CODEWITHSUNDEEP
machine-learningscikit-learnone-hot-encoding
Yagel
Yagel

Reputation: 1312

How to define oneHotEncoder to the titanic dataset

I'm trying to work on the titanic dataset. The data has categorical values, so I used labelEncoder to change the data to numbers, instead of text. Before:

     PassengerId  Survived  Pclass     Sex    Age  SibSp  Parch      Fare Embarked
0              1         0       3    male  22.00      1      0    7.2500        S
1              2         1       1  female  38.00      1      0   71.2833        C
2              3         1       3  female  26.00      0      0    7.9250        S

After:

     PassengerId  Survived  Pclass  Sex    Age  SibSp  Parch      Fare  Embarked
0              1         0       3    1  22.00      1      0    7.2500         2
1              2         1       1    0  38.00      1      0   71.2833         0
2              3         1       3    0  26.00      0      0    7.9250         2

This is the code:

from sklearn.preprocessing import LabelEncoder

labelencoder_X = LabelEncoder()    
data['Embarked'] = labelencoder_X.fit_transform(data['Embarked'])
data['Sex'] = labelencoder_X.fit_transform(data['Sex'])

Now, because the gender of the passenger is with the same importancy, I want to use oneHotEncoder. As I understand, the data should look the following:

     PassengerId  Survived  Pclass  Male Female    Age  SibSp  Parch      Fare  Embarked
0              1         0       3    1     0     22.00  1      0    7.2500         2
1              2         1       1    0     1     38.00  1      0   71.2833         0
2              3         1       3    0     1     26.00  0      0    7.9250         2

How can I write a code to do this? I have tried to work with similar method for oneHotEncoder:

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X = LabelEncoder()    
data['Embarked'] = labelencoder_X.fit_transform(data['Embarked'])
data['Sex'] = labelencoder_X.fit_transform(data['Sex'])

onehotencoder = OneHotEncoder()
data['Embarked'] = onehotencoder.fit_transform(data['Embarked'].values.reshape(-1,1))

But it just return the same result. How can I fix it? I'm new with Scikit and ML, I hope I'm doing things correctly.

Upvotes: 1

Views: 2065

Answers (1)

skillsmuggler
skillsmuggler

Reputation: 1902

This is how you can do it.

import pandas as pd
from sklearn.preprocessing import OneHotEncoder

# Sample data
   Sex
0    1
1    0
2    0
3    1

# OneHotEncoder
result = OneHotEncoder().fit_transform(df['Sex'].reshape(-1, 1)).toarray()

# Appending columns
df[['Female', 'Male']] = pd.DataFrame(result, index = df.index)

# Resulting dataframe
df
   Sex  Female  Male
0    1     0.0   1.0
1    0     1.0   0.0
2    0     1.0   0.0
3    1     0.0   1.0

Upvotes: 2

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