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)