{"id":11001,"date":"2020-11-30T12:17:10","date_gmt":"2020-11-30T12:17:10","guid":{"rendered":"https:\/\/www.askpython.com\/?p=11001"},"modified":"2023-02-16T19:56:57","modified_gmt":"2023-02-16T19:56:57","slug":"label-encoding","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python\/examples\/label-encoding","title":{"rendered":"Label Encoding in Python – A Quick Guide!"},"content":{"rendered":"\n

Hello, readers! In this article, we will be focusing on Label Encoding<\/strong> in Python.<\/p>\n\n\n\n

In our last article, we understood the working and implementation of One hot Encoding<\/a> wherein Label Encoding is the initial step of the process. <\/p>\n\n\n\n

Today, we’ll have a look at one of the most fundamental steps in the categorical encoding of data values.<\/p>\n\n\n\n

So, without any further delay, let us begin!<\/p>\n\n\n\n

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What is Label Encoding in Python?<\/h2>\n\n\n\n

Before diving deep into the concept of Label Encoding, let us understand the impact of the concept of ‘Label’ on the dataset.<\/p>\n\n\n\n

A label <\/strong>is actually a number or a string that represents a particular set of entities. Labels helps the model in better understanding of the dataset and enables the model to learn more complex structures.<\/p>\n\n\n\n

Recommended – How to standardize datasets for Machine learning?<\/a><\/em><\/p>\n\n\n\n

Label Encoder<\/strong> performs the conversion of these labels of categorical data into a numeric format.<\/p>\n\n\n\n

For example, if a dataset contains a variable ‘Gender’ with labels ‘Male’ and ‘Female’, then the label encoder would convert these labels into a number format and the resultant outcome would be [0,1].<\/p>\n\n\n\n

Thus, by converting the labels into the integer format, the machine learning model can have a better understanding in terms of operating the dataset.<\/p>\n\n\n\n

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Label Encoding – Syntax to know!<\/h2>\n\n\n\n

Python sklearn library<\/strong> provides us with a pre-defined function to carry out Label Encoding on the dataset.<\/p>\n\n\n\n

Syntax:<\/strong><\/p>\n\n\n

\nfrom sklearn import preprocessing  \nobject = preprocessing.LabelEncoder() \n<\/pre><\/div>\n\n\n
Here, we create an object of the LabelEncoder class and then utilize the object for applying label encoding on the data.<\/p>\n\n\n\n
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1. Label Encoding with sklearn<\/h3>\n\n\n\n
Let’s get right into the process on label encoding. The first step to encoding a dataset is to have a dataset. <\/p>\n\n\n\n
So, we’ll create a simple dataset here. Example: Creation of a dataset<\/strong><\/p>\n\n\n
\nimport pandas as pd \ndata = {"Gender":['M','F','F','M','F','F','F'], "NAME":['John','Camili','Rheana','Joseph','Amanti','Alexa','Siri']}\nblock = pd.DataFrame(data)\nprint("Original Data frame:\\n")\nprint(block)\n<\/pre><\/div>\n\n\n
Here, we have created a dictionary<\/a> ‘data’ and then transformed it into a DataFrame using pandas.DataFrame() <\/code>function.<\/p>\n\n\n\n
Output:<\/strong><\/p>\n\n\n
\nOriginal Data frame:\n\n  Gender    NAME\n0      M    John\n1      F  Camili\n2      F  Rheana\n3      M  Joseph\n4      F  Amanti\n5      F   Alexa\n6      F    Siri\n<\/pre><\/div>\n\n\n
From the above dataset, it is clear that the variable ‘Gender’ has labels as ‘M’ and ‘F’.<\/p>\n\n\n\n
Further, now let us import the LabelEncoder<\/strong> class and applying it on the ‘Gender’ variable of the dataset.<\/p>\n\n\n
\nfrom sklearn import preprocessing \nlabel = preprocessing.LabelEncoder() \n\nblock['Gender']= label.fit_transform(block['Gender']) \nprint(block['Gender'].unique())\n<\/pre><\/div>\n\n\n
We have used fit_transform() method<\/code> to apply the functionality of the label encoder pointed by the object to the data variable.<\/p>\n\n\n\n
Output:<\/strong><\/p>\n\n\n
\n[1 0]\n<\/pre><\/div>\n\n\n
So, you see, the data has been transformed into integer labels of [0,1].<\/p>\n\n\n
\nprint(block)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\nGender    NAME\n0       1    John\n1       0  Camili\n2       0  Rheana\n3       1  Joseph\n4       0  Amanti\n5       0   Alexa\n6       0    Siri\n<\/pre><\/div>\n\n\n
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2. Label Encoding using Category codes<\/strong><\/h3>\n\n\n\n
Let us first check the data type of the variables of our dataset.<\/p>\n\n\n
\nblock.dtypes\n<\/pre><\/div>\n\n\n
Data type<\/strong>:<\/p>\n\n\n
\nGender    object\nNAME      object\ndtype: object\n<\/pre><\/div>\n\n\n
Now, transform and convert the datatype of the variable ‘Gender’ to category<\/strong> type.<\/p>\n\n\n
\nblock['Gender'] = block['Gender'].astype('category')\n<\/pre><\/div>\n\n
\nblock.dtypes\n<\/pre><\/div>\n\n
\nGender    category\nNAME        object\ndtype: object\n<\/pre><\/div>\n\n\n
Now, let us transform the labels to integer types using pandas.DataFrame.cat.codes<\/code> function.<\/p>\n\n\n
\nblock['Gender'] = block['Gender'].cat.codes\n<\/pre><\/div>\n\n
\nprint(block)\n<\/pre><\/div>\n\n\n
As seen below, the variable ‘Gender’ has been encoded to integer values [0,1].<\/p>\n\n\n
\nGender    NAME\n0       1    John\n1       0  Camili\n2       0  Rheana\n3       1  Joseph\n4       0  Amanti\n5       0   Alexa\n6       0    Siri\n<\/pre><\/div>\n\n\n
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Conclusion<\/h2>\n\n\n\n
By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question. <\/p>\n\n\n\n
For a deeper understanding of the topic, try implementing the concept of Label Encoder on different dataset and variables. Do let us know your experience in the comment section ! \ud83d\ude42<\/p>\n\n\n\n
For more such posts related to Python, Stay tuned and till then, Happy Learning!! \ud83d\ude42<\/p>\n\n\n\n
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References<\/h2>\n\n\n\n