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

By on Tuesday, April 6, 2021

(Python Example for Beginners)

 

Write a Pandas program to insert a new column in existing DataFrame.

Sample DataFrame:
Sample Python dictionary data and list labels:
exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’],
‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]}
labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’]

 

Sample Solution :

Python Code :


import pandas as pd
import numpy as np

exam_data  = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
        'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']

df = pd.DataFrame(exam_data , index=labels)
print("Original rows:")
print(df)
color = ['Red','Blue','Orange','Red','White','White','Blue','Green','Green','Red']

df['color'] = color
print("nNew DataFrame after inserting the 'color' column")
print(df)

Sample Output:

Original rows:                                                          
   attempts       name qualify  score                                  
a         1  Anastasia     yes   12.5                                  
b         3       Dima      no    9.0                                  
c         2  Katherine     yes   16.5                                  
d         3      James      no    NaN                                  
e         2      Emily      no    9.0                                  
f         3    Michael     yes   20.0                                  
g         1    Matthew     yes   14.5                                  
h         1      Laura      no    NaN                                  
i         2      Kevin      no    8.0                                  
j         1      Jonas     yes   19.0                                  
                                                                       
New DataFrame after inserting the 'color' column                       
   attempts       name qualify  score   color                          
a         1  Anastasia     yes   12.5     Red                          
b         3       Dima      no    9.0    Blue                          
c         2  Katherine     yes   16.5  Orange                          
d         3      James      no    NaN     Red
e         2      Emily      no    9.0   White                          
f         3    Michael     yes   20.0   White                          
g         1    Matthew     yes   14.5    Blue                          
h         1      Laura      no    NaN   Green                          
i         2      Kevin      no    8.0   Green                          
j         1      Jonas     yes   19.0     Red

 

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

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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.
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