35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
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import zipfile
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import os
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import pandas as pd
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from sklearn.model_selection import train_test_split
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if os.getenv("KAGGLE_KEY") is None or os.getenv("KAGGLE_USERNAME") is None:
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print("Brak zmiennych środowiskowych KAGGLE_KEY lub KAAGLE_USERNAME")
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exit()
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if not os.path.isfile('fifa19.zip'):
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os.system('kaggle datasets download -d karangadiya/fifa19')
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with zipfile.ZipFile('fifa19.zip', 'r') as zip_ref:
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zip_ref.extractall('.')
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df=pd.read_csv('data.csv')
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df = df[df["Release Clause"].notna()]
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df = df[df["Release Clause"].notnull()]
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if df["Overall"].mean() > 1:
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df["Overall"]= df["Overall"]/100
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df["Release Clause"] = df["Release Clause"].str.replace("€", "")
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df["Release Clause"] = (df["Release Clause"].replace(r'[KM]+$', '', regex=True).astype(float) *
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df["Release Clause"].str.extract(r'[\d\.]+([KM]+)', expand=False)
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.replace(['K','M'], [1000, 1000000]).astype(int))
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df.to_csv('data.csv')
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train, dev = train_test_split(df, train_size=0.6, test_size=0.4, shuffle=True)
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dev, test = train_test_split(dev, train_size=0.5, test_size=0.5, shuffle=False)
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test.to_csv('test.csv')
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dev.to_csv('dev.csv')
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train.to_csv('train.csv')
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