If you have ever worked with machine learning or data preprocessing in Python, you may have encountered categorical data. Since most machine learning models work best with numerical data, we need a way to convert categories into numbers without losing their meaning. This is where one-hot encoding comes into play! One-hot encoding can be done in Python using pandas.get_dummies() for DataFrames or sklearn.preprocessing. OneHotEncoder for NumPy arrays.
In this blog, we’ll explain what one-hot encoding is, why it is useful, and how you can implement it in Python with ease. Let’s get started!
Table of Contents
What is One-hot Encoding?
One-hot encoding is a method to denote categorical data as binary vectors.
It helps to create a unique binary vector for each vector by avoiding unintended ordinal relationships.
For example, let’s say we have a column named “Color” with three categories: Red, Green, and Blue. With the help of One-hot encoding, you can transform it into:
|
| Color | Fruit_Apple | Fruit_Banana | Fruit_Orange |
| Red | 1 | 0 | 0 |
| Green | 0 | 1 | 0 |
| Blue | 0 | 0 | 1 |
From the above table, we can see that each category is now represented by a binary vector, thus making it easy for machine learning models to interpret.
What is the Difference between Encoding and One-Hot Encoding?
Given below is a detailed tabular comparison between Encoding and One-Hot Encoding, highlighting all the important differences:
| Feature | Encoding | One-Hot Encoding |
| Definition | It is a general method to convert categorical data into numerical form. | It is a specific encoding method, which creates binary columns for each category. |
| Types | It includes Label Encoding, Ordinal Encoding, Target Encoding, Frequency Encoding etc. | It has only one type: binary representation of categories. |
| Number of Columns | Retains the original number of columns. | For each unique category, it creates new binary columns. |
| Handling of Order | It may introduce an artificial ordinal relationship (e.g., “Red” = 0, “Blue” = 1). | It doesn’t assume any order among categories. |
| Computational Cost | It has a low computational cost. | It has a high computational cost. |
| Interpretability | It is less interpretable, as numerical labels may not have direct meaning. | It is more interpretable as binary representation is easier to understand. |
| Scalability | It works well for large datasets with high cardinality. | It can become impractical when the number of unique categories is too high. |
| Example Input | [‘Red’, ‘Blue’, ‘Green’] | [‘Red’, ‘Blue’, ‘Green’] |
| Example Output | Label Encoding: {‘Red’:0, ‘Blue’:1, ‘Green’:2} | One-Hot Encoding: Red: [1,0,0], Blue: [0,1,0], Green: [0,0,1] |
| Suitable Models | It works well with tree-based models like Decision Trees, XGBoost, and Random Forest] | Preferred for linear models and deep learning models like Logistic Regression, and Neural Networks. |
| Potential Issue | It can introduce misinterpretation of relationships between categories. | Curse of dimensionality increases dataset size if too many categories exist. |
| Use Case Example | If we have [‘Low’, ‘Medium’, ‘High’], encoding as [0,1,2] makes sense. | If we have [‘Red’, ‘Green’, ‘Blue’], one-hot encoding does not create a false order. |
How to One-Hot Encode in Python?
There are methods in which one-hot encoding can be implemented in Python. So let’s explore the most relevant methods.
Method 1: Using pandas.get_dummies()
If you are working with the “pandas” Dataframe, the best way to one-hot encode is by using pd.get_dummies().
Example:
Output:
Explanation:
The above code creates a Pandas DataFrame using a categorical “Fruit” column. It applies One-Hot Encoding using pd.get_dummies(), which converts each fruit category into separate binary columns.
Method 2: Using OneHotEncoder from sklearn
For the building of Machine Learning models using sklearn.preprocessing.OneHotEncoder gives you more flexibility.
Example:
Output:
Explanation:
One important difference here is that the OneHotEncoder returns a NumPy array instead of a data frame. If you need columns, you can use: encoder.get_feature_names_out().
Method 3: Using TensorFlow/Keras for Deep Learning
If you are working with Deep Learning models, TensorFlow/Keras also provides a way to encode labels.
Example:
Output:
Explanation:
The above code converts NumPy arrays of categorical labels (labels) into a one-hot encoded format. It uses TensorFlow’s to_categorical(), which makes it suitable for training machine learning models.
When should you use One-Hot encoding?
One-hot encoding is useful when:
- You have categorical data without any natural ordering(e.g., colors, cities, brands).
- Your machine learning models do not support categorical variables (most do not).
- You have a small number of unique categories(if too many, it can lead to high memory usage).
When Should You Avoid One-Hot Encoding?
- High Cardinality Data – If there are too many unique values in a categorical feature (e.g., thousands of zip codes), too many columns are created by one-hot encoding, which leads to memory inefficiency and slow computation.
- When data is ordinal – When features are in natural order (e.g., “Low”, “Medium”, “High”), the ordinal relationship is lost by one-hot encoding. Instead, you can use label encoding or ordinal encoding.
- Sparse Data Issues – When there are cases where one-hot encoded columns contain zeros, the dataset becomes sparse, which makes it harder for models to learn useful patterns.
- Tree-Based Models – Random Forests, Decision Trees, and Gradient-Boosting models (like XGBoost) can handle categorical variables directly, which makes one-hot encoding unnecessary and even less efficient.
- Increased Computational Cost – One-Hot Encoding significantly increases the number of features with many categorical variables, which makes training slower and more computationally expensive.
- When Using Distance-Based Algorithms – Algorithms like k-NN and k-Means clustering increase dimensionality without preserving meaningful relationships between categories. Therefore, they may not work well with one-hot encoding.
- Limited Data – If there is a small dataset, the introduction of too many one-hot encoded features can lead to overfitting. Here, the model memorizes the data instead of generalizing well.
Advantages and Disadvantages of One-Hot Encoding
One-Hot Encoding is a popular technique to convert categorical data into a numerical format, which helps the machine learning models to process it effectively. Although it offers several advantages, it also has limitations that ought to be considered before using it.
Some of the advantages of using One-Hot Encoding are mentioned below:
- Makes Categorical Data Usable for Machine Learning:
Most ML algorithms, like linear models and neural networks, are not able to process categorical data directly. Therefore, they use one-hot encoding, which transforms categorical values into a format that models can understand and train on.
- Avoids Ordinal Misinterpretation:
One-Hot Encoding does not assign random numerical values to categories. This is because it prevents models from assuming a false ordinal relationship between them. For example, If [‘Red’, ‘Blue’, ‘Green’] is label-encoded as [0, 1, 2], the model might incorrectly assume that Green (2) is greater than Blue(1).
- Works well with Linear Models:
Linear Models (for e.g., Logistic Regression) benefit from one-hot encoding. This is because each category gets its independent feature, which makes feature importance clearer.
- Useful for Neural Networks:
Categorical Data are often required by Deep Learning models which are to be transformed into numerical form. For this, One-Hot Encoding is a simpler and effective way to do so.
- Improves Interpretability in Some Cases:
While working with small datasets, one-hot encoding allows a clear separation of categories. This helps in understanding feature importance.
Some of the disadvantages of using one-hot encoding are given below:
- High Dimensionality:
If there are too many unique values in a categorical variable (e.g., thousands of city names), a large number of new features are created by one-hot encoding.
- Increases the complexity of the model:
Having more features means that the model needs to process and optimize a larger dataset, which increases the computation time. If there isn’t enough training data, it can make models prone to overfitting.
- Leads to Sparse Matrices:
The generated matrix contains mostly zeros, which makes it sparse. For certain models, sparse data can be inefficient, like k-NN and k-means clustering, which rely on distance calculations.
- Not Always Suitable for Tree-Based Models:
Algorithms like Random Forests, XGBoost, and Decision Trees can handle categorical data directly, and they may perform better without one-hot encoding. Splitting of one-hot encoded variables may lead to unnecessary complexity in tree-based models.
- Difficult to Handle New Categories:
If new categorical values appear in the test set but don’t appear in the training set, one-hot encoding will fail unless there is an implementation of extra handling (e.g., adding an “unknown” category).
- Increased Risk of Overfitting:
If the dataset has many categories but is small, one-hot encoding can lead to overfitting. Here the model memorizes category-specific details instead of generalizing.
Best Practices for One-Hot Encoding
- Use One-Hot Encoding for nominal data only. Avoid the use of ordinal data (e.g., “low”, “medium”, “high”), where label encoding is more appropriate.
- Handle High Cardinality- If there are too many unique values in a feature, you can group rare categories, use hashing, or you can apply target encoding to avoid excessive dimensionality.
- Drop One Column to Avoid Multicollinearity- You can remove one column to prevent redundancy and it reduces the risk of correlated features affecting linear models.
- Applying Encoding After Train-Test-Split- Helps to prevent data leakage by fitting the encoder only on the training data and then separately transforming the test data.
- Handle Unknown Categories in the Test Set- You can use handle_unknown=’ignore’ in OneHotEncoder which prevents errors when encountering new categories in the test set.
Conclusion
In machine learning, one-hot encoding is a basic yet effective method for managing categorical data. No matter if you are using pandas.get_dummies(), sklearn.preprocessing.OneHotEncoder or other deep learning methods, understanding when and where to apply the right method at the right place will make your data preprocessing workflow much smoother. If you want to learn more about this technology, then check out our Comprehensive Data Science Course.
FAQs
1. What is One-Hot Encoding in Python?
One-hot encoding in Python is a technique that is used to convert categorical variables into binary vectors, which makes it suitable for machine learning models that require numerical input.
2. What are the common ways to perform One-Hot Encoding in Python?
The common ways to perform One-Hot Encoding in Python are: You can use pandas.get_dummies() for DataFrames, sklearn.preprocessing.OneHotEncoder for NumPy arrays or tensorflow.keras.utils.to_categories() for deep learning applications.
When should I use One-Hot Encoding?
You can use One-Hot Encoding when dealing with categorical features that do not have an inherent ordinal relationship, such as color names or product categories.
What are the drawbacks of One-Hot Encoding?
Some of the drawbacks of one-hot encoding are: One-hot encoding can lead to a high-dimensional feature space, which causes increased memory usage and computational complexity, especially with large categorical datasets.
What are the alternatives to one-hot encoding?
As alternatives to one-hot encoding, you can use label encoding, target encoding, binary encoding, and embedding layers (for deep learning), which help to manage dimensionality and preserve feature relationships.