Pandas Example – Write a Pandas program to filter words from a given series that contain atleast two vowels

(Python Example for Beginners)


Write a Pandas program to swap the cases of a specified character column in a given DataFrame.


Sample Solution:

Python Code :

import pandas as pd

df = pd.DataFrame({
    'company_code': ['Abcd','EFGF', 'zefsalf', 'sdfslew', 'zekfsdf'],
    'date_of_sale': ['12/05/2002','16/02/1999','25/09/1998','12/02/2022','15/09/1997'],
    'sale_amount': [12348.5, 233331.2, 22.5, 2566552.0, 23.0]

print("Original DataFrame:")

print("nSwapp cases in comapny_code:")
df['swapped_company_code'] = list(map(lambda x: x.swapcase(), df['company_code']))

Sample Output:

Original DataFrame:
  company_code date_of_sale  sale_amount
0         Abcd   12/05/2002      12348.5
1         EFGF   16/02/1999     233331.2
2      zefsalf   25/09/1998         22.5
3      sdfslew   12/02/2022    2566552.0
4      zekfsdf   15/09/1997         23.0

Swapp cases in comapny_code:
  company_code         ...          swapped_company_code
0         Abcd         ...                          aBCD
1         EFGF         ...                          efgf
2      zefsalf         ...                       ZEFSALF
3      sdfslew         ...                       SDFSLEW
4      zekfsdf         ...                       ZEKFSDF

[5 rows x 4 columns]


Pandas Example – Write a Pandas program to filter words from a given series that contain atleast two vowels

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.