2022-04-02 15:36:46 +02:00
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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# usuwamy przy okazji puste pola
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2022-04-02 16:54:36 +02:00
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lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
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2022-04-02 15:36:46 +02:00
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# list_price moze byc do dwoch miejsc po przecinku
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lego['list_price'] = lego['list_price'].round(2)
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# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
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lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
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lego['piece_count'] = lego['piece_count'].apply(np.int64)
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lego['prod_id'] = lego['prod_id'].apply(np.int64)
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# wglad, statystyki
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2022-04-02 17:10:11 +02:00
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print(lego)
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print(lego.describe(include='all'))
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2022-04-02 15:36:46 +02:00
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# pierwszy podzial, wydzielamy zbior treningowy
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lego_train, lego_rem = train_test_split(lego, train_size=0.8, random_state=1)
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# drugi podział, wydzielamy walidacyjny i testowy
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lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5, random_state=1)
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# zapis
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lego.to_csv('lego_sets_clean.csv', index=None, header=True)
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lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
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lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
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lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)
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