Pandas Example – Write a Pandas program to convert continuous values of a column in a given DataFrame to categorical

Hits: 2

(Python Example for Beginners)

 

Write a Pandas program to convert continuous values of a column in a given DataFrame to categorical.

Input:
{ ‘Name’: [‘Alberto Franco’,’Gino Mcneill’,’Ryan Parkes’, ‘Eesha Hinton’, ‘Syed Wharton’], ‘Age’: [18, 22, 40, 50, 80, 5] }
Output:
Age group:
0 kids
1 adult
2 elderly
3 adult
4 elderly
5 kids
Name: age_groups, dtype: category
Categories (3, object): [kids < adult < elderly]

 

Sample Solution :

Python Code :


import pandas as pd

df = pd.DataFrame({
    'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton', 'Kierra Gentry'],
      'age': [18, 22, 85, 50, 80, 5]
})

print("Original DataFrame:")
print(df)

print('nAge group:')
df["age_groups"] = pd.cut(df["age"], bins = [0, 18, 65, 99], labels = ["kids", "adult", "elderly"])
print(df["age_groups"])

Sample Output:

Original DataFrame:
             name  age
0  Alberto Franco   18
1    Gino Mcneill   22
2     Ryan Parkes   85
3    Eesha Hinton   50
4    Syed Wharton   80
5   Kierra Gentry    5

Age group:
0       kids
1      adult
2    elderly
3      adult
4    elderly
5       kids
Name: age_groups, dtype: category
Categories (3, object): [kids < adult < elderly]

 

Pandas Example – Write a Pandas program to convert continuous values of a column in a given DataFrame to categorical

Sign up to get end-to-end “Learn By Coding” example.


Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Leave a Reply

Your email address will not be published. Required fields are marked *