Werken met categorische data in Python
Kasey Jones
Research Data Scientist
De basis:
n - 1, waarbij n het aantal categorieën is-1 is gereserveerd voor missende waardenHet nadeel:
Zet om naar categorisch en sorteer op fabrikantnaam
used_cars['manufacturer_name'] = used_cars['manufacturer_name'].astype("category")
Gebruik .cat.codes
used_cars['manufacturer_code'] = used_cars['manufacturer_name'].cat.codes
print(used_cars[['manufacturer_name', 'manufacturer_code']])
manufacturer_name manufacturer_code
0 Subaru 45
1 Subaru 45
2 Subaru 45
... ... ...
38526 Chrysler 8
38527 Chrysler 8

codes = used_cars['manufacturer_name'].cat.codes
categories = used_cars['manufacturer_name']
name_map = dict(zip(codes, categories))print(name_map)
{45: 'Subaru',
24: 'LADA',
12: 'Dodge',
...
}
Codes aanmaken:
used_cars['manufacturer_code'] = used_cars['manufacturer_name'].cat.codes
Terugzetten naar oude waarden:
used_cars['manufacturer_code'].map(name_map)
0 Acura
1 Acura
2 Acura
...
Zoek alle carrosserietypes met "van":
# Code uit de vorige les:
used_cars["body_type"].str.contains("van", regex=False)
Maak een booleaanse codering:
used_cars["van_code"] = np.where( used_cars["body_type"].str.contains("van", regex=False), 1, 0)used_cars["van_code"].value_counts()
0 34115
1 4416
Name: van_code, dtype: int64
Werken met categorische data in Python