sklearn 和命名列中的单热编码多列

Question

我有以下代码来对我拥有的 2 列进行单热编码。

# encode city labels using one-hot encoding scheme
city_ohe = OneHotEncoder(categories='auto')
city_feature_arr = city_ohe.fit_transform(df[['city']]).toarray()
city_feature_labels = city_ohe.categories_
city_features = pd.DataFrame(city_feature_arr, columns=city_feature_labels)

phone_ohe = OneHotEncoder(categories='auto')
phone_feature_arr = phone_ohe.fit_transform(df[['phone']]).toarray()
phone_feature_labels = phone_ohe.categories_
phone_features = pd.DataFrame(phone_feature_arr, columns=phone_feature_labels)

我想知道我如何在 4 行中执行此操作，同时在输出中获得正确命名的列。也就是说，我可以通过在 fit_transform 中包含两个列名称来创建一个正确的单热编码数组，但是当我尝试命名结果数据框的列时，它告诉我索引的形状不匹配:

ValueError: Shape of passed values is (6, 50000), indices imply (3, 50000)

对于背景，phone 和城市都有 3 个值。

    city    phone
0   CityA   iPhone
1   CityB Android
2   CityB iPhone
3   CityA   iPhone
4   CityC   Android

Answer 1

你为什么不看一看pd.get_dummies？编码方式如下：

df['city'] = df['city'].astype('category')
df['phone'] = df['phone'].astype('category')
df = pd.get_dummies(df)

Answer 2

你快完成了...就像你说的，你可以直接在 fit_transform 中添加你想要编码的所有列。

ohe = OneHotEncoder(categories='auto')
feature_arr = ohe.fit_transform(df[['phone','city']]).toarray()
feature_labels = ohe.categories_

然后您只需要执行以下操作：

feature_labels = np.array(feature_labels).ravel()

这使您可以按照自己的意愿命名列：

features = pd.DataFrame(feature_arr, columns=feature_labels)

Answer 3

cat_features = [
    "gender", "cholesterol", "gluc", "smoke", "alco"
]

data = pd.get_dummies(data, columns = cat_features)

sklearn 和命名列中的单热编码多列

One-hot-encoding multiple columns in sklearn and naming columns

python

python-3.x

pandas

scikit-learn

one-hot-encoding