import zipfile
import os
import pandas as pd
from sklearn.model_selection import train_test_split

if os.getenv("KAGGLE_KEY") is None or os.getenv("KAGGLE_USERNAME") is None:
    print("Brak zmiennych środowiskowych KAGGLE_KEY lub KAAGLE_USERNAME")
    exit()

if not os.path.isfile('fifa19.zip'):
    os.system('kaggle datasets download -d karangadiya/fifa19')

with zipfile.ZipFile('fifa19.zip', 'r') as zip_ref:
    zip_ref.extractall('.')

df=pd.read_csv('data.csv')
df = df[df["Release Clause"].notna()]
df = df[df["Release Clause"].notnull()]

if df["Overall"].mean() > 1:
    df["Overall"]= df["Overall"]/100

df["Release Clause"] = df["Release Clause"].str.replace("€", "")

df["Release Clause"] = (df["Release Clause"].replace(r'[KM]+$', '', regex=True).astype(float) * 
          df["Release Clause"].str.extract(r'[\d\.]+([KM]+)', expand=False)
          .replace(['K','M'], [1000, 1000000]).astype(int))

df.to_csv('data.csv')
train, dev = train_test_split(df, train_size=0.6, test_size=0.4, shuffle=True)
dev, test = train_test_split(dev,  train_size=0.5, test_size=0.5, shuffle=False)

test.to_csv('test.csv') 
dev.to_csv('dev.csv') 
train.to_csv('train.csv')