将分类列转换为单个虚拟变量列
Converting categorical column into a single dummy variable column
假设我有以下数据框：
Survived Pclass Sex Age Fare
0 0 3 male 22.0 7.2500
1 1 1 female 38.0 71.2833
2 1 3 female 26.0 7.9250
3 1 1 female 35.0 53.1000
4 0 3 male 35.0 8.0500
我使用 get_dummies() 函数创建了虚拟变量。代码及输出如下：
one_hot = pd.get_dummies(dataset, columns = ['Category'])
这将 return:
Survived Pclass Age Fare Sex_female Sex_male
0 0 3 22 7.2500 0 1
1 1 1 38 71.2833 1 0
2 1 3 26 7.9250 1 0
3 1 1 35 53.1000 1 0
4 0 3 35 8.0500 0 1
我想要的是具有值 0 或 1 而不是 2 列的单列 Sex。
有趣的是，当我在不同的数据帧上使用 get_dummies() 时，它就像我想要的那样工作。
对于以下数据框：
Category Message
0 ham Go until jurong point, crazy.. Available only ...
1 ham Ok lar... Joking wif u oni...
2 spam Free entry in 2 a wkly comp to win FA Cup final...
3 ham U dun say so early hor... U c already then say...
4 ham Nah I don't think he goes to usf, he lives aro...
使用代码：
one_hot = pd.get_dummies(dataset, columns = ['Category'])
它returns:
Message ... Category_spam
0 Go until jurong point, crazy.. Available only ... ... 0
1 Ok lar... Joking wif u oni... ... 0
2 Free entry in 2 a wkly comp to win FA Cup fina... ... 1
3 U dun say so early hor... U c already then say... ... 0
4 Nah I don't think he goes to usf, he lives aro... ... 0
- 为什么 get_dummies() 在这两个数据帧上的工作方式不同？
- 我怎样才能确保每次都能得到第二个输出？
您可以通过以下多种方式进行操作：
from sklearn.preprocessing import LabelEncoder
lbl=LabelEncoder()
df['Sex_encoded'] = lbl.fit_transform(df['Sex'])
# using only pandas
df['Sex_encoded'] = df['Sex'].map({'male': 0, 'female': 1})
Survived Pclass Sex Age Fare Sex_encoded
0 0 3 male 22.0 7.2500 0
1 1 1 female 38.0 71.2833 1
2 1 3 female 26.0 7.9250 1
3 1 1 female 35.0 53.1000 1
4 0 3 male 35.0 8.0500 0
假设我有以下数据框：
Survived Pclass Sex Age Fare
0 0 3 male 22.0 7.2500
1 1 1 female 38.0 71.2833
2 1 3 female 26.0 7.9250
3 1 1 female 35.0 53.1000
4 0 3 male 35.0 8.0500
我使用 get_dummies() 函数创建了虚拟变量。代码及输出如下：
one_hot = pd.get_dummies(dataset, columns = ['Category'])
这将 return:
Survived Pclass Age Fare Sex_female Sex_male
0 0 3 22 7.2500 0 1
1 1 1 38 71.2833 1 0
2 1 3 26 7.9250 1 0
3 1 1 35 53.1000 1 0
4 0 3 35 8.0500 0 1
我想要的是具有值 0 或 1 而不是 2 列的单列 Sex。
有趣的是，当我在不同的数据帧上使用 get_dummies() 时，它就像我想要的那样工作。
对于以下数据框：
Category Message
0 ham Go until jurong point, crazy.. Available only ...
1 ham Ok lar... Joking wif u oni...
2 spam Free entry in 2 a wkly comp to win FA Cup final...
3 ham U dun say so early hor... U c already then say...
4 ham Nah I don't think he goes to usf, he lives aro...
使用代码：
one_hot = pd.get_dummies(dataset, columns = ['Category'])
它returns:
Message ... Category_spam
0 Go until jurong point, crazy.. Available only ... ... 0
1 Ok lar... Joking wif u oni... ... 0
2 Free entry in 2 a wkly comp to win FA Cup fina... ... 1
3 U dun say so early hor... U c already then say... ... 0
4 Nah I don't think he goes to usf, he lives aro... ... 0
- 为什么 get_dummies() 在这两个数据帧上的工作方式不同？
- 我怎样才能确保每次都能得到第二个输出？
您可以通过以下多种方式进行操作：
from sklearn.preprocessing import LabelEncoder
lbl=LabelEncoder()
df['Sex_encoded'] = lbl.fit_transform(df['Sex'])
# using only pandas
df['Sex_encoded'] = df['Sex'].map({'male': 0, 'female': 1})
Survived Pclass Sex Age Fare Sex_encoded
0 0 3 male 22.0 7.2500 0
1 1 1 female 38.0 71.2833 1
2 1 3 female 26.0 7.9250 1
3 1 1 female 35.0 53.1000 1
4 0 3 male 35.0 8.0500 0