Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Apprendre One-Hot Encoding | Processing Categorical Data
Data Preprocessing

book
One-Hot Encoding

So, let's start to understand when and what encoding methods are best to use.

One-hot encoding is generally better to use when the categorical variable has no natural ordering or hierarchy between the categories and when the number of unique categories is relatively small. It is commonly used for nominal categorical data, where the categories have no inherent order or relationship between them.

Take a look at some examples of nominal categorical data:

  • Colors: red, blue, green, yellow, etc.;

  • Countries: USA, Canada, Mexico, Japan, etc.;

  • Different pets: dog, cat, bird, fish, etc.;

  • Genres of music: pop, rock, hip hop, country, etc.;

  • Marital status: single, married, divorced, widowed, etc..

The basic idea behind one-hot encoding is to create a binary (0/1) variable for each category in the categorical variable.

We can perform one-hot encoding using the pd.get_dummies() method, which creates 3 new binary columns for each of the three unique color values. The resulting dataset shows the binary representation of each color value:

import pandas as pd

# Create a sample dataset with categorical data
dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']})

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['color'])

# Display the one-hot encoded dataframe
print(one_hot_encoded)
12345678910
import pandas as pd # Create a sample dataset with categorical data dataset = pd.DataFrame({'color': ['red', 'green', 'blue', 'red', 'blue']}) # Perform one-hot encoding one_hot_encoded = pd.get_dummies(dataset['color']) # Display the one-hot encoded dataframe print(one_hot_encoded)
copy
Tâche

Swipe to start coding

Use the one-hot encoding method on the 'cars.csv' dataset.

Solution

import pandas as pd

# Read the dataset
dataset = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/cars.csv', sep=';')

# Drop first column in the dataset
dataset = dataset.drop(dataset.index[0])

# Perform one-hot encoding
one_hot_encoded = pd.get_dummies(dataset['Origin'])

# Print encoded dataset
print(one_hot_encoded)

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 3. Chapitre 2
import pandas as pd

# Read the dataset
dataset = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/cars.csv', sep=';')

# Drop first column in the dataset
dataset = dataset.drop(dataset.index[0])

# Perform one-hot encoding
one_hot_encoded = pd.___(dataset['Origin'])

# Print encoded dataset
print(one_hot_encoded)

Demandez à l'IA

expand
ChatGPT

Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

some-alt