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)
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# 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)
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# 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)
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# 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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