Python | Pandas.factorize()

Last Updated : 27 Sep, 2018
pandas.factorize() method helps to get the numeric representation of an array by identifying distinct values. This method is available as both pandas.factorize() and Series.factorize().
Parameters: values : 1D sequence. sort : [bool, Default is False] Sort uniques and shuffle labels. na_sentinel : [ int, default -1] Missing Values to mark 'not found'. Return: Numeric representation of array
Code: Explaining the working of factorize() method Python3 1== 
# importing libraries
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype

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

print("Numeric Representation : \n", labels)
print("Unique Values : \n", uniques)
Python3 1== 
# sorting the numerics
label1, unique1 = pd.factorize(['b', 'd', 'd', 'c', 'a', 'c', 'a', 'b'], 
                                                           sort = True)

print("\n\nNumeric Representation : \n", label1)
print("Unique Values : \n", unique1)
Python3 1== 
# Missing values indicated
label2, unique2 = pd.factorize(['b', None, 'd', 'c', None, 'a', ], 
                                              na_sentinel = -101)

print("\n\nNumeric Representation : \n", label2)
print("Unique Values : \n", unique2)
Python3 1== 
# When factorizing pandas object; unique will differ 
a = pd.Categorical(['a', 'a', 'c'], categories =['a', 'b', 'c'])

label3, unique3 = pd.factorize(a)

print("\n\nNumeric Representation : \n", label3)
print("Unique Values : \n", unique3)
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