Why Python's Protocol Classes help you to write better code for your Data Science projects

 Python is a powerful and versatile language that offers a wide range of tools for developers to build complex applications. One such tool is the use of Protocol Classes, which enable developers to define a set of rules that other classes must follow. They are somewhat similar to ABC classes, we talked about in the blog post from the previous week.

 In this post, we'll explore what Protocol Classes are, how they can be used, and why they are essential in building scalable and maintainable Python applications. We'll provide an extensive explanation of the use of Protocol Classes in Python and provide an elaborate code example to demonstrate their application. By the end of this post, you'll have a better understanding of how to use Protocol Classes in Python and how they can help you build better and more reliable code.

What is a Protocol Class and how does it work?

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 In Python, a Protocol Class is a way to define an interface or a set of rules that an object or a class must follow. It allows developers to define a structure that other classes can use to ensure they implement the necessary methods and attributes. Protocol Classes help in creating more maintainable, scalable, and error-free code.

 Python supports Protocol Classes as part of the typing module, which provides a way to define type hints for objects and functions. A Protocol Class can define a set of attributes and methods that must be implemented by any class that conforms to the protocol.

 The key benefit of using Protocol Classes is that they enable type checking at runtime, which can help catch errors early on in the development process. For instance, if you have a function that expects an object of a certain type and the passed object doesn't conform to the expected protocol, an error will be raised at runtime.

 Here's an example of a Protocol Class:

from typing import Protocol

class JsonSerializable(Protocol):
    def to_json(self) -> str:
        pass
    
    @staticmethod
    def from_json(json_string: str) -> 'JsonSerializable':
        pass

 In the example above, we define a Protocol Class called JsonSerializable. This protocol class requires that any class implementing it must have two methods - to_json and from_json. The to_json method should return a JSON string, while the from_json method should take a JSON string as input and return an object that conforms to the JsonSerializable protocol.

 Here's an example of a class that conforms to the JsonSerializable protocol:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
        
    def to_json(self):
        return f'{{"name": "{self.name}", "age": {self.age}}}'
    
    @staticmethod
    def from_json(json_string):
        data = json.loads(json_string)
        return Person(data['name'], data['age'])

 In the example above, we define a class called Person that implements the JsonSerializable protocol. The to_json method returns a JSON string representing the object's state, while the from_json method takes a JSON string as input and returns a new "Person" object created from the JSON data.

 Now, let's see an example of how to use the JsonSerializable protocol in a function:

def save_to_file(data: JsonSerializable, filename: str):
    with open(filename, 'w') as f:
        f.write(data.to_json())

 In the example above, we define a function called save_to_file that takes an object that conforms to the JsonSerializable protocol and a filename as input. The function saves the object's state to a file by calling the to_json method of the object.

 If we try to pass an object that doesn't conform to the JsonSerializable protocol, we'll get a runtime error. This helps us catch errors early on in the development process and ensures that the code is more maintainable and scalable.

 In conclusion, using Protocol Classes in Python can help developers define a set of rules that other classes must follow, enabling type checking at runtime, and catch errors early on in the development process. It can lead to more maintainable, scalable, and error-free code.

Why should and how can I use Protocol Classes in Data Science projects?

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 Protocol Classes are also a useful concept in Data Science projects, where they can help enforce a set of rules for data and models. Data Science projects often involve processing and manipulating large amounts of data, building models, and deploying them to production environments. In such scenarios, having clear interfaces and rules can help ensure that the data is handled correctly, and the models are built to the correct specifications.

 One common application of Protocol Classes in Data Science is defining a protocol for input data. When building machine learning models, it's essential to ensure that the input data is in the right format and has the necessary fields. By defining a Protocol Class for the input data, developers can ensure that the data is formatted correctly and can catch errors early on in the development process.

 Here's an example of a Protocol Class for input data in a machine learning project:

from typing import Protocol, List

class InputData(Protocol):
    data: List[List[float]]
    labels: List[int]

 In the example above, we define a Protocol Class called InputData that has two attributes: data and labels. The data attribute is a list of lists containing the input data for the model, and the labels attribute is a list of integers representing the class labels for each data point.

 Here's an example of a machine learning model that uses the InputData Protocol Class:

class MyModel:
    def __init__(self, input_data: InputData):
        self.input_data = input_data
    
    def train(self):
        # Train the model using self.input_data
        pass
    
    def predict(self, new_data: List[List[float]]) -> List[int]:
        # Make predictions on new_data
        pass

 In the example above, we define a machine learning model called MyModel that takes an object of type InputData as input. The model's train method uses the input data to train the model, while the predict method takes new data as input and returns a list of predicted class labels.

 By defining the InputData Protocol Class, we can ensure that the input data is formatted correctly and has the necessary fields. This can help catch errors early on in the development process and ensure that the model is built to the correct specifications.

 In conclusion, Protocol Classes are a useful concept in Data Science projects, where they can help enforce a set of rules for data and models. By defining clear interfaces and rules, developers can ensure that the data is handled correctly, and the models are built to the correct specifications. The use of Protocol Classes can help catch errors early on in the development process and ensure that the code is more maintainable, scalable, and error-free.

Give Protocol Classes a try in your next project!

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 That was it about Protocol Classes! I highly recommend you to try them out in your next project because their benefits are clear: they are a useful tool that can help you to ensure that classes conform to a set of rules and enable type checking at runtime. This, in turn, ensures that the code is more maintainable, scalable, and error-free. Protocol Classes provide a powerful mechanism for building interfaces in Python and should be an essential tool in every developer's and data scientist's toolkit. By using Protocol Classes, you can ensure that your code is more reliable, easier to maintain, and easier to scale. Hopefully, this post has provided you with a solid basic understanding of how to use Protocol Classes and how they can benefit your projects.

I hope to see you in the next post!

Additional resources

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For a comparison and when-to-use-what video between ABC or Abstract Base Classes and Protocol, check out this YouTube video of ArjanCodes: