Skip to content
Related Articles

Related Articles

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




# 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)







# 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)





# 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)





# 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)



Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :