Pandas Example – Write a Pandas program to convert the datatype of a given column(float to int)

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(Python Example for Beginners)

 

Write a Pandas program to convert the datatype of a given column(float to int).

 

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, 9.1, 16.5, 12.77, 9.21, 20.22, 14.5, 11.34, 8.8, 19.13],
        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

df = pd.DataFrame(exam_data)
print("Original DataFrame:")
print(df)
print("nData types of the columns of the said DataFrame:")
print(df.dtypes)
print("nNow change the Data type of 'score' column from float to int:")

df.score = df.score.astype(int)
print(df)
print("nData types of the columns of the DataFrame now:")
print(df.dtypes)

Sample Output:

Original DataFrame:
   attempts       name qualify  score
0         1  Anastasia     yes  12.50
1         3       Dima      no   9.10
2         2  Katherine     yes  16.50
3         3      James      no  12.77
4         2      Emily      no   9.21
5         3    Michael     yes  20.22
6         1    Matthew     yes  14.50
7         1      Laura      no  11.34
8         2      Kevin      no   8.80
9         1      Jonas     yes  19.13

Data types of the columns of the said DataFrame:
attempts      int64
name         object
qualify      object
score       float64
dtype: object

Now change the Data type of 'score' column from float to int:
   attempts       name qualify  score
0         1  Anastasia     yes     12
1         3       Dima      no      9
2         2  Katherine     yes     16
3         3      James      no     12
4         2      Emily      no      9
5         3    Michael     yes     20
6         1    Matthew     yes     14
7         1      Laura      no     11
8         2      Kevin      no      8
9         1      Jonas     yes     19

Data types of the columns of the DataFrame now:
attempts     int64
name        object
qualify     object
score        int64
dtype: object

 

 

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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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