28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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cols = list(pd.read_csv("avocado.csv", nrows=1))
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# print("###\n", cols, "\n###")
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avocados = pd.read_csv("avocado.csv", usecols=cols[1:])
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avocados.describe(include="all")
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float_cols = ['AveragePrice','Total Volume','4046','4225','4770','Total Bags','Small Bags','Large Bags','XLarge Bags']
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avocados.loc[:, float_cols] = StandardScaler().fit_transform(avocados.loc[:, float_cols])
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print(avocados.head())
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# avocados.loc[:, float_cols] = MinMaxScaler().fit_transform(avocados.loc[:, float_cols])
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# print(avocados.head())
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avocado_train, avocado_test = train_test_split(avocados, test_size=2000, random_state=3337)
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avocado_train, avocado_valid = train_test_split(avocado_train, test_size=2249, random_state=3337)
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print("Train\n", avocado_train.describe(include="all"), "\n")
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print("Valid\n", avocado_valid.describe(include="all"), "\n")
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print("Test\n", avocado_test.describe(include="all"))
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avocado_train.to_csv("avocado.data.train", index=False)
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avocado_valid.to_csv("avocado.data.valid", index=False)
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avocado_test.to_csv("avocado.data.test", index=False)
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