在 Python 中處理類別資料
Kasey Jones
Research Data Scientist
重點:
n - 1,其中 n 為類別數-1 保留給遺漏值缺點:
轉為分類並依製造商名稱排序
used_cars['manufacturer_name'] = used_cars['manufacturer_name'].astype("category")
使用 .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',
...
}
建立代碼:
used_cars['manufacturer_code'] = used_cars['manufacturer_name'].cat.codes
還原為原值:
used_cars['manufacturer_code'].map(name_map)
0 Acura
1 Acura
2 Acura
...
找出所有包含「van」的車身型式:
# 前一課的程式碼:
used_cars["body_type"].str.contains("van", regex=False)
建立布林編碼:
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
在 Python 中處理類別資料