# Factorize Data Values in Pandas

In this tutorial, we will learn to factorize in Pandas. We will be using the `pandas.factorize()` function to perform the task.

By recognizing different values, the `pandas.factorize()` method aids in obtaining the numeric representation of an array.

Firstly, we will import the `Pandas` and `numpy` libraries and other required libraries.

``````import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
``````

## Use the `pandas.factorize()` Function in Pandas

Now we will pass a list containing the characters to the `factorize()` function, which will return us the labels and the unique values. We will output the labels and unique values separately.

``````labels, uniques = pd.factorize(['b', 'd', 'd', 'c', 'a', 'c', 'a', 'b'])
``````

The above code will return us the list of the numeric representations of characters and the unique values.

Let us see the output using the below code.

``````print("Numeric Representation : \n", labels)
print("Unique Values : \n", uniques)
``````
``````Numeric Representation :
[0 1 1 2 3 2 3 0]
Unique Values :
['b' 'd' 'c' 'a']
``````

We can also sort the alphabet using the below code.

``````labels, uniques = pd.factorize(['b', 'd', 'd', 'c', 'a', 'c', 'a', 'b'], sort = True)
``````

We will have the below output for the above amendment.

``````Numeric Representation :
[1 3 3 2 0 2 0 1]
Unique Values :
['a' 'b' 'c' 'd']
``````

We can also use categories to divide the data values into a category, and in this case, the unique values will differ. For this purpose, we will use the `pd.Categorical()` function to divide our data values.

``````a = pd.Categorical(['a', 'a', 'c'], categories =['a', 'b', 'c'])

label3, unique3 = pd.factorize(a)
``````

Let us now see the output of the above code.

``````Numeric Representation :
[0 0 1]
Unique Values :
['a', 'c']
Categories (3, object): ['a', 'b', 'c']
``````

We can see in the above output that our unique values list contains only the unique values.

Therefore, we can factorize the data values using Pandas using the following approaches.

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