What is Label Encoding in Python

Introduction

Label encoding is a method utilized in machine studying and information evaluation to transform categorical variables into numerical format. It is especially helpful when working with algorithms that require numerical enter, as most machine studying fashions can solely function on numerical information. In this clarification, we’ll discover how label encoding works and tips on how to implement it in Python.

Let’s contemplate a easy instance with a dataset containing details about several types of fruits, the place the “Fruit” column has categorical values similar to “Apple,” “Orange,” and “Banana.” Label encoding assigns a singular numerical label to every distinct class, reworking the specific information into numerical illustration.

To carry out label encoding in Python, we will use the scikit-learn library, which offers a variety of preprocessing utilities, together with the LabelEncoder class. Here’s a step-by-step information:

1. Import the required libraries:

pythonCopy codefrom sklearn.preprocessing import LabelEncoder

2. Create an occasion of the LabelEncoder class:

pythonCopy codelabel_encoder = LabelEncoder()

3. Fit the label encoder to the specific information:

pythonCopy codelabel_encoder.match(categorical_data)

Here, categorical_data refers back to the column or array containing the specific values you need to encode.

4. Transform the specific information into numerical labels:

pythonCopy codeencoded_data = label_encoder.remodel(categorical_data)

The remodel methodology takes the unique categorical information and returns an array with the corresponding numerical labels.

5. If wanted, it’s also possible to reverse the encoding to acquire the unique categorical values utilizing the inverse_transform methodology:

pythonCopy codeoriginal_data = label_encoder.inverse_transform(encoded_data)

Label encoding can be utilized to a number of columns or options concurrently. You can repeat steps 3-5 for every categorical column you need to encode.

It is vital to notice that label encoding introduces an arbitrary order to the specific values, which can result in incorrect assumptions by the mannequin. To keep away from this problem, you may think about using one-hot encoding or different strategies similar to ordinal encoding, which give extra applicable representations for categorical information.

Label encoding is a straightforward and efficient option to convert categorical variables into numerical kind. By utilizing the LabelEncoder class from scikit-learn, you may simply encode your categorical information and put together it for additional evaluation or enter into machine studying algorithms.

Now, allow us to first briefly perceive what information varieties are and its scale. It is vital to know this for us to proceed with categorical variable encoding. Data might be labeled into three varieties, particularly, structured information, semi-structured, and unstructured information. 

Structured information denotes that the information represented is in matrix kind with rows and columns. The information might be saved in database SQL in a desk, CSV with delimiter separated, or excel with rows and columns.

The information which isn’t in matrix kind might be labeled into semi-Structured information (information in XML, JSON format) or unstructured information (emails, photographs, log information, movies, and textual information).

Let us say, for given information science or machine studying enterprise downside if we’re coping with solely structured information and the information collected is a mixture of each Categorical variables and Continuous variables, a lot of the machine studying algorithms is not going to perceive, or not be capable of take care of categorical variables. Meaning, that machine studying algorithms will carry out higher by way of accuracy and different efficiency metrics when the information is represented as a quantity as a substitute of categorical to a mannequin for coaching and testing. 

Deep studying strategies such because the Artificial Neural community anticipate information to be numerical. Thus, categorical information have to be encoded to numbers earlier than we will use it to suit and consider a mannequin.

Few ML algorithms similar to Tree-based (Decision Tree, Random Forest ) do a greater job in dealing with categorical variables. The greatest follow in any information science mission is to remodel categorical information right into a numeric worth. 

Now, our goal is obvious. Before constructing any statistical fashions, machine studying, or deep studying fashions, we have to remodel or encode categorical information to numeric values. Before we get there, we’ll perceive several types of categorical information as beneath.

Nominal Scale

The nominal scale refers to variables which can be simply named and are used for labeling variables. Note that each one of A nominal scale refers to variables which can be names. They are used for labeling variables. Note that each one of those scales don’t overlap with one another, and none of them has any numerical significance. 

Below are the examples which can be proven for nominal scale information. Once the information is collected, we should always often assign a numerical code to characterize a nominal variable.

For instance, we will assign a numerical code 1 to characterize Bangalore, 2 for Delhi, 3 for Mumbai, and 4 for Chennai for a categorical variable- through which place do you reside. Important to notice that the numerical worth assigned doesn’t have any mathematical worth connected to them. Meaning, that fundamental mathematical operations similar to addition, subtraction, multiplication, or division are pointless. Bangalore + Delhi or Mumbai/Chennai doesn’t make any sense.

Ordinal Scale

An Ordinal scale is a variable through which the worth of the information is captured from an ordered set. For instance, buyer suggestions survey information makes use of a Likert scale that’s finite, as proven beneath.

In this case, let’s say the suggestions information is collected utilizing 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 healthier than 4, and 5 is a lot better than 3. But if you happen to have a look at wonderful minus good, it’s meaningless. 

We very nicely know that the majority machine studying algorithms work solely with numeric information. That is why we have to encode categorical options right into a illustration appropriate with the fashions. Hence, we’ll cowl some in style encoding approaches:

• Label encoding
• One-hot encoding
• Ordinal Encoding

Label Encoding

In label encoding in Python, we substitute the specific worth with a numeric worth between 0 and the variety of courses minus 1. If the specific variable worth accommodates 5 distinct courses, we use (0, 1, 2, 3, and 4).

To perceive label encoding with an instance, allow us to take COVID-19 circumstances in India throughout states. If we observe the beneath information body, the State column accommodates a categorical worth that isn’t very machine-friendly and the remainder of the columns include a numerical worth. Let us carry out Label encoding for State Column.

From the beneath picture, after label encoding, the numeric worth is assigned to every of the specific values. You is perhaps questioning why the numbering isn’t in sequence (Top-Down), and the reply is that the numbering is assigned in alphabetical order. Delhi is assigned 0 adopted by Gujarat as 1 and so forth.

Label Encoding utilizing Python

• Before we proceed with label encoding in Python, allow us to import vital information science libraries similar to pandas and NumPy.
• Then, with the assistance of panda, we’ll learn the Covid19_India information file which is in CSV format and examine if the information file is loaded correctly. With the assistance of information(). We can discover {that a} state datatype is an object. Now we will proceed with LabelEncoding. 

Label Encoding might be carried out in 2 methods particularly:

• LabelEncoder class utilizing scikit-learn library 
• Category codes

Approach 1 – scikit-learn library strategy

As Label Encoding in Python is a part of information preprocessing, therefore we’ll take an assist of preprocessing module from sklearn bundle and import LabelEncoder class as beneath:

And then:

1. Create an occasion of LabelEncoder() and retailer it in labelencoder variable/object
2. Apply match and remodel which does the trick to assign numerical worth to categorical worth and the identical is saved in new column referred to as “State_N”
3. Note that we have now added a brand new column referred to as “State_N” which accommodates numerical worth related to categorical worth and nonetheless the column referred to as State is current within the dataframe. This column must be eliminated earlier than we feed the ultimate preprocess information to machine studying mannequin to study

Approach 2 – Category Codes

1. As you had already noticed that “State” column datatype is an object kind which is by default therefore, have to convert “State” to a class kind with the assistance of pandas
2. We can entry the codes of the classes by operating covid19[“State].cat.codes

One potential problem with label encoding is that more often than not, there is no such thing as a relationship of any sort between classes, whereas label encoding introduces a relationship. 

In the above six courses’ instance for “State” column, the connection appears to be like as follows: 0 < 1 < 2 < 3 < 4 < 5. It implies that numeric values might be misjudged by algorithms as having some kind of order in them. This doesn’t make a lot sense if the classes are, for instance, States. 

Also Read: 5 widespread errors to keep away from whereas working with ML

There is not any such relation within the authentic information with the precise State names, however, through the use of numerical values as we did, a number-related connection between the encoded information is perhaps made. To overcome this downside, we will use one-hot encoding as defined beneath.

One-Hot Encoding

In this strategy, for every class of a characteristic, we create a brand new column (typically referred to as a dummy variable) with binary encoding (0 or 1) to indicate whether or not a selected row belongs to this class. 

Let us contemplate the earlier State column, and from the beneath picture, we will discover that new columns are created ranging from state title Maharashtra until Uttar Pradesh, and there are 6 new columns created. 1 is assigned to a selected row that belongs to this class, and 0 is assigned to the remainder of the row that doesn’t belong to this class. 

A possible disadvantage of this methodology is a major improve within the dimensionality of the dataset (which is known as a Curse of Dimensionality).

Meaning, one-hot encoding is the truth that we’re creating extra columns, one for every distinctive worth within the set of the specific attribute we’d prefer to encode. So, if we have now a categorical attribute that accommodates, say, 1000 distinctive values, that one-hot encoding will generate 1,000 extra new attributes and this isn’t fascinating.

To maintain it easy, one-hot encoding is sort of a robust instrument, however it’s only relevant for categorical information which have a low variety of distinctive values.

Creating dummy variables introduces a type of redundancy to the dataset. If a characteristic has three classes, we solely have to have two dummy variables as a result of, if an commentary is neither of the 2, it have to be the third one. This is also known as the dummy-variable lure, and it’s a greatest follow to all the time take away one dummy variable column (often called the reference) from such an encoding.

Data shouldn’t get into dummy variable traps that can result in an issue often called multicollinearity. Multicollinearity happens the place there’s a relationship between the impartial variables, and it’s a main risk to a number of linear regression and logistic regression issues.

To sum up, we should always keep away from label encoding in Python when it introduces false order to the information, which may, in flip, result in incorrect conclusions. Tree-based strategies (determination bushes, Random Forest) can work with categorical information and label encoding. However, for algorithms similar to linear regression, fashions calculating distance metrics between options (k-means clustering, k-Nearest Neighbors) or Artificial Neural Networks (ANN) are one-hot encoding.

One-Hot Encoding utilizing Python

Now, let’s see tips on how to apply one-hot encoding in Python. Getting again to our instance, in Python, this course of might be applied utilizing 2 approaches as follows:

• scikit-learn library 
• Using Pandas

Approach 1 – scikit-learn library strategy

1. As one-hot encoding can be a part of information preprocessing, therefore we’ll take an assist of preprocessing module from sklearn bundle and them import OneHotEncoder class as beneath
2. Instantiate the OneHotEncoder object, notice that parameter drop = ‘first’ will deal with dummy variable traps
3. Perform OneHotEncoding for categorical variable

4. Merge One Hot Encoded Dummy Variables to Actual information body however don’t forget to take away the precise column referred to as “State”
5. From the beneath output, we will observe, dummy variable lure has been taken care

Approach 2 – Using Pandas: with the assistance of get_dummies operate

• As everyone knows, one-hot encoding is such a typical operation in analytics, that pandas present a operate to get the corresponding new options representing the specific variable.
• We are contemplating the identical dataframe referred to as “covid19” and imported pandas library which is adequate to carry out one sizzling encoding

• As you discover beneath code, this generates a brand new DataBody containing 5 indicator columns, as a result of as defined earlier for modeling we don’t want one indicator variable for every class; for a categorical characteristic with Okay classes, we’d like solely Okay-1 indicator variables. In our instance, “State_Delhi” was eliminated
• In the case of 6 classes, we’d like solely 5 indicator variables to protect the knowledge (and keep away from collinearity). That is why the pd.get_dummies operate has one other Boolean argument, drop_first=True, which drops the primary class
• Since the pd.get_dummies operate generates one other DataBody, we have to concatenate (or add) the columns to our authentic DataBody and in addition don’t neglect to take away column referred to as “State”

• Here, we use the pd.concat operate, indicating with the axis=1 argument that we need to concatenate the columns of the two DataFrames given within the listing (which is the primary argument of pd.concat). Don’t neglect to take away precise “State” column

Ordinal Encoding

An Ordinal Encoder is used to encode categorical options into an ordinal numerical worth (ordered set). This strategy transforms categorical worth into numerical worth in ordered units.

This encoding approach seems nearly much like Label Encoding. But, label encoding wouldn’t contemplate whether or not a variable is ordinal or not, however within the case of ordinal encoding, it can assign a sequence of numerical values as per the order of information.

Let’s create a pattern ordinal categorical information associated to the shopper suggestions survey, after which we’ll apply the Ordinal Encoder approach. In this case, let’s say the suggestions information is collected utilizing a Likert scale through which numerical code 1 is assigned to Poor, 2 for Good, 3 for Very Good, and 4 for Excellent. If you observe, we all know that 5 is healthier than 4, 5 is a lot better than 3, however taking the distinction between 5 and a couple of is meaningless (Excellent minus Good is meaningless).

Ordinal Encoding utilizing Python

With the assistance of Pandas, we’ll assign buyer survey information to a variable referred to as “Customer_Rating” via a dictionary after which we will map every row for the variable as per the dictionary.

That brings us to the tip of the weblog on Label Encoding in Python. We hope you loved this weblog. Also, take a look at this free Python for Beginners course to study the Fundamentals of Python. If you want to discover extra such programs and study new ideas, be a part of the Great Learning Academy free course at present.