import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

# usuwamy przy okazji puste pola
lego = pd.read_csv('lego_sets.csv').dropna()

# list_price moze byc do dwoch miejsc po przecinku
lego['list_price'] = lego['list_price'].round(2)

# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
lego['piece_count'] = lego['piece_count'].apply(np.int64)
lego['prod_id'] = lego['prod_id'].apply(np.int64)

# wglad, statystyki
print(lego)
print(lego.describe(include='all')).encode('utf-8')

# pierwszy podzial, wydzielamy zbior treningowy
lego_train, lego_rem = train_test_split(lego, train_size=0.8, random_state=1)

# drugi podział, wydzielamy walidacyjny i testowy
lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5, random_state=1)

# zapis
lego.to_csv('lego_sets_clean.csv', index=None, header=True)
lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)