熊貓-多個“是/否”虛擬變量

Question

我有一個包含多個類別變量的數據框，需要將其轉換為虛擬變量。 性別和區域（4種類型）通過pd.get_dummies可以輕松pd.get_dummies 。 但是，此后我有幾個變量yes/no 。 我該怎么做，以便啞元yes和no列包含變量名稱？ 例如，“結婚”變量會變成married_yes和married_no ？

這是我當前的代碼和前五行的屏幕截圖：

genderdummy=pd.get_dummies(bank_df['gender'])
regiondummy=pd.get_dummies(bank_df['region'])
marrieddummy=pd.get_dummies(bank_df['married'])
cardummy=pd.get_dummies(bank_df['car'])
savingsdummy=pd.get_dummies(bank_df['savings_acct'])
currentdummy=pd.get_dummies(bank_df['current_acct'])
mortgagedummy=pd.get_dummies(bank_df['mortgage'])
pepdummy=pd.get_dummies(bank_df['pep'])
newdata_df=pd.concat([genderdummy,regiondummy,marrieddummy,cardummy,savingsdummy,currentdummy,mortgagedummy,pepdummy], axis=1)
newdata_df.head()

因此，根據建議，這是我現在擁有的：

## HW Part 6:  Converting Categorical Variables and Exporting Data
genderdummy=pd.get_dummies(bank_df['gender'])
regiondummy=pd.get_dummies(bank_df['region'])
dummy_vars = [bank_df('married'), bank_df('car'),bank_df('savings_acct'),bank_df('current_acct'),bank_df('mortgage'),bank_df('pep')]
pd.get_dummies(bank_df[dummy_vars])
newdata_df=pd.concat([genderdummy,regiondummy,dummy_vars], axis=1)
newdata_df.head()

Answer 1

如果您更改方法，它將自動執行此操作。 您只需要在數據幀而不是序列上調用pd.get_dummies ：

import numpy as np
import pandas as pd

# Define sample data and columns for dummy variables
df = pd.DataFrame(np.random.choice(['yes', 'no'], size=(6, 3)), columns=['gender', 'region', 'married'])
dummy_vars = ['gender', 'married']

# Create dummy variables
pd.get_dummies(df[dummy_vars])

   gender_no  gender_yes  married_no  married_yes
0          0           1           1            0
1          1           0           0            1
2          0           1           1            0
3          1           0           1            0
4          1           0           1            0
5          0           1           1            0

或者，您可以使用prefix參數來明確指定：

pd.get_dummies(df[dummy_vars], prefix=dummy_vars)

---

更新：

使用變量，它應如下所示：

genderdummy = pd.get_dummies(bank_df['gender'])
regiondummy = pd.get_dummies(bank_df['region'])
dummy_vars = ['married', 'car', 'savings_acct', 'current_acct', 'mortgage', 'pep']
other_dummies = pd.get_dummies(bank_df[dummy_vars])
newdata_df = pd.concat([genderdummy, regiondummy, other_dummies], axis=1)
newdata_df.head()

公告dummy_vars在你列的只是名字bank_df 。

Answer 2

在pandas.get_dummies（）中使用prefix參數

df = pd.DataFrame({'text':['cat', 'dog','cat','dog']})
df = pd.get_dummies(df['text'], prefix='text')
print(df)

產量

    text_cat    text_dog
0   1           0
1   0           1
2   1           0
3   0           1