Label Encoding in Python in 2024

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. 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. 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.

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.

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.

*medium.com

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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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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