Python | Pandas TimedeltaIndex.factorize

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) 

Python | Pandas TimedeltaIndex.factorize

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