Customizing a DataFrame

In your company, any data has to come with a timestamp recording when the dataset was created, to make sure that outdated information is not being used. You would like to use pandas DataFrames for processing data, but you would need to customize the class to allow for the use of timestamps.

In this exercise, you will implement a small LoggedDF class that inherits from a regular pandas DataFrame but has a created_at attribute storing the timestamp. You will then augment the standard to_csv() method to always include a column storing the creation date.

Tip: all DataFrame methods have many parameters, and it is not sustainable to copy all of them for each method you're customizing. The trick is to use variable-length arguments *args and **kwargsto catch all of them.

This exercise is part of the course

Object-Oriented Programming in Python

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import pandas as pd
____

# Define LoggedDF inherited from pd.DataFrame and add the constructor
____
    
    
ldf = LoggedDF({"col1": [1,2], "col2": [3,4]})
print(ldf.values)
print(ldf.created_at)

Edit and Run Code

This exercise is part of the course

Object-Oriented Programming in Python

AdvancedSkill Level

4.7+

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In this chapter, you'll learn what object-oriented programming (OOP) is, how it differs from procedural-programming, and how it can be applied. You'll then define your own classes, and learn how to create methods, attributes, and constructors.

Exercise 1: What is OOP?Exercise 2: OOP termininology Exercise 3: Exploring object interface Exercise 4: Class anatomy: attributes and methods Exercise 5: Understanding class definitions Exercise 6: Create your first class Exercise 7: Using attributes in class definition Exercise 8: Class anatomy: the __init__ constructor Exercise 9: Correct use of __init__Exercise 10: Add a class constructor Exercise 11: Write a class from scratch

Inheritance and polymorphism are the core concepts of OOP that enable efficient and consistent code reuse. Learn how to inherit from a class, customize and redefine methods, and review the differences between class-level data and instance-level data.

Exercise 1: Instance and class data Exercise 2: Class-level attributes Exercise 3: Changing class attributes Exercise 4: Alternative constructors Exercise 5: Class inheritance Exercise 6: Understanding inheritance Exercise 7: Create a subclass Exercise 8: Customizing functionality via inheritance Exercise 9: Method inheritance Exercise 10: Inheritance of class attributes Exercise 11: Customizing a DataFrame

Current Exercise

In this chapter, you'll learn how to make sure that objects that store the same data are considered equal, how to define and customize string representations of objects, and even how to create new error types. Through interactive exercises, you’ll learn how to further customize your classes to make them work more like standard Python data types.

Exercise 1: Operator overloading: comparison Exercise 2: Overloading equality Exercise 3: Checking class equality Exercise 4: Comparison and inheritance Exercise 5: Operator overloading: string representation Exercise 6: String formatting review Exercise 7: String representation of objects Exercise 8: Exceptions Exercise 9: Catching exceptions Exercise 10: Custom exceptions Exercise 11: Handling exception hierarchies

How do you design classes for inheritance? Does Python have private attributes? Is it possible to control attribute access? You'll find answers to these questions (and more) as you learn class design best practices.

Exercise 1: Designing for inheritance and polymorphism Exercise 2: Polymorphic methods Exercise 3: Square and rectangle Exercise 4: Managing data access: private attributes Exercise 5: Attribute naming conventions Exercise 6: Using internal attributes Exercise 7: Properties Exercise 8: What do properties do?Exercise 9: Create and set properties Exercise 10: Read-only properties Exercise 11: Congratulations!