Converting categorical column into a single dummy variable column

Question

Consider I have the following dataframe:

   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

I used the get_dummies() function to create dummy variable. The code and output are as follows:

one_hot = pd.get_dummies(dataset, columns = ['Category'])

This will 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

What I would like to have is a single column for Sex having the values 0 or 1 instead of 2 columns.

Interestingly, when I used get_dummies() on a different dataframe, it worked just like I wanted.
For the following dataframe:

  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...

With the code:

one_hot = pd.get_dummies(dataset, columns = ['Category'])

It 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

1. Why does get_dummies() work differently on these two dataframes?
2. How can I make sure I get the 2nd output everytime?

Answer 1

Here are multiple ways you can do:

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