{"id":18980,"date":"2023-11-08T10:17:38","date_gmt":"2023-11-08T04:47:38","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/label-encoding-in-python\/"},"modified":"2024-12-18T20:31:11","modified_gmt":"2024-12-18T15:01:11","slug":"label-encoding-in-python","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/label-encoding-in-python\/","title":{"rendered":"Label Encoding in Python"},"content":{"rendered":"\n

Introduction <\/strong><\/h2>\n\n\n\n

Label encoding is a technique used in machine learning and data analysis to convert categorical variables into numerical format. It is particularly useful when working with algorithms that require numerical input, as most machine learning models can only operate on numerical data. In this explanation, we'll explore how label encoding works and how to implement it in Python.<\/p>\n\n\n\n

Let's consider a simple example with a dataset containing information about different types of fruits, where the \"Fruit\" column has categorical values such as \"Apple,\" \"Orange,\" and \"Banana.\" Label encoding assigns a unique numerical label to each distinct category, transforming the categorical data into numerical representation.<\/p>\n\n\n\n

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To perform label encoding in Python, we can use the scikit-learn library, which provides a range of preprocessing utilities, including the LabelEncoder class. Here's a step-by-step guide:<\/p>\n\n\n\n

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  1. Import the necessary libraries:<\/li>\n<\/ol>\n\n\n\n
    pythonCopy codefrom sklearn.preprocessing import LabelEncoder\n<\/code><\/pre>\n\n\n\n
      \n
    1. Create an instance of the LabelEncoder class:<\/li>\n<\/ol>\n\n\n\n
      pythonCopy codelabel_encoder = LabelEncoder()\n<\/code><\/pre>\n\n\n\n
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      1. Fit the label encoder to the categorical data:<\/li>\n<\/ol>\n\n\n\n
        pythonCopy codelabel_encoder.fit(categorical_data)\n<\/code><\/pre>\n\n\n\n
        Here, categorical_data<\/code> refers to the column or array containing the categorical values you want to encode.<\/p>\n\n\n\n
          \n
        1. Transform the categorical data into numerical labels:<\/li>\n<\/ol>\n\n\n
          \npythonCopy codeencoded_data = label_encoder.transform(categorical_data)\n\n<\/pre><\/div>\n\n\n
          pythonCopy codeencoded_data = label_encoder.transform(categorical_data)\n<\/code><\/pre>\n\n\n\n
          The transform<\/code> method takes the original categorical data and returns an array with the corresponding numerical labels.<\/p>\n\n\n\n
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          1. If needed, you can also reverse the encoding to obtain the original categorical values using the inverse_transform<\/code> method:<\/li>\n<\/ol>\n\n\n\n
            pythonCopy codeoriginal_data = label_encoder.inverse_transform(encoded_data)\n<\/code><\/pre>\n\n\n\n
            Label encoding can also be applied to multiple columns or features simultaneously. You can repeat steps 3-5 for each categorical column you want to encode.<\/p>\n\n\n\n
            It is important to note that label encoding introduces an arbitrary order to the categorical values, which may lead to incorrect assumptions by the model. To avoid this issue, you can consider using one-hot encoding or other methods such as ordinal encoding, which provide more appropriate representations for categorical data.<\/p>\n\n\n\n
            Label encoding is a simple and effective way to convert categorical variables into numerical form. By using the LabelEncoder class from scikit-learn, you can easily encode your categorical data and prepare it for further analysis or input into machine learning algorithms.<\/p>\n\n\n\n
            Now, let us first briefly understand what data types are and its scale. It is important to know this for us to proceed with categorical variable encoding. Data can be classified into three types, namely, structured data, semi-structured, <\/strong>and unstructured data<\/strong>. <\/p>\n\n\n\n
            Structured data denotes that the data represented is in matrix form with rows and columns. The data can be stored in database SQL in a table<\/a>, CSV with delimiter separated, or excel with rows and columns.<\/p>\n\n\n\n
            The data which is not in matrix form can be classified into semi-Structured data (data in XML, JSON format) or unstructured data (emails, images, log data, videos, and textual data).<\/p>\n\n\n\n
            Let us say, for given data science or machine learning business problem if we are dealing with only structured data and the data collected is a combination of both Categorical variables and Continuous variables, most of the machine learning algorithms will not understand, or not be able to deal with categorical variables. Meaning, that machine learning algorithms will perform better in terms of accuracy and other performance metrics when the data is represented as a number<\/strong> instead of categorical to a model for training and testing. <\/p>\n\n\n\n
            Deep learning techniques such as the Artificial Neural network expect data to be numerical. Thus, categorical data must be encoded to numbers before we can use it to fit and evaluate a model.<\/p>\n\n\n\n
            Few ML algorithms such as Tree-based (Decision Tree<\/a>, Random Forest ) do a better job in handling categorical variables. The best practice in any data science project is to transform categorical data into a numeric value. <\/p>\n\n\n\n
            Now, our objective is clear. Before building any statistical models, machine learning<\/a>, or deep learning models, we need to transform or encode categorical data to numeric values. Before we get there, we will understand different types of categorical data as below.<\/p>\n\n\n\n    
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            Nominal Scale<\/strong><\/h2>\n\n\n\n
            The nominal scale refers to variables that are just named and are used for labeling variables. Note that all of A nominal scale refers to variables that are names. They are used for labeling variables. Note that all of these scales do not overlap with each other, and none of them has any numerical significance. <\/p>\n\n\n\n
            Below are the examples that are shown for nominal scale data. Once the data is collected, we should usually assign a numerical code to represent a nominal variable.<\/p>\n\n\n
            \"\"<\/figure>\n\n\n\n
            For example, we can assign a numerical code 1 to represent Bangalore, 2 for Delhi, 3 for Mumbai, and 4 for Chennai for a categorical variable- in which place do you live. Important to note that the numerical value assigned does not have any mathematical value attached to them. Meaning, that basic mathematical operations such as addition, subtraction, multiplication, or division are pointless. Bangalore + Delhi or Mumbai\/Chennai does not make any sense.<\/p>\n\n\n\n
            Ordinal Scale<\/strong><\/h2>\n\n\n\n
            An Ordinal scale is a variable in which the value of the data is captured from an ordered set. For example, customer feedback survey data uses a Likert scale that is finite, as shown below.<\/p>\n\n\n
            \"\"<\/figure>\n\n\n\n
            In this case, let\u2019s say the feedback data is collected using a five-point Likert scale. The numerical code 1, is assigned to Poor, 2 for Fair, 3 for Good, 4 for Very Good, and 5 for Excellent. We can observe that 5 is better than 4, and 5 is much better than 3. But if you look at excellent minus good, it is meaningless. <\/p>\n\n\n\n
            We very well know that most machine learning algorithms work exclusively with numeric data. That is why we need to encode categorical features into a representation compatible with the models. Hence, we will cover some popular encoding approaches:<\/p>\n\n\n\n