефти (можно наблюдать на диаграмме по размеру точки).
+Страна, лидирующая по объёму выбросов от добычи газа. Страна является лидером по запасам природного газа.
+
++ Кластер 4: 
+
+Включает страну - Китай. Судя по диаграмме кластеризации Китай производит большой объём выбросов 
+в процессе добычи нефти и рекордный в процессе добычи угля.
+Страна, лидирующая по выбросам от добычи угля и нефти, а также средняя по выбросам от газа. 
+Опираясь на результаты кластеризации, можно сказать, что Китай - лидер по добыче угля.
+
+Данная кластеризация на 4 кластера хорошо подходит для распределения стран 
+на группы по объёму выбросов от добычи нефти, газа и угля.

arzamaskina_milana_lab_4/main.py

@@ -0,0 +1,44 @@
+import pandas as pd
+from scipy.cluster.hierarchy import linkage, fcluster
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Кластеризация: Группировка стран на основе их характеристик:
+# количество выбросов от добычи нефтепродуктов, газа, угля в 2020 году.
+
+# Загружаем данные, отбираем данные за 2020 год
+# и удаляем строки, в которых данные отсутствуют
+data_first = pd.read_csv('CO2.csv')
+data_first = data_first.dropna()
+data = data_first[data_first['Year'] == 2020]
+data = data[data.Country != 'Global']
+names = data['Country']
+
+# Выделение признаков
+features = data[['Coal', 'Oil', 'Gas']]
+
+# Применение алгоритма linkage
+link_cl = linkage(features, method='ward', metric='euclidean')
+
+# Кластеризация на 3 кластера
+clusters = fcluster(link_cl, 4, criterion='maxclust')
+
+# Добавление информации о кластерах в исходные данные
+features['cluster'] = clusters
+
+# Диаграмма результатов кластеризации
+plt.figure(figsize=(12, 6))
+sns.scatterplot(x='Coal', y='Gas', size='Oil',
+                sizes=(10, 200), hue='cluster', palette='viridis', data=features)
+plt.title('Clustering Results')
+plt.xlabel('Coal')
+plt.ylabel('Gas')
+
+# Вывод таблицы с результатами кластеризации
+# Добавление наименований стран
+features.insert(loc=0, column='country', value=names)
+# Вывод результатов кластеризации
+result_clust = features[['cluster', 'country', 'Coal', 'Oil', 'Gas']]
+print(result_clust)
+
+plt.show()

basharin_sevastyan_lab_2/README.md

@@ -0,0 +1,42 @@
+## Лабораторная работа 2. Вариант 5.
+### Задание 
+Выполнить ранжирование признаков. Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Провести анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? 
+
+Модели:
+
+- Гребневая регрессия `Ridge`, 
+- Рекурсивное сокращение признаков `Recursive Feature Elimination – RFE`, 
+- Сокращение признаков Случайными деревьями `Random Forest Regressor`
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+```
+python main.py
+```
+
+### Используемые технологии
+- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации.
+  - `Ridge` - инструмент работы с моделью "Гребневая регрессия"
+  - `RFE` - инструмент оценки важности признаков "Рекурсивное сокращение признаков"
+  - `RandomForestRegressor` - инструмент работы с моделью "Регрессор случайного леса"
+
+### Описание работы
+1. Программа генерирует данные для обучения моделей, содержащие матрицу признаков X и вектор целевой переменной y.
+1. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную.
+1. Разделяет данные на матрицу признаков X и вектор целевой переменной y
+1. Создает список обученных моделей для ранжирования признаков: гребневой регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями.
+1. Создает словарь model_scores для хранения оценок каждой модели.
+1. Выводит оценки признаков каждой модели и их средние оценки.
+1. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения.
+
+### Результат работы
+![](ridge.png "Гребневая регрессия")
+![](rfe.png "Рекурсивное сокращение признаков")
+![](rfr.png "Сокращение признаков Случайными деревьями")
+![](res.png "Четыре самых важных")
+
+### Вывод
+Четыре наиболее важных признака, определенных на основе средних оценок, включают 
+Признак 1, Признак 3, Признак 12 и Признак 6.

basharin_sevastyan_lab_2/main.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Ridge, LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import RFE
+from sklearn.preprocessing import MinMaxScaler
+
+''' Задание
+Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с 
+помощью указанных по вариантумоделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и
+среднюю  оценку. Проведите анализ получившихся результатов. Какие четырепризнака оказались самыми важными по среднему 
+значению? (Названия\индексы  признаков и будут ответом на задание).
+
+Вариант 5.
+Гребневая регрессия (Ridge), Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE),
+Сокращение признаков Случайными деревьями (Random Forest Regressor).
+'''
+
+# создание данных
+random_state = np.random.RandomState(2)
+X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state)
+data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])])
+data['Целевая переменная'] = y
+X = data.drop('Целевая переменная', axis=1)
+y = data['Целевая переменная']
+
+ridge = Ridge(alpha=1)  # Гребневая регрессия
+ridge.fit(X, y)
+
+recFE = RFE(LinearRegression(), n_features_to_select=1)  # Рекурсивное сокращение признаков
+recFE.fit(X, y)
+
+rfr = RandomForestRegressor()  # Сокращение признаков Случайными деревьями
+rfr.fit(X, y)
+
+models = [('Ridge', ridge),
+          ('RFE', recFE),
+          ('RFR', rfr)]
+model_scores = []
+
+for name, model in models:
+    if name == 'Ridge':
+        coef = model.coef_
+        normalized_coef = MinMaxScaler().fit_transform(coef.reshape(-1, 1))
+        model_scores.append((name, normalized_coef.flatten()))
+    elif name == 'RFE':
+        rankings = model.ranking_
+        normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1)
+        model_scores.append((name, normalized_rankings))
+    elif name == 'RFR':
+        feature_importances = model.feature_importances_
+        normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1))
+        model_scores.append((name, normalized_importances.flatten()))
+
+for name, scores in model_scores:
+    print(f"{name} оценки признаков:")
+    for feature, score in enumerate(scores, start=1):
+        print(f"Признак {feature}: {score:.2f}")
+    print(f"Средняя оценка: {np.mean(scores):.2f}")
+
+all_feature_scores = np.mean(list(map(lambda x: x[1], model_scores)), axis=0)
+sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True)
+top_features = sorted_features[:4]
+print("Четыре наиболее важных признака:")
+for feature, score in top_features:
+    print(f"Признак {feature}: {score:.2f}")

basharin_sevastyan_lab_3/README.md

@@ -0,0 +1,93 @@
+## Лабораторная работа 3. Вариант 4.
+### Задание 
+Выполнить ранжирование признаков и решить с помощью библиотечной реализации дерева решений 
+задачу классификации на 99% данных из курсовой работы. Проверить 
+работу модели на оставшемся проценте, сделать вывод.
+
+Модель:
+- Дерево решений `DecisionTreeClassifier`.
+
+### Как запустить
+Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать:
+``` python
+python main.py
+```
+
+### Используемые технологии
+- Библиотека `pandas`, используемая для работы с данными для анализа scv формата.
+- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули:
+  - `metrics` - набор инструменов для оценки моделей
+  - `DecisionTreeClassifier` - классификатор, реализующий алгоритм дерева решений. Дерево решений - это модель машинного обучения, которая разбивает данные на рекурсивные решения на основе значений признаков. Она используется для задач классификации и регрессии.
+  - `accuracy_score` -функция из scikit-learn, которая используется для оценки производительности модели классификации путем вычисления доли правильно классифицированных примеров (точности) на тестовом наборе данных.
+  - `train_test_split` - это функция из scikit-learn, используемая для разделения набора данных на обучающий и тестовый наборы.
+  - `LabelEncoder` - это класс из scikit-learn, используемый для преобразования категориальных признаков (например, строки) в числовые значения.
+
+### Описание работы
+#### Описание набора данных
+Набор данных: набор данных о цене автомобиля в автопарке.
+
+Названия столбцов набора данных и их описание:
+
+- Id: Уникальный идентификатор для каждого автомобиля в списке.
+- Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+- Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+- Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+- Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+- Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+- Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+- Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+- Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+- Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+- Assembly: Импорт или местный рынок.
+- Body Type: Тип кузова.
+- Transmission Type: Тип трансмиссии.
+- Registration Status: Статус регистрации.
+
+Ссылка на страницу набора на kuggle: [Ultimate Car Price Prediction Dataset](https://www.kaggle.com/datasets/mohidabdulrehman/ultimate-car-price-prediction-dataset/data)
+
+#### Оцифровка и нормализация данных
+Для нормальной работы с данными, необходимо исключить из них все нечисловые значения. 
+После этого, представить все строковые значения параметров как числовые и очистить датасет от "мусора". 
+Для удаления нечисловых значений воспользуемся функцией `.dropna()`.
+Так же мы удаляем первый столбец `Id`, так как при открытии файла в `pd` он сам нумерует строки.
+
+Все нечисловые значения мы преобразуем в числовые с помощью `LabelEncoder`:
+```python
+label_encoder = LabelEncoder()
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+```
+
+#### Выявление значимых параметров
+```python
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+```
+
+#### Решение задачи кластеризации на полном наборе признаков 
+Чтобы решить задачу кластеризации моделью `DecisionTreeClassifier`, воспользуемся методом `.predict()`.
+```python
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+```
+
+#### Оценка эффективности
+Для оценки точности модели будем использовать встроенный инструмент `accuracy_score`:
+```python
+accuracy = accuracy_score(y_test, y_pred)
+```
+
+#### Результаты
+![](accuracy.png "Точность")
+
+![](important.png "Важность признаков")

basharin_sevastyan_lab_3/main.py

@@ -0,0 +1,78 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+''' Названия столбцов набора данных и их описание:
+Id: Уникальный идентификатор для каждого автомобиля в списке.
+Price: Ценовой диапазон автомобилей с конкретными ценниками и подсчетами. (111000 - 77500000)
+Company Name: Название компании-производителя автомобилей с указанием процентной доли представительства каждой компании.
+Model Name: Название модели автомобилей с указанием процентного соотношения каждой модели.
+Model Year: Диапазон лет выпуска автомобилей с указанием количества и процентных соотношений. (1990 - 2019)
+Location: Местоположение автомобилей с указанием регионов, где они доступны для покупки, а также их процентное соотношение.
+Mileage: Информация о пробеге автомобилей с указанием диапазонов пробега, количества и процентов. (1 - 999999)
+Engine Type: Описания типов двигателей с процентными соотношениями для каждого типа.
+Engine Capacity: Мощность двигателя варьируется в зависимости от количества и процентов. (16 - 6600)
+Color: Цветовое распределение автомобилей с указанием процентных соотношений для каждого цвета.
+'''
+
+
+# Загрузите данные из вашей курсовой работы, предположим, что у вас есть файл CSV.
+data = pd.read_csv('Data_pakwheels.csv')
+data.pop("Id")
+
+data.dropna(inplace=True)  # Удаление строки с пропущенными значениями.
+
+# Преобразуйте категориальные признаки в числовые. Используйте, например, one-hot encoding.
+# data = pd.get_dummies(data, columns=['Company Name', 'Model Name', 'Location', 'Engine Type', 'Color'])
+
+# Создайте объект LabelEncoder
+label_encoder = LabelEncoder()
+
+data['Location'] = label_encoder.fit_transform(data['Location'])
+data['Company Name'] = label_encoder.fit_transform(data['Company Name'])
+data['Model Name'] = label_encoder.fit_transform(data['Model Name'])
+data['Engine Type'] = label_encoder.fit_transform(data['Engine Type'])
+data['Color'] = label_encoder.fit_transform(data['Color'])
+data['Assembly'] = label_encoder.fit_transform(data['Assembly'])
+data['Body Type'] = label_encoder.fit_transform(data['Body Type'])
+data['Transmission Type'] = label_encoder.fit_transform(data['Transmission Type'])
+data['Registration Status'] = label_encoder.fit_transform(data['Registration Status'])
+
+# Разделение данных на обучающий набор и тестовый набор. Мы будем использовать 99% данных для обучения.
+train_data, test_data = train_test_split(data, test_size=0.01, random_state=42)
+
+# Определите целевую переменную (то, что вы пытаетесь предсказать, например, 'Price').
+X_train = train_data.drop(columns=['Price'])
+y_train = train_data['Price']
+X_test = test_data.drop(columns=['Price'])
+y_test = test_data['Price']
+
+# Создание и обучение модели DecisionTreeClassifier
+clf = DecisionTreeClassifier(random_state=42)
+clf.fit(X_train, y_train)
+
+# Оценка важности признаков
+feature_importances = clf.feature_importances_
+
+# Создание DataFrame с именами признаков и их важностью
+feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})
+
+# Сортировка признаков по убыванию важности
+feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)
+
+# Вывод ранжированных признаков
+print(feature_importance_df)
+
+clf = DecisionTreeClassifier(max_depth=5, random_state=42)
+
+# Обучите модель на обучающем наборе данных
+clf.fit(X_train, y_train)
+
+# Предсказание целевой переменной на тестовом наборе данных
+y_pred = clf.predict(X_test)
+
+# Оцените производительность модели с помощью различных метрик
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Точность модели: {accuracy}')

belyaeva_ekaterina_lab_3/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_3/README.md

@@ -0,0 +1,35 @@
+## Задание 
+
+Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы  искусственного  интеллекта»на  99%  ваших  данных.  Проверьте работу модели на оставшемся проценте, сделайте вывод
+
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача определить винрейт персонажа на ранге рекрут в зависимости от его атрибута, роли (я взяла 2 - саппорт или керри), и того, как часто его берут на рекрутах.
+
+Программа берет столбцы Herald Win Rate, Primary Attribute, Herald Picks и Roles, далее проводит фильтрацию столбца Roles и выбирает тех персонажей, у которых есть роль support или carry. Затем создает
+два новых столбца - isCarry и isSupport, так как в столбце Roles несколько значений и его нужно удалить.
+
+Затем данные делятся на обучающую и тестовую выборки и выясняется зависимость винрейта от остальных признаков.
+
+В конце программа выводит, насколько важны были выбранные признаки при определении винрейта и точность модели. 
+
+
+## Результат
+
+В результате получаем следующее:
+
+Feature Importances: [0.08035262 0.82893841 0.00453277 0.08617619]
+Score: 0.23055568233652535
+
+Вывод: самым значимым признаком при определении винрейта стал признак Primary Attribute. На фоне других признаков его значимость сильно выделяется, все остальные признаки уже играют очень маленькую роль.
+
+Точность модели вышла относительно низкой, но это легко объясняется тем, что в Доте невозможно точно предсказать винрейт персонажа, основываясь на подобных признаках. Винрейт предсказывается только лишь тем, какие персонажи сильны в данной мете, что зависит от их скиллов и изменений патча, не описанных в датасете (но и нет такого датасета, где они могли бы быть описаны).
+
+Тем не менее, данная программа дала понять, что на рекрутах на винрейт персонажа сильно влияет его главный атрибут.
+

belyaeva_ekaterina_lab_3/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+
+# Загрузка данных
+data = pd.read_csv("Current_Pub_Meta.csv")
+
+# Отбор нужных столбцов
+selected_columns = ['Herald Win Rate', 'Primary Attribute', 'Herald Picks', 'Roles']
+data = data[selected_columns]
+
+# Фильтрация по ролям Carry и Support
+data = data[data['Roles'].apply(lambda x: 'Carry' in x or 'Support' in x)]
+
+# Создание столбцов для каждой роли и заполнение их значениями 1 или 0
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+
+# Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Замена категориальных переменных на числовые
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+# Разделение данных на обучающую и тестовую выборки
+X = data.drop('Herald Win Rate', axis=1)
+y = data['Herald Win Rate']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Обучение модели
+model = DecisionTreeRegressor()
+model.fit(X_train, y_train)
+
+# Прогноз на тестовой выборке
+y_pred = model.predict(X_test)
+
+# Вывод обработанных данных
+print("Обработанные данные:")
+print(data)
+
+# Оценка значимости признаков
+feature_importances = model.feature_importances_
+print("Feature Importances:", feature_importances)
+
+# Оценка score модели
+score = model.score(X_test, y_test)
+print("Score:", score)

belyaeva_ekaterina_lab_4/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_4/README.md

@@ -0,0 +1,31 @@
+## Задание 
+
+Использовать метод кластеризациипо варианту для данных из таблицы 1  по  варианту(таблица  9),самостоятельно  сформулировав  задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной вами задачи  
+Вариант 6 - dendogram
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scipy, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача сгруппировать персонажей по их винрейту и частоте их пиков на определенных рангах.
+
+Программа берет столбцы Name, Herald Win Rate, Herald Picks, создает матрицу для анализа и вычисляет матрицу связей, а затем выводит дендограмму, где персонажи объединены по тому, как часто их пикают и какой у них винрейт.
+
+## Результат
+
+В результате получаем дендограмму, где персонажи сгруппированы по частоте пиков и винрейту. Наглядное представление оказалось очень точным и такой способ решения поставленной задачи выполнил свою работу хорошо.
+
+Например, на диаграмме ниже можно обратить внимание на то, что на ранге рекрут персонажи Phantom Asassin, Witch Doctor, Sniper и Pudge стоят вместе в правом нижнем углу. Такое наблюдение говорит о том, что датасет очень приближен к реальным данным и составлен правильно, а так же о том, что программа работает верно и выдает правильный, приближенный к реальности, результат.
+
+![heraldInfo.png](heraldInfo.png)
+
+Если же посмотреть на результат по данным для ранга титан, можно увидеть других героев, объединенных друг с другом по тому же приципу.
+
+![ImmortalInfo.png](ImmortalInfo.png)
+
+Сначала я хотела объединить героев по их винрейту на всех рангах, но такая информация не несет в себе много смысла, поэтому задача, которую я описала выше, сформулирована правильно, несет в себе смысл и решается заданным способом. 
+
+Такую статистику можно посмотреть по любому из рангов, заменив в коде слово Herald на интересующий ранг.

belyaeva_ekaterina_lab_4/main.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.cluster.hierarchy import dendrogram, linkage
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+# Выбор нужных столбцов
+selected_columns = ['Name', 'Herald Picks', 'Herald Win Rate']
+data = data[selected_columns]
+
+# Создание матрицы для анализа
+matrix = data.drop('Name', axis=1).values
+
+# Вычисление матрицы связей
+linked = linkage(matrix, 'ward')
+
+# Рисование дендрограммы
+plt.figure(figsize=(10, 6))
+dendrogram(linked,
+           orientation='top',
+           labels=data['Name'].tolist(),
+           distance_sort='descending',
+           show_leaf_counts=True)
+plt.title('Dendrogram of Hero Win Percentage')
+plt.xlabel('Heroes')
+plt.ylabel('Distance')
+plt.xticks(rotation=90)
+plt.show()

belyaeva_ekaterina_lab_5/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_5/README.md

@@ -0,0 +1,42 @@
+## Задание 
+
+Использовать  регрессию по  варианту для  данных  из  таблицы  1  по варианту(таблица  10),самостоятельно  сформулировав  задачу.  Оценить, насколько хорошо она подходит для решения сформулированной вами задачи  
+Вариант 6 - полиномиальная регрессия
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, matplotlib, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача предсказать винрейт персонажа по тому, как часто его берут и по его винрейту на 
+смежных рангах (просто предсказать винрейт по тому, как часто его берут, нельзя, потому что винрейт зависит от текущей меты)
+
+Программа берет столбцы Name, Archon Picks, Archon Win Rate, Legend Picks, Legend Win Rate, Ancient Picks, Ancient Win Rate.
+Все столбцы, кроме Name и Legend Win Rate, нужны для того чтобы обучить модель. Legend Win Rate - 
+данные, которые нужно предсказать. Name - столбец для вывода результатов.
+
+Дальше все по дефолту - программа делит данные на обучающую и тестовые выборки, просиходит
+применение данных для обучения, затем обучаем модель. После этого происходит то же самое с тестовыми данными и затем выводится 
+оценка качества модели.
+
+В конце программа строит график, где показывает точки обучающей и тестовой выборки, но к тестовой выборки я решила добавить названия
+персонажей, чтобы график был более наглядным, но в то же время не перегруженным.
+
+## Результат
+
+В результате получаем график, который показывает результаты обучающей и тестовой выборок.
+![diagram.png](diagram.png)
+
+Помимо этого, программа вводит оценку качества модели:
+![R2Score.png](R2Score.png)
+
+Из чего можно сделать вывод, что модель работает очень хорошо и успешно решает поставленную задачу.
+
+Это объясняется тем, что модели было предоставлено достаточно большое количество признаков, по которым можно предсказать 
+интересующие нас данные. Кроме того, винрейт персонажей взят со смежных рангов. 
+
+Если взять винрейт персонажей на рангах, которые 
+находятся далеко от целевого, модель будет работать хуже, потому что чем больше разница в рангах, тем более разный винрейт у персонажей.
+Также, если бы было взято меньше признаков, оценка качества модели так же была бы ниже.

belyaeva_ekaterina_lab_5/main.py

@@ -0,0 +1,47 @@
+import pandas as pd
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+# Загрузка данных
+data = pd.read_csv('Current_Pub_Meta.csv')
+
+X = data[['Archon Picks', 'Archon Win Rate', 'Legend Picks', 'Ancient Picks', 'Ancient Win Rate']]
+y = data['Legend Win Rate']
+names = data['Name']
+
+# Разбиваем данные на обучающую и тестовую выборки
+X_train, X_test, y_train, y_test, names_train, names_test = train_test_split(X, y, names, test_size=0.1, random_state=42)
+
+# Применяем полиномиальные признаки к обучающим данным
+poly_features = PolynomialFeatures(degree=2)
+X_train_poly = poly_features.fit_transform(X_train)
+
+# Создаем и обучаем модель полиномиальной регрессии
+poly_model = LinearRegression()
+poly_model.fit(X_train_poly, y_train)
+
+# Применяем полиномиальные признаки к тестовым данным и делаем предсказания
+X_test_poly = poly_features.transform(X_test)
+y_pred = poly_model.predict(X_test_poly)
+
+# Оценка качества модели на тестовых данных
+r2 = poly_model.score(X_test_poly, y_test)
+print(f"R-квадрат: {r2}")
+
+# Построение графика с именами персонажей
+plt.figure(figsize=(10, 6))
+plt.title('Корреляция между выбором персонажей и победами в ранге "Legend"')
+plt.grid(True)
+plt.scatter(X_train['Legend Picks'], y_train, color='blue', alpha=0.5, label='Обучающая выборка')
+plt.scatter(X_test['Legend Picks'], y_test, color='red', alpha=0.5, label='Тестовая выборка')
+
+# Добавляем имена персонажей на график
+for i, name in enumerate(names_test):
+    plt.annotate(name, (X_test['Legend Picks'].iloc[i], y_pred[i]), fontsize=8, alpha=0.7, color='black')
+
+plt.xlabel('Legend Picks')
+plt.ylabel('Legend Win Rate')
+plt.legend()
+plt.show()

belyaeva_ekaterina_lab_6/Current_Pub_Meta.csv

@@ -0,0 +1,125 @@
+,Name,Primary Attribute,Roles,Herald Picks,Herald Wins,Herald Win Rate,Guardian Picks,Guardian Wins,Guardian Win Rate,Crusader Picks,Crusader Wins,Crusader Win Rate,Archon Picks,Archon Wins,Archon Win Rate,Legend Picks,Legend Wins,Legend Win Rate,Ancient Picks,Ancient Wins,Ancient Win Rate,Divine Picks,Divine Wins,Divine Win Rate,Immortal Picks,Immortal Wins,Immortal Win Rate
+0,Abaddon,all,"Support, Carry, Durable",1111,575,51.76,6408,3309,51.64,13811,7050,51.05,16497,8530,51.71,11360,5877,51.73,5571,2893,51.93,2632,1345,51.1,991,497,50.15
+1,Alchemist,str,"Carry, Support, Durable, Disabler, Initiator, Nuker",1119,486,43.43,6370,2883,45.26,12238,5617,45.9,13028,6130,47.05,8455,4055,47.96,4120,1984,48.16,2021,1023,50.62,860,424,49.3
+2,Ancient Apparition,int,"Support, Disabler, Nuker",2146,1073,50.0,13697,7069,51.61,30673,16118,52.55,35145,18219,51.84,23114,12166,52.63,10688,5528,51.72,5035,2573,51.1,2134,1076,50.42
+3,Anti-Mage,agi,"Carry, Escape, Nuker",3765,1818,48.29,22050,10774,48.86,47371,23304,49.19,49115,24074,49.02,28599,13991,48.92,12303,5958,48.43,4866,2349,48.27,1502,751,50.0
+4,Arc Warden,agi,"Carry, Escape, Nuker",1448,704,48.62,8047,4162,51.72,14946,7982,53.41,14711,7875,53.53,9472,5167,54.55,4323,2309,53.41,2104,1148,54.56,789,435,55.13
+5,Axe,str,"Initiator, Durable, Disabler, Carry",5343,2880,53.9,32652,17719,54.27,71010,37736,53.14,77869,40559,52.09,49182,25079,50.99,22637,11353,50.15,10114,5000,49.44,3795,1837,48.41
+6,Bane,all,"Support, Disabler, Nuker, Durable",745,334,44.83,4983,2422,48.61,11332,5504,48.57,13633,6767,49.64,10132,5032,49.66,5596,2861,51.13,3028,1555,51.35,1958,1055,53.88
+7,Batrider,all,"Initiator, Disabler, Escape",349,136,38.97,1983,812,40.95,4053,1595,39.35,4725,1861,39.39,3173,1275,40.18,1678,731,43.56,802,362,45.14,497,227,45.67
+8,Beastmaster,all,"Initiator, Disabler, Durable, Nuker",402,174,43.28,2447,1060,43.32,5787,2569,44.39,6930,3092,44.62,5288,2389,45.18,2816,1274,45.24,1593,752,47.21,1176,539,45.83
+9,Bloodseeker,agi,"Carry, Disabler, Nuker, Initiator",2765,1382,49.98,12589,6270,49.81,21781,10683,49.05,20961,10420,49.71,13035,6430,49.33,6210,3006,48.41,2941,1475,50.15,1465,718,49.01
+10,Bounty Hunter,agi,"Escape, Nuker",3852,1868,48.49,19609,9535,48.63,36362,17600,48.4,37059,18314,49.42,22934,11518,50.22,10584,5276,49.85,5105,2594,50.81,2498,1325,53.04
+11,Brewmaster,all,"Carry, Initiator, Durable, Disabler, Nuker",545,280,51.38,3564,1745,48.96,8941,4388,49.08,12340,6111,49.52,11185,5623,50.27,7645,3906,51.09,4812,2478,51.5,3533,1820,51.51
+12,Bristleback,str,"Carry, Durable, Initiator, Nuker",5884,3262,55.44,27952,14587,52.19,48847,24379,49.91,46702,22927,49.09,27466,13319,48.49,12398,5969,48.14,5865,2915,49.7,2639,1304,49.41
+13,Broodmother,all,"Carry, Pusher, Escape, Nuker",456,173,37.94,2048,842,41.11,3444,1462,42.45,3392,1448,42.69,2193,1048,47.79,1203,602,50.04,795,422,53.08,453,230,50.77
+14,Centaur Warrunner,str,"Durable, Initiator, Disabler, Nuker, Escape",1721,911,52.93,11754,6266,53.31,28691,15201,52.98,35369,18741,52.99,25393,13468,53.04,12653,6607,52.22,6124,3181,51.94,2442,1243,50.9
+15,Chaos Knight,str,"Carry, Disabler, Durable, Pusher, Initiator",3032,1639,54.06,16762,8931,53.28,31892,17139,53.74,30697,16435,53.54,18217,9810,53.85,8572,4620,53.9,4230,2291,54.16,1750,943,53.89
+16,Chen,all,"Support, Pusher",284,125,44.01,1450,678,46.76,2969,1345,45.3,3258,1604,49.23,2641,1331,50.4,1488,767,51.55,970,512,52.78,770,448,58.18
+17,Clinkz,agi,"Carry, Escape, Pusher",3151,1608,51.03,13891,7141,51.41,25465,12938,50.81,27327,14066,51.47,18846,9726,51.61,9452,4890,51.74,4765,2475,51.94,2093,1052,50.26
+18,Clockwerk,all,"Initiator, Disabler, Durable, Nuker",816,397,48.65,5860,2837,48.41,14478,6929,47.86,18466,8843,47.89,13143,6301,47.94,6612,3169,47.93,3286,1581,48.11,1378,658,47.75
+19,Crystal Maiden,int,"Support, Disabler, Nuker",4821,2529,52.46,26584,13626,51.26,52168,26040,49.92,52258,25365,48.54,30690,14848,48.38,13295,6404,48.17,5602,2680,47.84,1638,771,47.07
+20,Dark Seer,all,"Initiator, Escape, Disabler",627,320,51.04,3675,1884,51.27,7881,3803,48.26,9589,4844,50.52,7186,3573,49.72,3902,1983,50.82,2145,1095,51.05,1217,593,48.73
+21,Dark Willow,all,"Support, Nuker, Disabler, Escape",2654,1293,48.72,13829,6657,48.14,28142,13480,47.9,32114,15785,49.15,23100,11331,49.05,12052,5909,49.03,6400,3182,49.72,3708,1915,51.65
+22,Dawnbreaker,str,"Carry, Durable",1746,875,50.11,12297,6105,49.65,32398,15921,49.14,44846,21936,48.91,35474,17441,49.17,19770,9832,49.73,10637,5263,49.48,6339,3173,50.06
+23,Dazzle,all,"Support, Nuker, Disabler",2827,1418,50.16,19852,9758,49.15,48236,23691,49.11,56417,27798,49.27,38159,18642,48.85,18695,9199,49.21,8530,4239,49.7,3382,1654,48.91
+24,Death Prophet,int,"Carry, Pusher, Nuker, Disabler",1372,659,48.03,6643,3145,47.34,11987,5729,47.79,12268,5856,47.73,7455,3606,48.37,3591,1698,47.28,1872,902,48.18,926,459,49.57
+25,Disruptor,int,"Support, Disabler, Nuker, Initiator",1541,757,49.12,11104,5331,48.01,27746,13542,48.81,33742,16310,48.34,23173,11096,47.88,10907,5201,47.68,4859,2255,46.41,1863,861,46.22
+26,Doom,str,"Carry, Disabler, Initiator, Durable, Nuker",1049,474,45.19,6112,2767,45.27,13700,6056,44.2,15454,6925,44.81,10727,4842,45.14,5444,2451,45.02,2979,1348,45.25,1545,731,47.31
+27,Dragon Knight,str,"Carry, Pusher, Durable, Disabler, Initiator, Nuker",1950,942,48.31,10643,5274,49.55,20451,9733,47.59,20326,9671,47.58,11674,5544,47.49,4979,2355,47.3,2024,973,48.07,725,341,47.03
+28,Drow Ranger,agi,"Carry, Disabler, Pusher",5737,2904,50.62,29675,14831,49.98,57655,28573,49.56,56682,27927,49.27,34310,16607,48.4,15050,7171,47.65,5947,2815,47.33,1768,788,44.57
+29,Earth Spirit,str,"Nuker, Escape, Disabler, Initiator, Durable",1038,465,44.8,7420,3276,44.15,20807,9432,45.33,30107,14166,47.05,25314,12148,47.99,14579,7041,48.3,7678,3802,49.52,4379,2169,49.53
+30,Earthshaker,str,"Support, Initiator, Disabler, Nuker",5012,2455,48.98,29784,14662,49.23,67050,33111,49.38,79963,39843,49.83,57108,28961,50.71,28650,14591,50.93,14186,7296,51.43,6151,3165,51.46
+31,Elder Titan,str,"Initiator, Disabler, Nuker, Durable",471,212,45.01,2551,1248,48.92,5213,2570,49.3,5572,2809,50.41,3847,1942,50.48,1964,998,50.81,1124,613,54.54,550,292,53.09
+32,Ember Spirit,agi,"Carry, Escape, Nuker, Disabler, Initiator",1514,635,41.94,9180,3836,41.79,20578,8738,42.46,25152,10844,43.11,17703,7814,44.14,8538,3793,44.42,4265,1892,44.36,2065,928,44.94
+33,Enchantress,int,"Support, Pusher, Durable, Disabler",1794,848,47.27,8050,3622,44.99,12921,5686,44.01,11673,4974,42.61,6863,2840,41.38,2948,1212,41.11,1434,654,45.61,806,318,39.45
+34,Enigma,all,"Disabler, Initiator, Pusher",1317,588,44.65,6937,3171,45.71,12908,5979,46.32,11687,5428,46.44,6194,2839,45.83,2493,1127,45.21,938,437,46.59,338,159,47.04
+35,Faceless Void,agi,"Carry, Initiator, Disabler, Escape, Durable",4323,2043,47.26,25618,11902,46.46,54581,25874,47.4,60671,28993,47.79,40137,19611,48.86,19376,9620,49.65,9579,4828,50.4,4439,2256,50.82
+36,Grimstroke,int,"Support, Nuker, Disabler, Escape",1455,694,47.7,9714,4789,49.3,24688,12430,50.35,32027,16094,50.25,23193,11795,50.86,12102,6100,50.4,6191,3047,49.22,3449,1666,48.3
+37,Gyrocopter,agi,"Carry, Nuker, Disabler",2560,1213,47.38,16589,7882,47.51,42072,20358,48.39,54200,26229,48.39,39414,19053,48.34,20164,9781,48.51,10164,4937,48.57,5241,2507,47.83
+38,Hoodwink,agi,"Support, Nuker, Escape, Disabler",2420,1126,46.53,14034,6800,48.45,31382,14964,47.68,35684,16966,47.55,22626,10651,47.07,9949,4690,47.14,4349,2089,48.03,1533,703,45.86
+39,Huskar,str,"Carry, Durable, Initiator",3501,1603,45.79,14234,6639,46.64,22794,10912,47.87,21801,10763,49.37,13811,6919,50.1,6769,3535,52.22,3556,1822,51.24,1936,993,51.29
+40,Invoker,all,"Carry, Nuker, Disabler, Escape, Pusher",4330,2042,47.16,27625,13176,47.7,69035,33863,49.05,86745,43479,50.12,61821,31510,50.97,31459,16321,51.88,15431,8195,53.11,7852,4148,52.83
+41,Io,all,"Support, Escape, Nuker",1274,615,48.27,6158,2999,48.7,12762,6247,48.95,14216,7024,49.41,9564,4843,50.64,5301,2685,50.65,2789,1463,52.46,1464,773,52.8
+42,Jakiro,int,"Support, Nuker, Pusher, Disabler",3147,1708,54.27,22718,12413,54.64,56736,30984,54.61,70038,37473,53.5,46389,24997,53.89,22084,11639,52.7,9838,5103,51.87,3282,1729,52.68
+43,Juggernaut,agi,"Carry, Pusher, Escape",5585,2711,48.54,30394,14800,48.69,62313,30581,49.08,65590,32344,49.31,39235,19326,49.26,16334,8012,49.05,6419,3066,47.76,1576,731,46.38
+44,Keeper of the Light,int,"Support, Nuker, Disabler",896,353,39.4,5051,2216,43.87,10452,4579,43.81,11614,5322,45.82,7870,3627,46.09,4268,2001,46.88,2147,1043,48.58,1333,588,44.11
+45,Kunkka,str,"Carry, Support, Disabler, Initiator, Durable, Nuker",2251,1124,49.93,13474,6828,50.68,31210,16196,51.89,39691,21293,53.65,30314,16458,54.29,15706,8793,55.98,7884,4339,55.04,3458,1898,54.89
+46,Legion Commander,str,"Carry, Disabler, Initiator, Durable, Nuker",6263,3264,52.12,37100,19157,51.64,81491,41557,51.0,91431,46558,50.92,59383,29917,50.38,27945,13917,49.8,13193,6587,49.93,5601,2745,49.01
+47,Leshrac,int,"Carry, Support, Nuker, Pusher, Disabler",674,316,46.88,3872,1799,46.46,7490,3433,45.83,7903,3604,45.6,5322,2526,47.46,2687,1298,48.31,1325,647,48.83,721,357,49.51
+48,Lich,int,"Support, Nuker",2700,1412,52.3,16646,8820,52.99,37785,19685,52.1,45471,23554,51.8,31203,16108,51.62,15530,7821,50.36,7243,3597,49.66,2520,1258,49.92
+49,Lifestealer,str,"Carry, Durable, Escape, Disabler",2515,1213,48.23,14131,6978,49.38,29724,14627,49.21,31211,15581,49.92,18970,9481,49.98,8689,4400,50.64,3630,1821,50.17,1229,617,50.2
+50,Lina,int,"Support, Carry, Nuker, Disabler",4512,2030,44.99,21927,10156,46.32,45301,21210,46.82,54229,25956,47.86,40016,19138,47.83,21072,10112,47.99,10481,5031,48.0,4369,2138,48.94
+51,Lion,int,"Support, Disabler, Nuker, Initiator",6204,2855,46.02,37869,17465,46.12,80124,36649,45.74,84390,38176,45.24,50720,22914,45.18,21698,9784,45.09,9308,4280,45.98,3220,1496,46.46
+52,Lone Druid,all,"Carry, Pusher, Durable",909,483,53.14,4714,2421,51.36,10987,5858,53.32,14580,7968,54.65,11810,6490,54.95,7241,3971,54.84,4024,2240,55.67,2303,1259,54.67
+53,Luna,agi,"Carry, Nuker, Pusher",1927,904,46.91,9091,4271,46.98,16571,7922,47.81,16035,7615,47.49,9728,4634,47.64,4463,2103,47.12,1912,911,47.65,719,322,44.78
+54,Lycan,all,"Carry, Pusher, Durable, Escape",374,174,46.52,1894,915,48.31,3691,1744,47.25,3824,1905,49.82,2694,1332,49.44,1460,753,51.58,827,411,49.7,532,289,54.32
+55,Magnus,all,"Initiator, Disabler, Nuker, Escape",770,339,44.03,5789,2651,45.79,17837,7954,44.59,26126,12058,46.15,20634,9592,46.49,10574,5056,47.82,4565,2073,45.41,1606,751,46.76
+56,Marci,all,"Support, Carry, Initiator, Disabler, Escape",1370,620,45.26,7092,3252,45.85,15199,7240,47.63,18485,8874,48.01,13308,6305,47.38,7176,3476,48.44,3689,1882,51.02,1746,883,50.57
+57,Mars,str,"Carry, Initiator, Disabler, Durable",862,375,43.5,5719,2529,44.22,15156,6756,44.58,20719,9369,45.22,16419,7387,44.99,9044,4052,44.8,4536,2093,46.14,1926,868,45.07
+58,Medusa,agi,"Carry, Disabler, Durable",1898,902,47.52,9289,4512,48.57,16504,7818,47.37,14796,6886,46.54,7488,3449,46.06,2775,1270,45.77,1073,482,44.92,394,184,46.7
+59,Meepo,agi,"Carry, Escape, Nuker, Disabler, Initiator, Pusher",1004,523,52.09,3970,1990,50.13,6904,3587,51.96,7166,3646,50.88,4906,2563,52.24,2383,1282,53.8,1139,588,51.62,585,300,51.28
+60,Mirana,all,"Carry, Support, Escape, Nuker, Disabler",2499,1193,47.74,16954,8135,47.98,39985,19097,47.76,45169,21554,47.72,28467,13456,47.27,12800,6047,47.24,5272,2500,47.42,1824,874,47.92
+61,Monkey King,agi,"Carry, Escape, Disabler, Initiator",3191,1384,43.37,17306,7544,43.59,35734,16113,45.09,40778,18322,44.93,27558,12630,45.83,14034,6433,45.84,6650,3152,47.4,3040,1440,47.37
+62,Morphling,agi,"Carry, Escape, Durable, Nuker, Disabler",1521,690,45.36,8620,4006,46.47,18075,8161,45.15,20414,9235,45.24,14395,6530,45.36,7697,3551,46.13,4432,2050,46.25,2560,1190,46.48
+63,Muerta,int,"Carry, Nuker, Disabler",2130,1089,51.13,10787,5740,53.21,22602,11898,52.64,27609,14495,52.5,20175,10465,51.87,10662,5518,51.75,5462,2759,50.51,2948,1517,51.46
+64,Naga Siren,agi,"Carry, Support, Pusher, Disabler, Initiator, Escape",1502,804,53.53,6495,3356,51.67,10423,5234,50.22,9830,4929,50.14,6057,2971,49.05,3216,1675,52.08,1855,933,50.3,1242,634,51.05
+65,Nature's Prophet,int,"Carry, Pusher, Escape, Nuker",5991,3029,50.56,36433,18143,49.8,83118,42095,50.64,100341,51268,51.09,69436,35870,51.66,34256,17858,52.13,16585,8745,52.73,7182,3755,52.28
+66,Necrophos,int,"Carry, Nuker, Durable, Disabler",4776,2702,56.57,28535,15771,55.27,62186,34285,55.13,70212,38163,54.35,46539,24708,53.09,21607,11302,52.31,9677,4994,51.61,3418,1733,50.7
+67,Night Stalker,str,"Carry, Initiator, Durable, Disabler, Nuker",1189,594,49.96,7868,3892,49.47,19446,10004,51.45,25524,13506,52.91,20138,10828,53.77,10767,5651,52.48,5499,2889,52.54,2415,1257,52.05
+68,Nyx Assassin,all,"Disabler, Nuker, Initiator, Escape",1718,867,50.47,10925,5525,50.57,27207,14073,51.73,34684,18059,52.07,25736,13572,52.74,13313,7093,53.28,6485,3444,53.11,2852,1468,51.47
+69,Ogre Magi,str,"Support, Nuker, Disabler, Durable, Initiator",5331,2845,53.37,31507,16299,51.73,62954,32248,51.22,61758,31373,50.8,33746,16988,50.34,13262,6654,50.17,4861,2420,49.78,1271,654,51.46
+70,Omniknight,str,"Support, Durable, Nuker",975,479,49.13,6426,3109,48.38,14641,7319,49.99,17258,8731,50.59,11695,5916,50.59,5746,2993,52.09,2870,1469,51.18,1333,656,49.21
+71,Oracle,int,"Support, Nuker, Disabler, Escape",796,384,48.24,4857,2417,49.76,13141,6645,50.57,18944,9853,52.01,15221,7964,52.32,8356,4458,53.35,4475,2380,53.18,1905,1018,53.44
+72,Outworld Destroyer,int,"Carry, Nuker, Disabler",2226,1118,50.22,13388,6864,51.27,33284,17362,52.16,43991,23377,53.14,32021,16994,53.07,16655,8724,52.38,8123,4218,51.93,3176,1649,51.92
+73,Pangolier,all,"Carry, Nuker, Disabler, Durable, Escape, Initiator",1156,534,46.19,7189,3209,44.64,17802,7937,44.58,25785,11677,45.29,21727,10144,46.69,13064,6351,48.61,7567,3737,49.39,5275,2734,51.83
+74,Phantom Assassin,agi,"Carry, Escape",8553,4426,51.75,48549,25553,52.63,104756,54881,52.39,119332,62511,52.38,79140,41143,51.99,37399,19325,51.67,17774,9077,51.07,7819,3856,49.32
+75,Phantom Lancer,agi,"Carry, Escape, Pusher, Nuker",3641,1960,53.83,19550,10374,53.06,38576,20633,53.49,41505,22310,53.75,26401,14268,54.04,12437,6590,52.99,5708,2985,52.3,2383,1243,52.16
+76,Phoenix,all,"Support, Nuker, Initiator, Escape, Disabler",743,315,42.4,5231,2471,47.24,13950,6633,47.55,18350,8864,48.31,13972,6715,48.06,7787,3761,48.3,4322,2132,49.33,2610,1325,50.77
+77,Primal Beast,str,"Initiator, Durable, Disabler",1455,701,48.18,9333,4448,47.66,22800,11058,48.5,30084,14643,48.67,24307,11993,49.34,13970,6991,50.04,7742,3890,50.25,4625,2407,52.04
+78,Puck,int,"Initiator, Disabler, Escape, Nuker",871,399,45.81,5773,2628,45.52,16596,7578,45.66,24480,11315,46.22,20070,9497,47.32,11023,5298,48.06,5656,2714,47.98,2555,1200,46.97
+79,Pudge,str,"Disabler, Initiator, Durable, Nuker",7677,3796,49.45,50891,24776,48.68,114784,56289,49.04,129604,63097,48.68,85800,41542,48.42,41730,20239,48.5,19823,9530,48.08,7112,3431,48.24
+80,Pugna,int,"Nuker, Pusher",2075,944,45.49,9998,4695,46.96,18962,8958,47.24,20240,9965,49.23,12807,6199,48.4,5825,2855,49.01,2758,1387,50.29,1195,592,49.54
+81,Queen of Pain,int,"Carry, Nuker, Escape",2287,1100,48.1,15119,7354,48.64,37137,18118,48.79,47706,23657,49.59,35500,18018,50.75,18405,9289,50.47,9243,4689,50.73,4227,2113,49.99
+82,Razor,agi,"Carry, Durable, Nuker, Pusher",2470,1231,49.84,12000,5964,49.7,24666,12142,49.23,30334,14844,48.94,21832,10558,48.36,11917,5679,47.65,6092,2912,47.8,3144,1551,49.33
+83,Riki,agi,"Carry, Escape, Disabler",3684,1929,52.36,19022,9891,52.0,35638,18582,52.14,33908,17415,51.36,20194,10312,51.06,8726,4377,50.16,3735,1855,49.67,1160,559,48.19
+84,Rubick,int,"Support, Disabler, Nuker",3090,1404,45.44,21639,9303,42.99,57417,24590,42.83,74874,32603,43.54,55186,24219,43.89,28206,12568,44.56,13732,6106,44.47,5764,2642,45.84
+85,Sand King,all,"Initiator, Disabler, Support, Nuker, Escape",2633,1513,57.46,13097,7323,55.91,25271,13807,54.64,26724,14323,53.6,17384,9144,52.6,7907,4104,51.9,3394,1719,50.65,1211,611,50.45
+86,Shadow Demon,int,"Support, Disabler, Initiator, Nuker",547,236,43.14,3252,1426,43.85,7920,3524,44.49,9752,4551,46.67,7404,3467,46.83,3956,1876,47.42,2076,1004,48.36,1054,497,47.15
+87,Shadow Fiend,agi,"Carry, Nuker",5051,2544,50.37,27255,14064,51.6,58589,29830,50.91,65429,33097,50.58,41810,21189,50.68,18766,9401,50.1,8232,4000,48.59,3016,1430,47.41
+88,Shadow Shaman,int,"Support, Pusher, Disabler, Nuker, Initiator",5323,2795,52.51,29733,15606,52.49,58894,31236,53.04,58765,30895,52.57,34475,18242,52.91,15166,7986,52.66,6377,3323,52.11,2413,1253,51.93
+89,Silencer,int,"Carry, Support, Disabler, Initiator, Nuker",4229,2324,54.95,27878,14960,53.66,61698,33081,53.62,65256,34458,52.8,38589,19853,51.45,16889,8653,51.23,6836,3416,49.97,2236,1105,49.42
+90,Skywrath Mage,int,"Support, Nuker, Disabler",4000,2030,50.75,22783,11675,51.24,46512,23624,50.79,51329,25706,50.08,34167,17364,50.82,16693,8415,50.41,8496,4208,49.53,4389,2069,47.14
+91,Slardar,str,"Carry, Durable, Initiator, Disabler, Escape",3935,2129,54.1,21523,11602,53.91,43947,23701,53.93,47721,25633,53.71,29887,16132,53.98,14233,7722,54.25,6530,3467,53.09,2322,1205,51.89
+92,Slark,agi,"Carry, Escape, Disabler, Nuker",4815,2521,52.36,29413,14762,50.19,64004,31771,49.64,70173,34411,49.04,44780,21926,48.96,20864,10270,49.22,9969,4962,49.77,4565,2394,52.44
+93,Snapfire,all,"Support, Nuker, Disabler, Escape",1524,682,44.75,10646,4576,42.98,27103,12120,44.72,34711,15412,44.4,24351,10786,44.29,11723,5131,43.77,5227,2294,43.89,1987,868,43.68
+94,Sniper,agi,"Carry, Nuker",8022,4079,50.85,44508,22727,51.06,88690,45223,50.99,87190,44086,50.56,47411,23648,49.88,18092,8924,49.33,6130,3040,49.59,1370,662,48.32
+95,Spectre,agi,"Carry, Durable, Escape",3454,2008,58.14,22097,12356,55.92,49157,26961,54.85,55914,30100,53.83,36321,19338,53.24,16946,8960,52.87,7921,4163,52.56,2568,1370,53.35
+96,Spirit Breaker,str,"Carry, Initiator, Disabler, Durable, Escape",4788,2423,50.61,26662,13530,50.75,56535,28908,51.13,63991,32249,50.4,42512,21357,50.24,20119,9926,49.34,9499,4814,50.68,3761,1884,50.09
+97,Storm Spirit,int,"Carry, Escape, Nuker, Initiator, Disabler",2202,1001,45.46,11656,5197,44.59,25644,11806,46.04,30968,14210,45.89,21680,10197,47.03,10810,5025,46.48,5278,2382,45.13,2363,1122,47.48
+98,Sven,str,"Carry, Disabler, Initiator, Durable, Nuker",3552,1761,49.58,19792,9744,49.23,41296,20478,49.59,48709,24228,49.74,35460,17828,50.28,19795,10065,50.85,11014,5655,51.34,6701,3387,50.54
+99,Techies,all,"Nuker, Disabler",2356,1131,48.01,13105,6245,47.65,27293,12893,47.24,29180,13507,46.29,18216,8407,46.15,8266,3771,45.62,3459,1644,47.53,1319,591,44.81
+100,Templar Assassin,agi,"Carry, Escape",2142,955,44.58,10932,4758,43.52,21211,9445,44.53,23928,10909,45.59,17399,8242,47.37,9567,4656,48.67,5525,2708,49.01,3524,1775,50.37
+101,Terrorblade,agi,"Carry, Pusher, Nuker",1115,484,43.41,5686,2430,42.74,10856,4638,42.72,11518,5041,43.77,8059,3540,43.93,4192,1827,43.58,2419,1082,44.73,1621,700,43.18
+102,Tidehunter,str,"Initiator, Durable, Disabler, Nuker, Carry",1835,855,46.59,11159,5369,48.11,26222,12699,48.43,30735,14879,48.41,20523,9727,47.4,9731,4740,48.71,4426,2079,46.97,1998,936,46.85
+103,Timbersaw,all,"Nuker, Durable, Escape",1050,448,42.67,5854,2584,44.14,12301,5391,43.83,14295,6097,42.65,9697,4217,43.49,4992,2163,43.33,2419,1021,42.21,1139,471,41.35
+104,Tinker,int,"Carry, Nuker, Pusher",2106,944,44.82,11058,5200,47.02,24263,11826,48.74,27531,13614,49.45,19017,9732,51.18,9416,4875,51.77,4700,2466,52.47,1951,1036,53.1
+105,Tiny,str,"Carry, Nuker, Pusher, Initiator, Durable, Disabler",1434,654,45.61,7742,3452,44.59,15936,6950,43.61,17139,7468,43.57,11269,4991,44.29,5485,2491,45.41,2599,1216,46.79,1058,519,49.05
+106,Treant Protector,str,"Support, Initiator, Durable, Disabler, Escape",1646,899,54.62,11430,5881,51.45,28752,15124,52.6,36093,19344,53.59,28762,15532,54.0,16751,9227,55.08,9870,5468,55.4,6801,3855,56.68
+107,Troll Warlord,agi,"Carry, Pusher, Disabler, Durable",3176,1720,54.16,14007,7445,53.15,24729,13022,52.66,25424,13228,52.03,17362,9030,52.01,9427,4913,52.12,4767,2499,52.42,2341,1242,53.05
+108,Tusk,str,"Initiator, Disabler, Nuker",1263,565,44.73,8338,3777,45.3,19642,8869,45.15,25308,11520,45.52,18927,8853,46.77,10100,4820,47.72,5220,2502,47.93,2350,1157,49.23
+109,Underlord,str,"Support, Nuker, Disabler, Durable, Escape",797,405,50.82,4583,2341,51.08,10067,5057,50.23,11650,5786,49.67,7224,3561,49.29,3310,1591,48.07,1368,673,49.2,395,190,48.1
+110,Undying,str,"Support, Durable, Disabler, Nuker",3170,1620,51.1,19403,10116,52.14,40582,21110,52.02,40850,21182,51.85,23985,12454,51.92,10395,5389,51.84,4541,2336,51.44,2064,1012,49.03
+111,Ursa,agi,"Carry, Durable, Disabler",2801,1273,45.45,15132,7038,46.51,33269,15478,46.52,40822,19264,47.19,29348,14011,47.74,15262,7375,48.32,7507,3622,48.25,3004,1473,49.03
+112,Vengeful Spirit,all,"Support, Initiator, Disabler, Nuker, Escape",2186,1108,50.69,15817,8285,52.38,41843,21809,52.12,57524,30476,52.98,45512,24120,53.0,25581,13382,52.31,13758,7121,51.76,8276,4303,51.99
+113,Venomancer,all,"Support, Nuker, Initiator, Pusher, Disabler",2309,1187,51.41,14669,7463,50.88,34787,18020,51.8,41797,21690,51.89,28706,15085,52.55,13974,7338,52.51,6538,3495,53.46,2794,1459,52.22
+114,Viper,agi,"Carry, Durable, Initiator, Disabler",4100,2057,50.17,18991,9510,50.08,33517,16923,50.49,32728,16677,50.96,18537,9427,50.86,7851,3928,50.03,3260,1652,50.67,1176,610,51.87
+115,Visage,all,"Support, Nuker, Durable, Disabler, Pusher",331,171,51.66,1638,813,49.63,3240,1577,48.67,3840,1986,51.72,3108,1609,51.77,1995,1055,52.88,1309,702,53.63,858,457,53.26
+116,Void Spirit,all,"Carry, Escape, Nuker, Disabler",1565,727,46.45,8672,4096,47.23,20010,9694,48.45,25213,12376,49.09,18817,9231,49.06,10026,4920,49.07,4788,2319,48.43,2006,964,48.06
+117,Warlock,int,"Support, Initiator, Disabler",2547,1369,53.75,18931,10331,54.57,49795,26999,54.22,66697,36220,54.31,48401,25668,53.03,24999,12942,51.77,12575,6356,50.54,6183,2934,47.45
+118,Weaver,agi,"Carry, Escape",2818,1389,49.29,13873,6770,48.8,23493,11571,49.25,21545,10694,49.64,12911,6427,49.78,5809,2928,50.4,2960,1455,49.16,1303,719,55.18
+119,Windranger,all,"Carry, Support, Disabler, Escape, Nuker",3861,1814,46.98,19934,9223,46.27,40644,18807,46.27,44476,20652,46.43,28952,13508,46.66,13418,6297,46.93,5898,2782,47.17,2374,1142,48.1
+120,Winter Wyvern,all,"Support, Disabler, Nuker",821,371,45.19,5168,2424,46.9,10544,5014,47.55,11184,5308,47.46,7426,3512,47.29,3730,1854,49.71,1862,934,50.16,944,464,49.15
+121,Witch Doctor,int,"Support, Nuker, Disabler",7504,4173,55.61,45501,25616,56.3,99664,54963,55.15,111382,60421,54.25,71830,37860,52.71,33164,17334,52.27,14610,7442,50.94,4196,2076,49.48
+122,Wraith King,str,"Carry, Support, Durable, Disabler, Initiator",4175,2266,54.28,26362,14516,55.06,58733,32403,55.17,66283,36503,55.07,42360,23083,54.49,19084,10251,53.72,8334,4315,51.78,2707,1376,50.83
+123,Zeus,int,"Nuker, Carry",4132,2106,50.97,23721,12487,52.64,51568,27475,53.28,58333,31078,53.28,37821,20047,53.0,17901,9504,53.09,8539,4459,52.22,3400,1791,52.68

belyaeva_ekaterina_lab_6/README.md

@@ -0,0 +1,92 @@
+## Задание 
+
+Использовать  нейронную  сеть MLPClassifier для  данных  из  таблицы  1  по  
+варианту, самостоятельно  сформулировав  задачу. Интерпретировать  результаты  и оценить,  насколько  хорошо  она подходит  для  решения сформулированной вами задачи
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки pandas, scikit-learn, их компоненты
+## Описание лабораторной (программы)
+Данный код берет данные из датасета о персонажах Dota 2, где описаны атрибуты персонажей, их роли, название, и как часто их пикают и какой у них винрейт на каждом звании в Доте, от реркута до титана.
+
+В моем случае была поставлена задача понять, можно ли определить позицию персонажа (всего в игре есть 5 позиций - 
+carry, mid, offlane, support, full support), по его главному атрибуту и по тому, какие роли он выполняет в игре. Учитывая
+то, что Dota 2 имеет 124 персонажа, все они очень разные, поэтому была вероятность, что модель не установит зависимость и 
+не будет работать в принципе. Именно поэтому я посчитала данную задачу довольно интересной. В моем датасете присутствует информация о главном атрибуте персонажа и его ролях, но нет 
+информации о том, на каких позициях он играется. Поэтому для выяснения этого списка я обратилась к внешним ресурсам
+и занесла информацию об этом в программу вручную. Это можно увидеть в коде в месте, где определяются роли.
+
+![positions.png](positions.png)
+
+Программа берет столбцы Name, Roles, PrimaryAttribute из датасета. Так как в столбце Roles есть 9 значений, которые прописаны
+в разном количестве и разные у каждого персонажа, нужно было создать 9 дополнительных столбцов, где для каждого персонажа
+выставлялось 1, если такая роль присутствует в его описании и 0, если ее нет. 
+
+Пример:
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+
+Далее столбец Roles был удален.
+
+Так как PrimaryAttribute указан в строковом значении, он так же был переведен в числовое значение.
+
+После этого нужно было заполнить столбцы posCarry, posMid, posOfflane, posSupport, posFullSupport. Если персонаж есть в списке
+персонажей с этой позицией, там проставлялась 1, 0 - если нет.
+
+В итоге получился датасет, где есть имя персонажа, его главный атрибут в виде числа, его роли (1 - если есть, 0 - если нет)
+и то же самое с позициями.
+
+Далее датафрейм делится на признаки (все столбцы, кроме столбцов с позициями) и метки (столбцы с позициями). Метки переводятся в числовой формат с помощью LabelEncoder(), иначе программа не может с ними работать.
+Данные делятся на обучающую и тестовую выборку.  
+
+Модель создается таким образом потому, что если ставить меньшее число итераций или скрытых слоев, то она не успевала обучаться.
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+
+Затем происходит предсказание позиций для тестовой выборки и оценка работы модели с помощью accuracy_score и classification_report
+
+## Результат
+
+В результате получаем следующее:
+
+![accuracy.png](accuracy.png)
+
+Оценка модели имеет относительно низкое значение. Однако, как было сказано ранее, она могла не работать в принципе, поэтому 
+я считаю это достаточно неплохим результатом и поставленная цель была выполнена - было выяснено, что позиция персонажа
+все-таки зависит от его атрибута и ролей, которые он выполняет по игре, хоть эта зависимость и не 100% явная. Если бы она
+была явная, например, все персонажи с атрибутом "сила" - это позиция offlane, тогда работа модели была бы значительно лучше.
+
+Далее мы получаем classification report:
+
+![classificationReport.png](classificationReport.png)
+
+В данном отчете представлены 5 классов, то есть позиции (0, 1, 2, 3, 4). Для каждого класса представлены значения точности, 
+полноты и F1-оценки, вычисленные с использованием соответствующих метрик. Также показана поддержка класса, которая 
+представляет собой количество образцов, принадлежащих этому классу.
+
+Precision (точность) - это метрика, которая оценивает долю правильно классифицированных объектов из всех объектов, которые модель отнесла к данному классу. Она измеряет, насколько точно модель предсказывает положительные классы. 
+
+Recall (полнота) - это метрика, которая оценивает долю правильно классифицированных объектов, отнесенных моделью к данному классу, относительно всех объектов, принадлежащих к данному классу. Она измеряет, насколько полно модель находит положительные классы.
+
+F1-мера (F1-score) - это гармоническое среднее между precision и recall. Она используется для объединения оценок точности и полноты в единую метрику. F1-мера принимает значение между 0 и 1, где 1 - это идеальное значение, означающее, что модель идеально находит и точно классифицирует объекты положительного класса
+
+micro avg - средневзвешенное значение точности, полноты и F1-оценки во всех классах, подсчитанное по общему количеству образцов.
+
+macro avg - среднее значение точности, полноты и F1-оценки по всем классам, без учета количества образцов.
+
+weighted avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов.
+
+samples avg - средневзвешенное значение точности, полноты и F1-оценки по всем классам, учитывая количество образцов 
+класса (если образец может принадлежать нескольким классам).
+
+Из данного отчета можно сделать вывод о том, что по атрибутам и ролям в игре модель точно выявила персонажей для позиции 
+mid и offlane, но при этом, при работе с объектами, модель пропустила больше всего объектов, относящихся к этим классам,
+и занесла их в другие классы, из-за чего снизилась precision других классов. Мы сами должны выбирать, что важнее - точность или полнота,
+и в моем случае важнее точность, ведь изначально стоял вопрос о том, сможет ли модель определить, что к чему относится. Но низкие 
+значения полноты говорят о том, что низкое значение accuracy вполне оправдано, и хоть модель и может выявить, какие объекты к каким классам относятся, 
+делает она это не совсем "пОлно" и пропускает некоторые объекты.
+
+Что касается признаков micro avg, macro avg, weighted avg, samples avg - все они показывают неплохие результаты относительно 
+ожиданий по поводу работы модели. Я думаю, что для поставленной задачи значения этих показателей довольно высоки.
+
+Вывод: точность и показатели из отчета вышли достаточно хорошими относительно поставленной задачи, также был получен ответ на вопрос
+зависит ли позиция персонажа от его атрибута и роли. Следовательно, с задачей разработанная модель справилась.

belyaeva_ekaterina_lab_6/main.py

@@ -0,0 +1,76 @@
+import pandas as pd
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report
+
+# Чтение данных из файла Current_Pub_Meta.csv
+current_pub_meta = pd.read_csv('Current_Pub_Meta.csv')
+
+# Создаем пустой DataFrame для хранения данных
+data = pd.DataFrame(columns=['Name', 'Roles', 'Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler',
+                             'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher', 'posCarry', 'posMid',
+                           'posOfflane', 'posSupport', 'posHardSupport'])
+
+
+# Добавление новых столбцов из файла в датафрейм data
+data['Name'] = current_pub_meta['Name']
+data['Roles'] = current_pub_meta['Roles']
+data['Primary Attribute'] = current_pub_meta['Primary Attribute']
+data['Primary Attribute'] = data['Primary Attribute'].map({'str': 0, 'all': 1, 'int': 2, 'agi': 3})
+
+data['IsDurable'] = data['Roles'].apply(lambda x: 1 if 'Durable' in x else 0)
+data['IsCarry'] = data['Roles'].apply(lambda x: 1 if 'Carry' in x else 0)
+data['IsSupport'] = data['Roles'].apply(lambda x: 1 if 'Support' in x else 0)
+data['IsDisabler'] = data['Roles'].apply(lambda x: 1 if 'Disabler' in x else 0)
+data['IsInitiator'] = data['Roles'].apply(lambda x: 1 if 'Initiator' in x else 0)
+data['IsNuker'] = data['Roles'].apply(lambda x: 1 if 'Nuker' in x else 0)
+data['IsEscaper'] = data['Roles'].apply(lambda x: 1 if 'Escaper' in x else 0)
+data['IsPusher'] = data['Roles'].apply(lambda x: 1 if 'Pusher' in x else 0)
+
+#Удаление столбца Roles
+data.drop('Roles', axis=1, inplace=True)
+
+# Создаем список персонажей на каждую позицию
+roles = {
+    'posHardSupport': ['Undying', 'Pudge', 'Marci', 'Grimstroke', 'Elder Titan', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren'],
+    'posSupport': ['Venomancer', 'Tusk', 'Tiny', 'Spirit Breaker', 'Techies', 'Snapfire', 'Pudge', 'Muerta', 'Marci', 'Hoodwink', 'Grimstroke', 'Earth Spirit', 'Bounty Hunter', 'Crystal Maiden', 'Lion', 'Shadow Shaman', 'Lich', 'Ogre Magi', 'Warlock', 'Dazzle', 'Witch Doctor', 'Vengeful Spirit', 'Ancient Apparition', 'Disruptor', 'Keeper of the Light', 'Rubick', 'Jakiro', 'Oracle', 'Visage', 'Silencer', 'Shadow Demon', 'Chen', 'Winter Wyvern', 'Bane', 'Treant Protector', 'Io', 'Enchantress', 'Naga Siren', 'Earthshaker', 'Skywrath Mage', 'Leshrac', 'Shadow Fiend', 'Nyx Assassin', 'Pugna', 'Lina', 'Zeus', "Nature's Prophet", 'Dark Willow'],
+    'posOfflane': ['Wraith King', 'Spirit Breaker', 'Snapfire', 'Pudge', 'Primal Beast', 'Marci', 'Dragon Knight', 'Tidehunter', 'Centaur Warrunner', 'Dark Seer', 'Beastmaster', 'Mars', 'Brewmaster', 'Timbersaw', 'Bristleback', 'Abaddon', 'Axe', 'Enigma', 'Sand King', 'Clockwerk', 'Doom', 'Underlord', 'Omniknight', 'Legion Commander', "Nature's Prophet", 'Slardar', 'Faceless Void', 'Earthshaker', 'Pangolier', 'Pugna', 'Mars', 'Batrider', 'Windranger', 'Mirana', 'Beastmaster', 'Brewmaster', 'Phoenix', 'Beastmaster', 'Dark Seer', 'Lone Druid', 'Timbersaw', 'Broodmother', "Nature's Prophet", 'Magnus', 'Necrophos', 'Bloodseeker', 'Lycan'],
+    'posMid': ['Void Spirit', 'Pudge', 'Primal Beast', 'Earth Spirit', 'Dragon Knight', 'Arc Warden', 'Invoker', 'Storm Spirit', 'Shadow Fiend', 'Templar Assassin', 'Queen of Pain', 'Puck', 'Zeus', 'Tinker', 'Lina', 'Ember Spirit', 'Outworld Destroyer', 'Morphling', 'Leshrac', 'Sniper', 'Mirana', 'Viper', 'Death Prophet', 'Razor', 'Pugna', 'Skywrath Mage', "Nature's Prophet", 'Windranger', 'Batrider', 'Lina', 'Shadow Fiend', 'Templar Assassin', 'Ember Spirit', 'Huskar', 'Kunkka', 'Puck', 'Queen of Pain', 'Invoker', 'Storm Spirit', 'Outworld Devourer', 'Death Prophet', 'Razor', 'Lina', 'Sniper', 'Medusa', 'Leshrac', 'Viper'],
+    'posCarry': ['Pudge', 'Muerta', 'Monkey King', 'Drow Ranger', 'Alchemist', 'Anti-Mage', 'Spectre', 'Juggernaut', 'Phantom Assassin', 'Faceless Void', 'Phantom Lancer', 'Lifestealer', 'Slark', 'Terrorblade', 'Medusa', 'Luna', 'Shadow Fiend', 'Morphling', 'Templar Assassin', 'Ember Spirit', 'Naga Siren', 'Troll Warlord', 'Gyrocopter', 'Lone Druid', 'Ursa', 'Riki', 'Sven', 'Phantom Lancer', 'Chaos Knight', 'Night Stalker', 'Wraith King', 'Meepo', 'Troll Warlord', 'Juggernaut', 'Lifestealer', 'Templar Assassin', 'Ursa', 'Clinkz', 'Weaver', 'Riki', 'Spectre', 'Phantom Assassin', 'Naga Siren', 'Luna', 'Gyrocopter', 'Meepo', 'Lone Druid', 'Slark', 'Morphling', 'Terrorblade', 'Medusa', 'Faceless Void']
+}
+
+# Перебираем каждого героя и добавляем значения в соответствующие столбцы
+for index, row in data.iterrows():
+    for role, characters in roles.items():
+        data.loc[index, role] = int(row['Name'] in characters)
+
+pd.set_option('display.max_columns', None)
+pd.set_option('display.max_rows', None)
+print(data)
+
+# Разделение датафрейма на признаки и метки
+X = data[['Primary Attribute', 'IsDurable', 'IsSupport', 'IsCarry', 'IsDisabler', 'IsInitiator', 'IsNuker', 'IsEscaper', 'IsPusher']]
+y = data[['posCarry', 'posMid', 'posOfflane', 'posSupport', 'posHardSupport']]
+
+# Преобразование меток в числовой формат
+label_encoder = LabelEncoder()
+y = y.apply(label_encoder.fit_transform)
+
+# Разделение выборки на обучающую и тестовую
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+
+# Создание и обучение модели
+model = MLPClassifier(hidden_layer_sizes=(128, 128, 128), activation='relu', max_iter=1000, random_state=42)
+model.fit(X_train, y_train)
+
+# Предсказание позиций для тестовой выборки
+y_pred = model.predict(X_test)
+
+# Оценка точности модели
+accuracy = accuracy_score(y_test, y_pred)
+class_report = classification_report(y_test, y_pred)
+print("Accuracy:", accuracy)
+print('Classification Report:')
+print(class_report)

belyaeva_ekaterina_lab_7/README.md

@@ -0,0 +1,54 @@
+## Задание 
+
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и обучить на нем рекуррентную 
+нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату.Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом.
+
+## Как запустить лабораторную
+Запустить файл main.py
+## Используемые технологии
+Библиотеки tensorflow, numpy, их компоненты
+## Описание лабораторной (программы)
+
+Данная лабораторная работа обучает модели для обработки русского и английского текста и решает задачу генерации.
+Ниже будет описан алгоритм работы одной из моделей (вторая работает аналогично):
+1. Читается текст из файла
+2. Создается экземпляр Tokenizer для токенизации текста
+3. С помощью метода fit_on_texts токенизатор анализирует текст и строит словарь уникальных слов
+4. rus_vocab_size - длина словаря
+5. C помощью метода text_to_sequences текст преобразуется в последовательность чисел
+6. Создаются последовательности для обучения модели
+7. Рассчитывается максимальная длина последовательности 
+8. Входные последовательности выравниваются до максимальной длины
+9. С помощью функции to_categorical последовательности преобразуются в one-hot представление
+10. Переменные x_rus_train, y_rus_train инициализируются соответствующими значениями
+11. Такая же обработка текста происходит и для текста на английском языке
+12. Происходит создание модели на русском языке: 
+ - создается экземпляр модели Sequential 
+ - добавляется слой Embedding, отображающий слова в векторы фиксированной длины
+ - добавляется слой LSTM с 512 нейронами
+ - добавляется слой Dense с функцией softmax для получения вероятности каждого слова в словаре
+ - модель компилируется
+13. Происходит обучение модели через model.fit()
+14. Все то же самое происходит для модели с английским языком
+15. Определяется функция generate_text для генерации текста на основе всех заданных параметров
+16. Выводятся результаты работы моделей и сгенерированные тексты
+
+## Результат
+
+Результат сгенерированного текста на русском языке: Помню просторный грязный двор и низкие домики обнесённые забором двор стоял у самой реки и по вёснам когда спадала полая вода он был усеян щепой и ракушками а иногда и другими куда более интересными вещами так однажды мы нашли туго набитую письмами сумку а потом вода принесла и осторожно положила на берег и самого почтальона он лежал на спине закинув руки как будто заслонясь от солнца ещё совсем молодой белокурый в форменной тужурке с блестящими пуговицами должно быть отправляясь в свой последний рейс почтальон начистил их мелом мелом мелом спадала щепой мелом мелом мелом мелом мелом спадала полая вода он ракушками а
+
+Результат сгенерированного текста на английском языке: The old man was thin and gaunt with deep wrinkles in the back of his neck the brown blotches of the benevolent skin cancer the sun brings from its reflection on the tropic sea were on his cheeks the blotches ran well down the sides of his face and his hands had the deep creased scars from handling heavy fish on the cords but none of these scars were fresh they were as old as erosions in a fishless desert fishless desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert desert fishless
+
+Результат потерь на тренировочных данных:
+
+![res.png](res.png)
+
+Вывод: можно заметить, что в сгенерированных текстах в конце слова повторяются. Это происходит потому, что в параметрах модели 
+указано сгенерировать 100 слов, хотя в тексте, по которому модель обучается, меньше слов. Поэтому сгенерированный текст сначала
+соответствует тексту для обучения, а затем начинает выдавать рандомные слова. Но нужно отметить, что это слова, а не просто 
+набор букв и пробелы, которые получались при иных настройках моделей.
+
+Так как у английской модели меньше потерь на тренировочных данных, чем у русской, то получается, что выполненная модель
+обрабатывает английский текст чуть лучше, чем русский, но в результате обе модели выдали осмысленный текст, что связано с большим
+числом нейронов и эпох, при помощи которых обучалась модель. Ведь когда было 20 эпох, а не 200, модель выдавала очень слабо осмысленный результат.
+

belyaeva_ekaterina_lab_7/eng.txt

@@ -0,0 +1,5 @@
+The old man was thin and gaunt with deep wrinkles in the back of his neck. The
+brown blotches of the benevolent skin cancer the sun brings from its reflection on the
+tropic sea were on his cheeks. The blotches ran well down the sides of his face and his
+hands had the deep-creased scars from handling heavy fish on the cords. But none of
+these scars were fresh. They were as old as erosions in a fishless desert.

belyaeva_ekaterina_lab_7/main.py

@@ -0,0 +1,97 @@
+import tensorflow as tf
+import numpy as np
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Embedding
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка и предобработка данных на русском языке
+with open("rus.txt", "r", encoding="utf-8") as f:
+    rus_text = f.read()
+
+tokenizer_rus = Tokenizer()
+tokenizer_rus.fit_on_texts([rus_text])
+
+rus_vocab_size = len(tokenizer_rus.word_index) + 1
+rus_sequences = tokenizer_rus.texts_to_sequences([rus_text])[0]
+rus_input_sequences = []
+rus_output_sequences = []
+
+for i in range(1, len(rus_sequences)):
+    rus_input_sequences.append(rus_sequences[:i])
+    rus_output_sequences.append(rus_sequences[i])
+
+rus_max_sequence_len = max([len(seq) for seq in rus_input_sequences])
+rus_input_sequences = pad_sequences(rus_input_sequences, maxlen=rus_max_sequence_len)
+
+x_rus_train = rus_input_sequences
+y_rus_train = tf.keras.utils.to_categorical(rus_output_sequences, num_classes=rus_vocab_size)
+
+# Загрузка и предобработка данных на английском языке
+with open("eng.txt", "r", encoding="utf-8") as f:
+    eng_text = f.read()
+
+tokenizer_eng = Tokenizer()
+tokenizer_eng.fit_on_texts([eng_text])
+
+eng_vocab_size = len(tokenizer_eng.word_index) + 1
+eng_sequences = tokenizer_eng.texts_to_sequences([eng_text])[0]
+eng_input_sequences = []
+eng_output_sequences = []
+
+for i in range(1, len(eng_sequences)):
+    eng_input_sequences.append(eng_sequences[:i])
+    eng_output_sequences.append(eng_sequences[i])
+
+eng_max_sequence_len = max([len(seq) for seq in eng_input_sequences])
+eng_input_sequences = pad_sequences(eng_input_sequences, maxlen=eng_max_sequence_len)
+
+x_eng_train = eng_input_sequences
+y_eng_train = tf.keras.utils.to_categorical(eng_output_sequences, num_classes=eng_vocab_size)
+
+# Построение модели для русского языка
+rus_model = Sequential()
+rus_model.add(Embedding(rus_vocab_size, 256, input_length=rus_max_sequence_len))
+rus_model.add(LSTM(512))
+rus_model.add(Dense(rus_vocab_size, activation='softmax'))
+
+rus_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для русского языка
+rus_history = rus_model.fit(x_rus_train, y_rus_train, batch_size=128, epochs=200)
+
+# Построение модели для английского языка
+eng_model = Sequential()
+eng_model.add(Embedding(eng_vocab_size, 256, input_length=eng_max_sequence_len))
+eng_model.add(LSTM(512))
+eng_model.add(Dense(eng_vocab_size, activation='softmax'))
+
+eng_model.compile(loss='categorical_crossentropy', optimizer='adam')
+
+# Обучение модели для английского языка
+eng_history = eng_model.fit(x_eng_train, y_eng_train, batch_size=128, epochs=200)
+
+def generate_text(model, tokenizer, max_sequence_len, seed_text):
+    output_text = seed_text
+    for _ in range(100):  # Генерируем 100 слов
+        encoded_text = tokenizer.texts_to_sequences([output_text])[0]
+        pad_encoded = pad_sequences([encoded_text], maxlen=max_sequence_len, truncating='pre')
+        pred_word_index = np.argmax(model.predict(pad_encoded), axis=-1)
+        pred_word = tokenizer.index_word[pred_word_index[0]]
+        output_text += " " + pred_word
+    return output_text
+
+# Генерация текста для русской и английской моделей
+rus_output_text = generate_text(rus_model, tokenizer_rus, rus_max_sequence_len, "Помню просторный")
+eng_output_text = generate_text(eng_model, tokenizer_eng, eng_max_sequence_len, "The old man")
+
+# Вывод результатов
+print("Русская модель:")
+print("Потери на тренировочных данных:", rus_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(rus_output_text)
+
+print("Английская модель:")
+print("Потери на тренировочных данных:", eng_history.history['loss'][-1])
+print("Сгенерированный текст:")
+print(eng_output_text)

belyaeva_ekaterina_lab_7/rus.txt

@@ -0,0 +1 @@
+Помню просторный грязный двор и низкие домики, обнесённые забором. Двор стоял у самой реки, и по вёснам, когда спадала полая вода, он был усеян щепой и ракушками, а иногда и другими, куда более интересными вещами. Так, однажды мы нашли туго набитую письмами сумку, а потом вода принесла и осторожно положила на берег и самого почтальона. Он лежал на спине, закинув руки, как будто заслонясь от солнца, ещё совсем молодой, белокурый, в форменной тужурке с блестящими пуговицами: должно быть, отправляясь в свой последний рейс, почтальон начистил их мелом.

gusev_vladislav_lab_2/README.md

@@ -0,0 +1,36 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Выполнить ранжирование признаков с помощью указанных по варианту моделей:
+- Лассо (Lasso)
+- Сокращение признаков Случайными деревьями (Random Forest Regressor)
+- Линейная корреляция (f_regression)
+
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_2.py, в консоль будут выведены результаты.
+
+### Технологии
+NumPy - библиотека для работы с многомерными массивами. Sklearn - библиотека с большим количеством алгоритмов машинного обучения.
+
+### По коду
+В начале генерируем исходные данные: 750 строк-наблюдений и 14 столбцов-признаков, задаем функцию-выход: регрессионную проблему Фридмана, добавляем зависимость признаков
+
+Далее создаем пустой словарь для хранения рангов признаков, используем методы из библиотеки Sklearn: Lasso, RandomForestRegressor и f_regression для задания по варианту.
+
+Далее необходимо объявить функцию def rank_to_dict(ranks, names): для соотнесения нашего списка рангов и списка оценок по признакам. Возвращает он словарь типа (имя_признака: оценка_признака) и оценки приведены к единому диапазону от 0 до 1 и округлены до сотых.
+
+В конце формируем среднее по каждому признаку, сортируем по убыванию и выводим на экран. 
+
+Пример:
+
+![img.png](img.png)
+
+Признаки х4 и х14 имеют наивысшие ранги, что говорит об их наибольшей значимости для решения задачи
+
+Далее x2 и x12 занимают второе место по значимости (средняя значимость)
+
+х1, х11 ниже среднего
+
+х5, х8, х7 низкая значимость
+
+х9, х3, х13, х10, х6 очень низкая значимость
+

gusev_vladislav_lab_2/gusev_vladislav_lab_2.py

@@ -0,0 +1,53 @@
+from sklearn.linear_model import Lasso
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import f_regression
+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))
+
+names = ["x%s" % i for i in range(1,15)]
+#Создается пустой словарь для хранения рангов признаков
+ranks = {}
+
+#Lasso
+lasso = Lasso(alpha=0.5)
+lasso.fit(X, Y)
+ranks["Lasso"] = dict(zip(names, lasso.coef_))
+#Случайные деревья
+rf = RandomForestRegressor(n_estimators=100)
+rf.fit(X, Y)
+ranks["Random Forest"] = dict(zip(names, rf.feature_importances_))
+#Линейная корреляция
+f_scores, p_values = f_regression(X, Y)
+ranks["f_regression"] = dict(zip(names, f_scores))
+
+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))
+
+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]
+
+
+sorted_mean = sorted(mean.items(), key=lambda x: x[1], reverse=True)
+result = {}
+for item in sorted_mean:
+    result[item[0]] = item[1]
+    print(f'{item[0]}: {item[1]}')
+

gusev_vladislav_lab_4/README.md

@@ -0,0 +1,20 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Использовать метод кластеризации DBSCAN, самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной задачи.
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_1.py, на экране будет нарисовано 3 графика
+### Технологии
+Pandas - библиотека для анализа данных. Она предоставляет структуры данных и функции для работы с табличными данными. Mathplotlib - библиотека для визуализации данных двумерной и трехмерной графикой. Sklearn - библиотека с большим количеством алгоритмов машинного обучения.
+### По коду
+1) Загружаем данные из csv файла
+2) Выбираем 10000 данных (потому что при сильном увеличении данных метод DBSCAN сильно загружает систему и программа начинает виснуть)
+3) Создаем модель DBSCAN, предварительно выбрав нужные данные
+4) Применяем DBSCAN к данным и создаём график
+
+Что получаем:
+
+![img.png](img.png)
+
+### Вывод
+- По данному графику можно сказать, что в основном глубина алмазов розница от ~57-~66, а карат в районе 1 (0.6-1.4)
+- В целом на графике видно очень много шума (фиолетовые точки), но также немало более светлых - близких к красным. Визуально можно сказать, что эффективность этого метода 30%-40%.

gusev_vladislav_lab_4/gusev_vladislav_lab_4.py

@@ -0,0 +1,25 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+# Загрузка данных из csv-файла
+data = pd.read_csv('diamonds_prices.csv', index_col='diamond_id')
+
+
+# Выбираем 10000 данных ()
+data_subset = data.head(10000)
+
+# Выделяем признаки (карат и глубина)
+features = data_subset[['carat', 'depth']]
+
+# Создание модели DBSCAN
+dbscan = DBSCAN(eps=0.1, min_samples=5)
+
+# Применение DBSCAN к данным
+data_subset['cluster'] = dbscan.fit_predict(features)
+
+# Создание графика для визуализации кластеров
+plt.scatter(data_subset['carat'], data_subset['depth'], c=data_subset['cluster'], cmap='rainbow')
+plt.xlabel('Карат (carat)')
+plt.ylabel('Глубина (depth)')
+plt.title('Кластеризация данных о карате и глубине алмазов')
+plt.show()

gusev_vladislav_lab_5/README.md

@@ -0,0 +1,24 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Использовать регрессию по варианту для данных из курсовой работы. Самостоятельно сформулировав задачу. Интерпретировать результаты и оценить, насколько хорошо он подходит для решения сформулированной задачи.
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_5.py, будет выведен график на экран.
+
+### Технологии
+NumPy - библиотека для работы с многомерными массивами. Mathplotlib - библиотека для визуализации данных двумерной и трехмерной графикой. Sklearn - библиотека с большим количеством алгоритмов машинного обучения.
+
+### Задача
+Мною было принято решение посмотреть, как зависит 
+### По коду
+1) Для начала загружаем данные из csv файла
+2) Разделяем данные на обучающее и тестовые
+3) Рескейлим данные из столбца price, который был в диапозоне от 370 до 2700 к диапозону от 0 до 1
+4) Обучаем модель, находим R^2 (среднеквадратическая ошибка) и коэффициент детерминации
+5) Выводим графики
+
+
+![img.png](img.png)
+
+### Вывод
+ - Среднеквадарическая ошибка получилась довольно низкой, что говорит нам о точности тестовых и предсказанных значений, однако коэффициент детерминации получился крайне низким, даже отрицательным. Это значит, что модель не понимает зависимости данных.
+ - Итог: гребневая модель регресси не применима к нашей задаче

gusev_vladislav_lab_5/gusev_vladislav_lab_5.py

@@ -0,0 +1,34 @@
+import pandas as pd
+
+from sklearn.linear_model import Ridge
+from sklearn import metrics
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
+import numpy as np
+# загрузка данных из файла
+data = pd.read_csv('diamonds_prices.csv')
+scaler = MinMaxScaler()
+
+x_train = data[['price', 'carat', 'depth']].iloc[0:round(len(data) / 100 * 99)]
+y_train = data['table'].iloc[0:round(len(data) / 100 * 99)]
+y_train = scaler.fit_transform(y_train.values.reshape(-1, 1)) # приводим к виду от 0 до 1
+y_train = y_train.flatten()
+x_test = data[['price', 'carat', 'depth']].iloc[round(len(data) / 100 * 99):len(data)]
+y_test = data['table'].iloc[round(len(data) / 100 * 99):len(data)]
+y_test = scaler.fit_transform(y_test.values.reshape(-1, 1)) # приводим к виду от 0 до 1
+y_test = y_test.flatten()
+
+rid = Ridge(alpha=1.0)
+rid.fit(x_train.values, y_train)
+y_predict = rid.predict(x_test.values)
+
+mid_square = np.round(np.sqrt(metrics.mean_squared_error(y_test, y_predict)),3)  # рассчёт Ср^2
+coeff_determ = np.round(metrics.r2_score(y_test, y_predict), 2) # рассчёт коэффициента детерминации
+
+plt.plot(y_test, c="red", label="y тестовые ")
+plt.plot(y_predict, c="green", label="y предсказанные \n"
+                                                  "Ср^2 = " + str(mid_square) + "\n"
+                                                  "Coeff_determ = " + str(coeff_determ))
+plt.legend(loc='upper right')
+plt.title("Гребневая регрессия")
+plt.show()

gusev_vladislav_lab_7/README.md

@@ -0,0 +1,41 @@
+### Вариант 9
+### Задание на лабораторную работу:
+Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный) и 
+обучить на нем рекуррентную нейронную сеть для решения задачи генерации. 
+Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. 
+Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, 
+как архитектура товарища справляется с вашим текстом. 
+В завершении подобрать компромиссную архитектуру, справляющуюся достаточно хорошо с обоими видами 
+текстов.
+### Как запустить лабораторную работу:
+Выполняем файл gusev_vladislav_lab_7.py, решение будет в консоли.
+
+### Технологии
+Keras - это библиотека для Python, позволяющая легко и быстро создавать нейронные сети.
+NumPy - библиотека для работы с многомерными массивами.
+
+### По коду
+1) Читаем файл с текстом
+2) Создаем объект tokenizer для превращение текста в числа для нейронной сети.
+3) Создаем модель нейронной сети с следующими аргументами:
+
+- Embedding - это слой, который обычно используется для векторного представления категориальных данных, таких как слова или символы. Он позволяет нейронной сети изучать эмбеддинги, то есть отображение слов (или символов) в вектора низкой размерности. Это позволяет сети понимать семантические отношения между словами.
+- LSTM - это слой, представляющий собой рекуррентный нейрон, который способен учитывать зависимости в последовательных данных. Он хорошо подходит для обработки последовательных данных, таких как текст.
+- Dense - это полносвязный слой, который принимает входные данные и применяет весовые коэффициенты к ним. Этот слой часто используется в конце нейронных сетей для решения задачи классификации или регрессии.
+
+4) Обучаем модель на 100 эпохах (итерациях по данным) и генерируем текст.
+
+
+
+![img.png](img.png)
+Английский 100 эпох
+![img_1.png](img_1.png)
+
+![img_3.png](img_3.png)
+Русский 100 эпох
+![img_2.png](img_2.png)
+Русский 17 эпох
+![img_4.png](img_4.png)
+### По консоли
+- Английский текст генерировался на 100 эпохах, начало получилось осмысленным, но чем ближе к концу тем хуже.
+- Русский текст также генерировался на 100 эпохах, с многочисленными ошибками в словах. Русский текст,сгенерированный на 17 эпохах по ошибкам в словах оказался лучше, но всё равно не идеально.

gusev_vladislav_lab_7/gusev_vladislav_lab_7.py

@@ -0,0 +1,61 @@
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Embedding, LSTM, Dense
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+# Загрузка текста из файла
+with open('text_ru.txt', 'r', encoding='utf-8') as file:
+    text = file.read()
+
+# Создание экземпляра Tokenizer
+tokenizer = Tokenizer(char_level=True)
+tokenizer.fit_on_texts(text)
+
+# Преобразование текста в последовательность чисел
+sequences = tokenizer.texts_to_sequences(text)
+
+# Подготовка обучающих данных
+seq_length = 100
+dataX, dataY = [], []
+for i in range(0, len(sequences) - seq_length):
+    seq_in = sequences[i:i + seq_length]
+    seq_out = sequences[i + seq_length]
+    dataX.append(seq_in)
+    dataY.append(seq_out)
+
+dataX = np.array(dataX)
+dataY = np.array(dataY)
+
+# Создание модели
+vocab_size = len(tokenizer.word_index) + 1
+embedding_dim = 256
+rnn_units = 1024
+
+model = Sequential()
+model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=seq_length))
+model.add(LSTM(units=rnn_units))
+model.add(Dense(units=vocab_size, activation='softmax'))
+
+model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
+
+# Обучение модели
+batch_size = 64
+model.fit(dataX, dataY, epochs=17, batch_size=batch_size)
+def generate_text(seed_text, gen_length):
+    generated_text = seed_text
+
+    for _ in range(gen_length):
+        sequence = tokenizer.texts_to_sequences([seed_text])[0]
+        sequence = pad_sequences([sequence], maxlen=seq_length)
+        prediction = model.predict(sequence)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_char = tokenizer.index_word[predicted_index]
+        generated_text += predicted_char
+        seed_text += predicted_char
+        seed_text = seed_text[1:]
+
+    return generated_text
+# Пример использования
+generated_text = generate_text("Мультфильмы", 250)
+print(generated_text)

gusev_vladislav_lab_7/text_eng.txt

@@ -0,0 +1,21 @@
+Do you like watching cartoons? Probably you do! But how did they come to be? Who invented them?
+
+This is actually a very tough question. The first cartoons were created long before the TV.
+For example, shadow play was a very popular form of entertainment in ancient China. Such shows looked almost like modern cartoons!
+
+A toy called a flip book was made in the late 19th century. It was a small soft book with pictures.
+Each picture was drawn in a slightly different5 way. When you bend this book and release the pages one by one, the images start to move.
+Strictly speaking, they don’t, but our eyes see it like that anyway. The first real cartoons were made using this trick, too!
+
+In 1895 brothers Louis and Auguste Lumière created a cinematograph.
+It was a camera and a film projector in one device. Using this device, many aspiring film directors started to create their own cartoons.
+
+This developed into a full industry by 1910. Many cartoons of that era are forgotten now, but some are still with us.
+For example, Felix the Cat was created by Otto Messmer in 1919, and he’s still with us, more than a hundred years later.
+Currently the rights to the character are held by DreamWorks Animation.
+
+One of the pioneers in the industry was famous Walt Disney.
+He was not afraid to experiment to make a cartoon, and his Snow White film was among the firsts to use a multiplane camera.
+With its help the characters were able to move around the objects, creating an illusion of a 3D world.
+
+Today most of the cartoons are made with computer animation. The last traditional Disney cartoon to date was Winnie the Pooh (2011).

gusev_vladislav_lab_7/text_ru.txt

@@ -0,0 +1,21 @@
+Вам нравится смотреть мультфильмы? Вероятно, так оно и есть! Но как они появились на свет? Кто их изобрел?
+
+На самом деле это очень сложный вопрос. Первые мультфильмы были созданы задолго до появления телевидения.
+Например, игра с тенью была очень популярной формой развлечения в Древнем Китае. Такие шоу выглядели почти как современные мультфильмы!
+
+Игрушка под названием книжка-перевертыш была изготовлена в конце 19 века. Это была маленькая мягкая книжка с картинками.
+Каждая картинка была нарисована немного по-разному. Когда вы сгибаете эту книгу и отпускаете страницы одну за другой, изображения начинают двигаться.
+Строго говоря, это не так, но наши глаза все равно видят это именно так. Первые настоящие мультфильмы тоже были сделаны с использованием этого трюка!
+
+В 1895 году братья Луи и Огюст Люмьер создали кинематограф.
+Это была камера и кинопроектор в одном устройстве. Используя это устройство, многие начинающие режиссеры начали создавать свои собственные мультфильмы.
+
+К 1910 году это развилось в полноценную индустрию. Многие мультфильмы той эпохи сейчас забыты, но некоторые все еще с нами.
+Например, кот Феликс был создан Отто Мессмером в 1919 году, и он все еще с нами, более ста лет спустя.
+В настоящее время правами на персонажа владеет DreamWorks Animation.
+
+Одним из пионеров в этой отрасли был знаменитый Уолт Дисней.
+Он не боялся экспериментировать при создании мультфильма, и его фильм "Белоснежка" был одним из первых, в котором использовалась многоплановая камера.
+С его помощью персонажи смогли передвигаться по объектам, создавая иллюзию трехмерного мира.
+
+Сегодня большинство мультфильмов создано с использованием компьютерной анимации. Последним традиционным диснеевским мультфильмом на сегодняшний день был "Винни-Пух" (2011).

ilbekov_dmitriy_lab_4/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

ilbekov_dmitriy_lab_4/README.md

@@ -0,0 +1,26 @@
+# Лабораторная работа 4
+### Вариант 10
+
+### Задание: 
+- Используя данные из "F1DriversDataset.csv" сформулировать задачу, решаемую кластеризацией: Выделить 3 группы гонщиков ("условно" легендарные, выдающиеся, обыкновенные) с похожими достижениями в гонках и определить характеристики каждой группы
+### Алгоритм кластеризации: 
+- K-means (по варианту)
+### Запуск
+- Запустить файл lab4.py
+
+### Технологии
+- Язык - 'Python'
+- Библиотеки sklearn, numpy, pandas, matplotlib
+
+### Что делает
+- Программа реализовывает кластеризацию алгоритмом k-means, в результате чего мы получаем 3 кластера гонщиков (с определенными характеристиками для каждого кластера)
+- Программа также оценивает качество кластеризации, используя Индекс силуэта (Метрика, которая измеряет, насколько каждый объект в кластере похож на свой собственный кластер по сравнению с другими кластерами. Вычисление индекса силуэта включает в себя вычисление среднего значения коэффициента силуэта для всех объектов. Чем ближе значение индекса силуэта к 1, тем лучше кластеризация.)
+- Программа выводит график, позволяющий визуально понять, как прошла кластеризация
+
+### Пример работы
+Пример работы представлен в виде скриншотов:
+
+![Console](console.jpg)
+![Graphics](graphics.png)
+
+Как мы видим кластеризация помолга нам распределить гонщиков на 3 группы и определить характеристики групп, оценка качества кластеризации - 0.77, что довольно хороший показатель, значит алгоритм K-means справился со своей задачей