{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"Player_Trackdf = pd.read_csv(\"/kaggle/input/nfl-playing-surface-analytics/PlayerTrackData.csv\")\nPlayer_Trackdf.info()\n\n# Oyuncularin maclardaki fiziksel aktivite dosyasini Player_Trackdf adina cevirerek indirdik ve inceledik\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Gamedf = pd.read_csv(\"/kaggle/input/nfl-playing-surface-analytics/PlayList.csv\")\nGamedf.info()\n\n\n# Oynanan maclarin detaylarini (hava durumu, cim tipi vb) barindiran dosyayi Gamedf adina cevirerek indirdik ve inceledik.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nInjurydf = pd.read_csv(\"/kaggle/input/nfl-playing-surface-analytics/InjuryRecord.csv\")\n\nInjurydf.info()\n\n# Sakatlik verilerini barindiran dosyayı Injurydf adina cevirerek indirdik ve inceledik\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Knee_Injury = Injurydf[Injurydf.BodyPart == \"Knee\"]\nAnkle_Injury = Injurydf[Injurydf.BodyPart == \"Ankle\"]\nFoot_Injury = Injurydf[Injurydf.BodyPart == \"Foot\"]\nToes_Injury = Injurydf[Injurydf.BodyPart == \"Toes\"]\nHeel_Injury = Injurydf[Injurydf.BodyPart == \"Heel\"]\n\n\nprint(Knee_Injury.describe())\nprint(Ankle_Injury.describe())\nprint(Foot_Injury.describe())\nprint(Toes_Injury.describe())\nprint(Heel_Injury.describe())\n\nInjurydf.columns\n\n# sakatliklarin olustugu bölgeleri ayri tanımladik ve sakatlik dosyasi altındaki parametrelerle basit ilişkisine baktik\n# Diz sakatliklarinin genel olarak 7 gun ve uzeri gun kaybına;\n# Ayak bilegi sakatliklarinin da diz sakatliklarina gore daha az olmakla birlikte genel olarak 7 gun ve uzeri gun kaybına;\n# Ayak bas parmak sakatliklarinin da diz sakatliklari gibi genel olarak 7 gun ve uzeri gun kaybına;\n# Ayak sakatiklarinin ise en fazla gun kaybına (28 gun ve uzeri + 42 gun ve uzeri) neden oldugunu goruyoruz.\n# Bu anlamda en ciddi sakatliklarin Ayak sakatliklari oldugunu soyleyebiliriz.\n# Topuk sakatliklarinin 28 gunden fazla gun kaybına neden olmadıgı (eksik data yuzunden de olabilir) gorulmekte.\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Natural = Injurydf[Injurydf.Surface == \"Natural\"]\nSynthetic = Injurydf[Injurydf.Surface == \"Synthetic\"]\n\nprint(Natural.describe())\nprint(Synthetic.describe())\n\n# Injurydf dataframe'i icindeki cim tipininin df deki diger parametrelerle basit ilişkisine baktik\n# Burada iliskisi incelenebilen datalar float olan datalar (ozellikle sakatliklarin neden oldugu gun kaybı)\n# Cim tipinin BodyPart ile iliskisine bakamadik cunku BodyPart bir string (buna nasil bakilmasi gerektigini inceliyorum)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(Natural.info())\nprint(Synthetic.info())\n\n# Dogal cim tipi ile Sentetik cim tipi ile degiskenlik gösteren datalari inceledik\n# Dogal cim tipi ile ilgili 48 adet data girisi var, dogal cimde oynanan mac sayisi = 48\n# Sentetik cim tipi ile ilgili 57 data girisi var, sentetik cimde oynanan mac sayisi = 57","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Injurydf.groupby('BodyPart').count()['PlayerKey'].plot(kind='bar', figsize=(15, 5), title='Injury Num by Body Part')\nplt.show()\n\nInjurydf.groupby('Surface').count()['PlayerKey'].plot(kind='barh', figsize=(15, 5), title='Injury Num by Body Part',color = \"g\")\nplt.show()\n\nInjurydf.groupby(['BodyPart','Surface']).count().unstack('BodyPart')['PlayerKey'].T.plot(kind='bar', figsize=(15, 5), title='Injury Body Part by Turf Type')\nplt.show()\n\n\n# Sakatliklari olustuklari anatomik bolgelere gore gruplandirdik ve indirdigimiz Matplotlib ile Bar grafiginde gorsellestirdik\n# Sakatliklari olustuklari zemin tipine göre gruplandirdik ve yatay (horizontal) Bar grafiginde gorsellestirdik\n# Sakatliklari hem olustuklari anatomik bolgelere gore hem de olustuklari zemin tipine gore gruplandirdik ve stack Bar grafiginde gorsellestirdik\n# itiraf ediyorum barh ve unstack kodlarini kaggle discussionlarda buldum :)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Grafiklerden ozellikle sentetik cimin ayak bas parmagi ve ayak bilegi sakatliklarinda on plana ciktigini goruyoruz\n# diz sakatliklarinda dogal ve sentetik cim farki goze carpmiyor\n# ayak sakatliklari dogal cimde daha cok, topuk sakatliklari ise sadece dogal cimde olusmus gorunuyor","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Injurydf_1 = Injurydf.copy()\nInjurydf_1.DM_M1 = Injurydf_1.DM_M1 - Injurydf_1.DM_M7\nInjurydf_1.DM_M7 = Injurydf_1.DM_M7 - Injurydf_1.DM_M28\nInjurydf_1.DM_M28 = Injurydf_1.DM_M28 - Injurydf_1.DM_M42\n\nM1 = Injurydf_1.DM_M1.sum()\nM7 = Injurydf_1.DM_M7.sum()\nM28 = Injurydf_1.DM_M28.sum()\nM42 = Injurydf_1.DM_M42.sum()\n\nprint(M1,M7,M28,M42)\n\n\ndata = [29, 39, 8, 29]\nplt.bar([\"0-7\",\"7-28\",\"28-42\",\"over 42\"], data)\nplt.title('Days Missing')\nplt.show()\n\n# Sakatlik yuzunden kaybedilen gunleri gurupladık ve bar chartta goruntuledik.\n# En fazla 7-28 gun kaybı yaratan sakatliklarin oldugunu goruyoruz.","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}