Pandas Example – Write a Pandas program to select columns by data type of a given DataFrame

Hits: 0

(Python Example for Beginners)

 

Write a Pandas program to select columns by data type of a given DataFrame.

 

Sample Solution :

Python Code :


import pandas as pd

df = pd.DataFrame({
    'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],
    'date_of_birth': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],
    'age': [18.5, 21.2, 22.5, 22, 23]
})

print("Original DataFrame")
print(df)

print("nSelect numerical columns")
print(df.select_dtypes(include = "number"))

print("nSelect string columns")
print(df.select_dtypes(include = "object"))

Sample Output:

Original DataFrame
             name date_of_birth   age
0  Alberto Franco    17/05/2002  18.5
1    Gino Mcneill    16/02/1999  21.2
2     Ryan Parkes    25/09/1998  22.5
3    Eesha Hinton    11/05/2002  22.0
4    Syed Wharton    15/09/1997  23.0

Select numerical columns
    age
0  18.5
1  21.2
2  22.5
3  22.0
4  23.0

Select string columns
             name date_of_birth
0  Alberto Franco    17/05/2002
1    Gino Mcneill    16/02/1999
2     Ryan Parkes    25/09/1998
3    Eesha Hinton    11/05/2002
4    Syed Wharton    15/09/1997

 

Pandas Example – Write a Pandas program to select columns by data type of a given DataFrame

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 *