Adventures in Machine Learning

Creating Python Class Instances from Dictionaries: Techniques and Tips

Creating Class Instances from a Dictionary in Python

Python is a powerful and versatile programming language that allows developers to create class instances using dictionaries. This technique is particularly useful in cases where a large amount of data needs to be organized into a single object for easier processing.

In this article, we will explore various ways to create class instances from dictionaries.

Using __init__() Method to Set Dictionary as Instance Attributes

Python’s __init__() method is used to initialize an object’s attributes when it is created. We can leverage this method to set up instance attributes using values from a dictionary.

A common way to achieve this is by passing the dictionary as an argument to the __init__() method. Here is an example below:


class Dog:

def __init__(self, dog_dict): = dog_dict[‘name’]

self.age = dog_dict[‘age’]

self.breed = dog_dict[‘breed’]

dog_data = {‘name’: ‘Fido’, ‘age’: 3, ‘breed’: ‘Labrador’}

fido = Dog(dog_data)


In this example, we created a Dog class that takes a dog dictionary as an argument.

The dictionary keys match the instance attribute names, which we set up using __init__() method.

Iterating Over Dictionary Items to Assign Instance Attributes

Rather than hard-coding each instance attribute, we can use a for loop to iterate over the dictionary and assign the corresponding key-value pairs as instance attributes. This approach is especially helpful when dealing with large or dynamic datasets.

Here is an example of how to achieve this:


class Car:

def __init__(self, car_dict):

for key, value in car_dict.items():

setattr(self, key, value)

car_data = {‘make’: ‘Toyota’, ‘model’: ‘Camry’, ‘year’: 2012}

toyota = Car(car_data)


In this example, we created a Car class that takes a dictionary as an argument. Using a for loop and setattr() function, we loop through each item in the dictionary and assign it to the corresponding instance attribute.

Taking Keyword Arguments when Creating an Instance

Python allows us to take keyword arguments when invoking a function, including __init__() method. This allows us to pass key-value pairs without necessarily defining them in a dictionary separate from the function call.

Below is an example:


class Book:

def __init__(self, name, author, year=None): = name = author

self.year = year

book1 = Book(name=’The Great Gatsby’, author=’F. Scott Fitzgerald’)

book2 = Book(name=’The Catcher in the Rye’, author=’J.D. Salinger’, year=1951)


In the above example, the Book class takes three arguments: name, author, and year.

Here, we use keyword arguments to create two instances of the Book class. The first instance does not have a year attribute; hence Python sets it to None.

Replacing Spaces in Dictionary Keys with Underscores

Dictionary keys cannot contain spaces. However, it’s common to have spaces in keys while importing data from a database, CSV files, or JSONs. In such cases, we can transform the spaces into underscores using a string method called replace().

We can put this method to use when creating instances using dictionaries with keys that have spaces. See an example below:


class Person:

def __init__(self, person_dict):

for key, value in person_dict.items():

setattr(self, key.replace(‘ ‘, ‘_’), value)

person_data = {‘first name’: ‘John’, ‘last name’: ‘Doe’, ‘age’: 30}

person1 = Person(person_data)


In this example, we customized the Person class’s __init__() method to include the string method replace().

This replaces spaces with underscores when we assign key-value pairs as instance attributes.

Initializing Attributes to None

In some cases, it’s useful in initializing instance attributes to None. When using dictionaries to create class instances, certain keys may not have corresponding values.

In such a scenario, initializing these attributes to None can prevent issues during data wrangling. Let us explore an example:


class Plant:

def __init__(self, plant_dict): = plant_dict[‘name’]

self.species = plant_dict.get(‘species’)

self.color = plant_dict.get(‘color’)

plant_data = {‘name’: ‘Rose’, ‘color’: ‘red’}

rose = Plant(plant_data)


In this example, we created a Plant class that takes a dictionary as an argument.

We use the get() method instead of direct key access to set the species and color instance attributes. Since ‘species’ key isn’t defined in plant_data, Python sets the instance attribute to None.

Creating Class Instances from a Dictionary using Unpacking

In Python, we can use the ** operator to unpack a dictionary and pass it as keyword arguments. When creating an instance from a dictionary, using unpacking with the ** operator eliminates the need for a for-loop.

Here is an example:


class Contact:

def __init__(self, name, email, phone): = name = email = phone

contact_data = {‘name’: ‘John Doe’, ’email’: ‘[email protected]’, ‘phone’: ‘555-1234’}

contact = Contact(**contact_data)


In this example, we unpacked the contact_data dictionary using the ** operator to create an instance of the Contact class. The values in the dictionary correspond directly to the instance attribute names, which are passed as keyword arguments with the ** operator.

Additional Resources

The following resources might be helpful when creating class instances from dictionaries in Python:

– The official Python documentation:

– GeeksForGeeks article on initializing instance variables:

– A blog post on creating class instances from dictionaries:

– StackOverflow post on unpacking arguments in Python:

In this article, we explored various ways to create class instances from dictionaries in Python. We learned how to set dictionary as instance attributes using __init__() method and how to iterate over dictionary items to assign instance attributes.

We also discussed how to take keyword arguments when creating an instance and how to replace spaces in dictionary keys with underscores. Additionally, we looked at initializing attributes to None and creating class instances from a dictionary using unpacking.

Using these techniques can help developers handle large datasets and organize data into objects for more accessible processing. As a final thought, these tools are essential for working with complex data, and understanding them can make developers more efficient and productive.