Label Encoding in Python in 2024

Q: What is Label Encoding?

Label encoding in Python is a simple and widely used preprocessing step in machine learning. Many algorithms cannot operate on categorical data. Therefore, we need to convert it to numerical data. In a label encoding , each category value is assigned a unique integer value. For example, the colour variable has three categories ‘Red’, ‘Blue’ and ‘Green’. We can encode these three categories into 0, 1 and 2. Some training algorithms assume that the output features are numeric when both categorical and continuous features are present.

Q: How to install a label encoder in Python?

Label encoding in Python can be performed using scikit-learn library LabelEncode r class which is part of the pre-processing module. However, this module is not activated by default while using a Python interpreter or using any Python IDE such as PyCharm or Spyder. You need to install the Scikit-Learn library explicitly in your site-packages directory by executing the command “–pip install -U scikit-learn.”

Q: How to label data using Python?

Labelling data in Python is done using the LabelEncoder class from scikit-learn. It usually involves a few steps: Importing Libraries Creating a Dataset: Initialising the Encoder Fitting and Transforming Viewing Results.

Q: Which library is used for label encoding?

The main library for label encoding in Python is Scikit-Learn. This library is quite popular in the machine learning community as it has many useful tools and functions for data preprocessing, model selection, and evaluation. The LabelEncoder class of scikit-learn can be used to easily achieve this task. In some simpler cases of label encoding , we can also use pandas' built-in functions.

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19, July 2023
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Label encoding is a crucial preprocessing technique in machine learning, converting categorical data into a numerical form. While the calendar ticks over to 2024, making sure how to perform label encoding in Python is important for both data scientists and practitioners in their work with machine learning.

Algorithms within machine learning typically require numbers to function appropriately. Furthermore, data in the form of labels or categories must be translated into a numerical form. The simplest technique for this kind of transformation is label encoding. Each category gets a unique integer, which the algorithms can then handle as necessary.

As businesses increasingly rely on data-driven decision-making, mastering label encoding becomes essential for professionals looking to enhance their skills and improve their models’ performance. This blog will explore the concept of label encoding, its implementation methods, and practical applications.

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Ways of Python Implementation of Label Encoding

There are many ways to do label encoding in Python. Here, we will use the two most popular libraries of Python for implementing LabelEncoder:

1. With Scikit-learn: The scikit-learn library provides a “LabelEncoder” class which is specifically designed for this purpose and can handle many more operations or scenarios than just the simple label encoding.
2. Using Pandas: The Pandas library offers simple ways to do label encoding. You have to use the “astype” method here.

LabelEncoder Class Using Scikit-Learn Library

To get started, you will need to import the required libraries and create an instance of the “LabelEncoder” class. Here is the process:

- Initialisation: Import all the necessary libraries and create a class using “LabelEncoder”.
- Fitting the Encoder: Next, you will fit the encoder to your categorical data. This is done by identifying all unique categories in the dataset.
- Transforming Data: Post-fitting, you can transform the categorical data to numerical labels. The “transform” method will assign a unique integer to each category based on its alphabetical order.
- Inverse Transformation: You can reverse the encoding using the “inverse_transform” method to get back the original categorical values if need be.

This method is efficient and simple, making it suitable for many machine-learning applications where categorical variables need to be converted into a format that algorithms can process.

Category Codes

Another way to do label encoding is using the Pandas library, which provides an easy way of handling categorical data :

1. Convert to Categorical Type: First, you have to convert your categorical column into Pandas category type. This makes sure Pandas understand that only a limited number of categories are present in the column.
2. Access Category Codes: After converting, directly access the category codes by “.cat.codes”. It returns an integer array in which each integer stands for the underlying code of that category in the original data.

This is especially convenient when dealing with DataFrame without installing extra dependencies.

Let’s use COVID-19 examples throughout states across the nation as an example to illustrate label encoding. The State column in the data frame below has a machine-unfriendly category value, while the other columns each have a number. Let’s encode the labels for the State column.

After label encoding, the numeric value is assigned to each of the categorical variables in the graphic below. The numbering is assigned according to alphabetical order, so it is not in sequence(Top-Bottom). Gujarat in 0, then Kerala in 1 and then so on.

States (Nominal Scale)	States (Label Encoding)
West Bengal	5
Kerala	1
Madhya Pradesh	2
Gujarat	0
Orissa	3
Uttar Pradesh	4

Where Can Label Encoding in Python Be Used?

There are multiple uses of Label Encoding in Python where categorical data variables need to be converted into the numerical format for further analysis. Some cases include:

- Preprocessing Categorical Data: Transforming categorical class labels into a particular numerical representation before applying an algorithm.
- Feature Engineering: In feature engineering, we can use label encoding in Python to derive new features from the categorical variables by mapping the categories to some numerical values, which will help improve the model performance.
- Data Visualisation: We can convert categorical variables into numerical ones so that it is fast and easy to operate on these rather than handling text data or strings. Using libraries like Matplotlib or Seaborn require numeric inputs for most of their functions.
- Natural Language Processing (NLP): In NLP tasks, label encoding can convert text labels into numerical numbers, which the machine learning algorithms can understand. It’s useful when we deal with some natural language processing tasks that require ordinal categories as features.
- Tree-based Algorithms: Certain tree-based algorithms can work with categorical features directly. However, we may have to perform label encoding to transform these categorical features into binary values.
- Neural Networks: In general, neural networks or deep learning models need numerical inputs. We would commonly begin by label encoding the categorical data as a first step to convert it into the required format.
- Generalised Linear Models: Most models, such as Logistic Regression, Linear Regression, etc., would again need numerical inputs, and we can apply label encoding to achieve that.
- Clustering Algorithms: Unsupervised learning algorithms like k-means clustering usually require numerical data. We can label and encode the categorical variables before we pass them to the clustering algorithm.

What is One-Hot Encoding?

The majority of algorithms for machine learning in use today cannot operate on data that is categorical. As an alternative, categorical data must first be changed into numbers. The technique used to carry out this conversion is one-hot encoding. This approach is typically employed when applying deep learning methods to issues involving consecutive classifications.

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Categorical variables are essentially represented as binary vectors in one-hot encoding. First, integer values are assigned to these categorical values. Then, every value of an integer is expressed as a binary vector made up of zeros only.

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Final Thoughts

In 2024, label encoding in Python remains useful in preprocessing all the data transformation into numerical format. For data scientists and machine learning practitioners, it is pivotal to master the implementation of label encoding in different libraries as a performance measure of a model, which relies directly on this step. Professionals may use a correctly implemented label encoding so that their models can precisely analyse the classification of variables and the outcome is more accurate.

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Frequently Asked Questions

What is Label Encoding?

Label encoding in Python is a simple and widely used preprocessing step in machine learning. Many algorithms cannot operate on categorical data. Therefore, we need to convert it to numerical data. In a label encoding, each category value is assigned a unique integer value.

For example, the colour variable has three categories ‘Red’, ‘Blue’ and ‘Green’. We can encode these three categories into 0, 1 and 2. Some training algorithms assume that the output features are numeric when both categorical and continuous features are present.

How to install a label encoder in Python?

Label encoding in Python can be performed using scikit-learn library LabelEncoder class which is part of the pre-processing module. However, this module is not activated by default while using a Python interpreter or using any Python IDE such as PyCharm or Spyder. You need to install the Scikit-Learn library explicitly in your site-packages directory by executing the command “–pip install -U scikit-learn.”

How to label data using Python?

Labelling data in Python is done using the LabelEncoder class from scikit-learn. It usually involves a few steps:

- Importing Libraries
- Creating a Dataset:
- Initialising the Encoder
- Fitting and Transforming
- Viewing Results.

Which library is used for label encoding?

The main library for label encoding in Python is Scikit-Learn. This library is quite popular in the machine learning community as it has many useful tools and functions for data preprocessing, model selection, and evaluation. The LabelEncoder class of scikit-learn can be used to easily achieve this task. In some simpler cases of label encoding, we can also use pandas’ built-in functions.

Which encoding method to use?

The correct encoding method depends on the nature of categorical data. Some common encoding methods are:

- Label Encoding: This encoding technique is best suited for ordinal categorical features – i.e., categorical features with some order involved (e.g.“Low”, “Medium”, “High”).
- One-Hot Encoding: This technique will create binary columns for each category and is better suited for nominal categorical variables.
- Target Encoding: Target encoding will replace the categories with the target mean (in case of classification) or target median (in case of regression).
- Binary Encoding: A hybrid approach of one hot and label encoding. The idea is to reduce the number of dimensions/binary columns while maintaining the information about categories.