Skip to content
Related Articles

Related Articles

Python | Pandas.CategoricalDtype()

Improve Article
Save Article
  • Last Updated : 10 Mar, 2022
Improve Article
Save Article

pandas.api.types.CategoricalDtype(categories = None, ordered = None) : This class is useful for specifying the type of Categorical data independent of the values, with categories and orderness. 
 

Parameters- 
categories : [index like] Unique categorization of the categories. 
ordered : [boolean] If false, then the categorical is treated as unordered. 
Return- Type specification for categorical data

Code: 
 

Python3




# Python code explaining
# numpy.pandas.CategoricalDtype()
    
# importing libraries
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
  
a = CategoricalDtype(['a', 'b', 'c'], ordered=True)
print ("a : ", a)
  
b = CategoricalDtype(['a', 'b', 'c'])
print ("\nb : ", b)
  
print ("\nTrue / False : ", a == CategoricalDtype(['a', 'b', 'c'],
                                                   ordered=False))
  
c = pd.api.types.CategoricalDtype(categories=["a","b","d","c"], ordered=True)
print ("\nType : ", c)

Python3




c1 = pd.Series(['a', 'b', 'a', 'e'], dtype = c)
print ("c1 : \n", c1)
  
  
c2 = pd.DataFrame({'A': list('abca'), 'B': list('bccd')})
  
c3 = CategoricalDtype(categories=list('abcd'), ordered=True)
  
c4 = c2.astype(c3)
  
print ("\n c4['A'] : \n", c4['A'])


My Personal Notes arrow_drop_up
Related Articles

Start Your Coding Journey Now!