Label encoding refers to converting labels to a numeric form in order to convert it to a machine-readable form. Machine learning algorithms can then better figure out how these labels should work. This is an important preprocessing step for a structured dataset in supervised learning.

Example:
Suppose we have a column in some dataset. 

After applying the label encoding, the Height column is converted to:

where 0 — label for tall, 1 — label for middle and 2 — for short stature.

We are applying the tag encoding to the iris dataset in the destination column, which is the View. Contains three species Iris-setosa, Iris-versicolor, Iris-virginica .

Exit:

 array ([`Iris-setosa`,` Iris-versicolor`, `Iris-virginica`], dtype = object)  
 After applying Label Encoding — 
 
 
 
  
 
 
 
 
  # Import the label encoder  
 
  from   sklearn   import   preprocessing   
  
  # label_encoder object knows how to understand word labels.  
 
  label_encoder   =   preprocessing.LabelEncoder ()  
 
  
  # Encode labels in the views column.  
 
  df [  `species`  ]   =   label_encoder.fit_transform (df [  `species`  ]) 
 
  
 
  df [  `species`  ]. unique () 
 
  
 
  
 
  Exit:
 
 array ([0, 1, 2], dtype = int64)  
  Label constraint Encoding  
 An encoding label converts data into machine-readable form, but assigns a unique number (starting at 0) to each data class. This can lead to the formation of a priority problem when training datasets. A high value label is considered to have higher priority than a lower value label. 
 
  example
 
 Attribute having output classes  Mexico ,  Paris ,  Dubai . On the Coding label of this column, let  mexico  be replaced with  0 ,  Paris  replaced with  1,  and  Dubai  is replaced by 2. 
 It can be interpreted that  Dubai  has a higher priority when training the model than  Mexico  and  Paris , but in fact there is no such priority relationship between these cities.