41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
#!/usr/bin/python
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import pandas as pd
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import numpy as np
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def prepareData():
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data = pd.read_csv("Customers.csv")
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#print(data[:10])
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dataF = data
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mapping = {'NaN' : 0, 'Healthcare' : 1, 'Engineer' : 2, 'Lawyer' : 3, 'Entertainment' : 4, 'Artist' : 5, 'Executive' : 6,
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'Doctor' : 7, 'Homemaker' : 8, 'Marketing' : 9}
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mapping2 = {'Male' : 0, 'Female' : 1}
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dataF = dataF.replace({'Profession': mapping})
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dataF = dataF.replace({'Gender': mapping2})
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dataF = dataF.drop(columns=['CustomerID'])
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dataF['Profession'] = dataF['Profession'].fillna(0)
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normalized_dataF = (dataF - dataF.min())/(dataF.max() - dataF.min())
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#print(normalized_dataF[:10])
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train_data = normalized_dataF[0:1600]
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dev_data = normalized_dataF[1600:1800]
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test_data = normalized_dataF[1800:]
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#print(f"Wielkość zbioru Customers: {len(data)} elementów")
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#print(f"Wielkość zbioru trenującego: {len(train_data)} elementów")
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#print(f"Wielkość zbioru walidującego: {len(dev_data)} elementów")
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#print(f"Wielkość zbioru testującego: {len(test_data)} elementów")
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#print(f" \nDane i wartości na temat zbioru: \n \n {normalized_dataF.describe()}")
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return train_data, dev_data, test_data
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