Pydantic Tutorial • Solving Python's Biggest Problem

Summary

This tutorial introduces the Pydantic module in Python, highlighting its benefits for data validation and type-hinting, which address Python's dynamic typing issues. Pydantic is compared to dataclasses, showing its advantages in validation and JSON serialization. The tutorial covers creating Pydantic models, ensuring data validity, using custom validators, and converting models to/from JSON. It demonstrates Pydantic's practical use in improving code reliability and IDE support, especially for larger or collaborative projects.

Takeaways

• 🐍 Python lacks static typing, which can lead to issues in large applications and unclear function arguments.
• 🔄 Dynamic typing in Python allows for easy variable type changes, but this flexibility can cause bugs that are hard to debug.
• 👤 Pydantic is a Python library that helps with data modeling and validation, enhancing type hints and reducing runtime errors.
• 📦 Pydantic is used by popular Python modules like HuggingFace, FastAPI, and LangChain, highlighting its reliability and utility.
• 🖥️ Pydantic models provide better IDE support, including type hints and autocomplete, making development more efficient.
• 🔒 Data validation with Pydantic ensures that objects are created with valid data, preventing future failures in the application.
• 🌐 Pydantic simplifies JSON serialization, making it easy to convert models to and from JSON, which is crucial for web applications and data storage.
• 📚 Pydantic allows for custom validation logic, enabling developers to enforce specific data requirements in their models.
• 📈 Compared to dataclasses, Pydantic offers more robust features like deep JSON serialization and data validation, making it ideal for complex data models.
• 🛠️ While dataclasses are built into Python and lightweight, they lack the advanced features of Pydantic, making them suitable for simpler data needs.

Q & A

What is the main issue with Python's dynamic typing?

Python's dynamic typing allows variables to be created without declaring their type and can be overridden with different types. This can lead to problems as applications grow, making it difficult to track variable types and causing issues with function arguments where types are not obvious. It also allows the accidental creation of invalid objects.

Why can dynamic typing be problematic in larger applications?

In larger applications, dynamic typing can make it harder to keep track of all variables and their expected types. It can also complicate debugging when type-related errors occur, as these errors might not be immediately apparent and could happen at any point in the program.

What is Pydantic and what problem does it solve?

Pydantic is a data validation library in Python that helps model data and solve problems associated with dynamic typing. It provides better IDE support for type hints and autocomplete, ensures data validity upon object creation, and simplifies data serialization for formats like JSON.

How does Pydantic improve IDE support?

Pydantic enhances IDE support by providing type hints and autocomplete features. This helps developers quickly understand the expected types of variables and functions, making it easier to work with the code.

What are the benefits of using Pydantic for data validation?

Pydantic ensures that data is validated when objects are created, reducing the risk of runtime errors due to invalid data. It also allows for the validation of complex data types, such as ensuring that an email string is valid.

How can Pydantic be used to serialize data to JSON?

Pydantic provides built-in support for JSON serialization. To convert a Pydantic model to JSON, you can call the JSON method on the model instance, which returns a JSON string representation of the model's data. Alternatively, the dict method can be used to obtain a plain Python dictionary.

How does Pydantic handle custom validation logic?

Pydantic allows adding custom validation logic through the use of a validator decorator. Developers can define a class function that checks for specific conditions and raises a ValueError if the conditions are not met.

What is the difference between Pydantic and Python's built-in dataclasses?

While both Pydantic and dataclasses provide type hints, Pydantic offers additional features like data validation and deep JSON serialization. Dataclasses are built into Python and are more lightweight, but they lack the robust validation and serialization capabilities of Pydantic.

Why might someone choose Pydantic over dataclasses?

Developers might choose Pydantic over dataclasses if they need complex data models, require extensive data validation, or need to work with a lot of external APIs and JSON serialization.

How can Pydantic be used to ensure that a field like 'account ID' is always positive?

Pydantic can enforce field constraints like a positive account ID through the use of a validator decorator. Developers can define a custom function that checks if the value is greater than zero and raises a ValueError if it is not.

What is the recommended approach for choosing between Pydantic and dataclasses?

The choice between Pydantic and dataclasses depends on the project's needs. Pydantic is recommended for complex data models and extensive JSON serialization, while dataclasses might suffice for simpler data models where data validation is not a priority.

Outlines

00:00

🐍 Introduction to Pydantic for Python Data Modeling

This paragraph introduces the Pydantic module in Python, highlighting the challenges of Python's dynamic typing system. It contrasts Python's flexibility with the need for type declarations in languages like Java or C. The speaker explains how dynamic typing can lead to issues in larger applications, such as difficulty in tracking variable types and potential runtime errors due to invalid object creation. The paragraph emphasizes the benefits of Pydantic, a data validation library, which provides tools for modeling data, enhancing IDE support, validating data integrity, and simplifying JSON serialization. The speaker also mentions notable Python modules that use Pydantic, such as HuggingFace, FastAPI, and LangChain.

05:01

🔒 Pydantic's Data Validation and Serialization Features

This paragraph delves into Pydantic's data validation capabilities, explaining how it ensures data integrity by failing early when incorrect data types are used. The speaker demonstrates how Pydantic can validate simple data types and more complex structures, such as email addresses. It also covers how to implement custom validation logic using Pydantic's validator decorator. Additionally, the paragraph discusses Pydantic's support for JSON serialization, showing how to convert Pydantic models to and from JSON, which is crucial for integrating Python applications with external systems. The speaker compares Pydantic with Python's built-in dataclasses, noting that while both provide type hints, Pydantic offers more robust validation and JSON serialization features.

10:03

📚 Choosing Between Pydantic and Dataclasses

In this concluding paragraph, the speaker provides guidance on when to use Pydantic versus dataclasses in Python. It emphasizes that while dataclasses are lightweight and built into Python, they lack the advanced validation and JSON serialization capabilities of Pydantic. The speaker recommends Pydantic for complex data models, extensive JSON serialization needs, or when working with external APIs. Conversely, for simpler data models where data validation is not a priority, dataclasses may suffice. The paragraph ends with an invitation for viewers to try Pydantic and provide feedback, and encourages subscriptions and comments for future video topics.

Keywords

💡Pydantic

Pydantic is a data validation library in Python used for modeling data and solving problems related to dynamic typing. It provides tools for better IDE support, data validation, and easy serialization. In the video, Pydantic is introduced as a solution to the issues caused by Python's dynamic typing, such as type mismatches and runtime errors. It is highlighted as being used by notable Python modules like HuggingFace, FastAPI, and LangChain.

💡Dynamic Typing

Dynamic typing is a feature of Python where variables do not need to have their types declared upfront. This means you can assign values of different types to the same variable. The video discusses how dynamic typing, while making Python easier to start with, can lead to problems in larger applications where tracking variable types becomes difficult and can cause runtime errors.

💡Type Hinting

Type hinting in Python is a way to specify the expected data types of variables, function parameters, and return values. It helps in improving code readability and IDE support. The video mentions type hinting as a tool that Python has to address some of the issues with dynamic typing, but it does not provide the robust validation and serialization that Pydantic offers.

💡Data Validation

Data validation is the process of checking that the data meets certain criteria or constraints. Pydantic provides this functionality out of the box, ensuring that objects are created with valid data. In the video, data validation is shown as a key benefit of Pydantic, allowing developers to catch errors early in the development process.

💡Serialization

Serialization is the process of converting data structures into a format that can be easily stored or transmitted, such as JSON. Pydantic provides built-in support for JSON serialization, making it easy to convert model instances to and from JSON. This is highlighted in the video as a useful feature for integrating with external applications or APIs.

💡Base Model Class

In Pydantic, a base model class is the parent class from which all Pydantic models inherit. It provides the foundational functionality for defining model fields and performing data validation. The video script shows how to define a Pydantic model by inheriting from this base class.

💡IDE Support

IDE (Integrated Development Environment) support refers to the features provided by development tools to assist in coding, such as autocompletion and type hints. The video emphasizes that Pydantic models provide better IDE support, making it easier for developers to write and maintain code by providing autocomplete suggestions based on the model's fields.

💡Dataclasses

Dataclasses is a built-in Python module that allows for the creation of classes with fields. It is compared to Pydantic in the video, highlighting that while dataclasses provide some type hinting and are lightweight, they lack the robust data validation and JSON serialization capabilities that Pydantic offers.

💡JSON

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write, and for machines to parse and generate. The video discusses how Pydantic models can be easily converted to and from JSON, which is crucial for interoperability with web services and data storage.

💡Validation Error

A validation error occurs when data does not meet the specified criteria during the validation process. In the context of the video, Pydantic raises validation errors when an object is attempted to be created with incorrect data types, helping to catch mistakes early and prevent runtime failures.

💡Custom Validation Logic

Custom validation logic refers to user-defined rules for validating data beyond the standard checks provided by Pydantic. The video demonstrates how to add custom validation to a Pydantic model, such as ensuring that an account ID is a positive number, by using a validator decorator and a custom function.

Highlights

Introduction to using the Pydantic module in Python to address the lack of static typing in the language.

Python's dynamic typing allows variables to be overridden with different types, which can cause issues in larger applications.

Static typing in languages like Java or C requires upfront type declaration, unlike Python.

Dynamic typing can lead to accidental creation of invalid objects with values they shouldn't have.

Pydantic is a data validation library used by top Python modules like HuggingFace, FastAPI, and LangChain.

Pydantic provides better IDE support for type-hints and autocomplete, and ensures data validity.

Pydantic enables easy serialization of objects to JSON, facilitating communication with other apps or saving data to disk.

Installation of Pydantic is required to use it in a Python environment.

Creating a Pydantic model involves defining a class that inherits from the base model class and specifying fields as class variables.

Instances of Pydantic models can be created by passing data as keyword arguments or unpacking a dictionary.

IDE type hints in Pydantic models improve developer experience by providing autocomplete and suggestions.

Pydantic models provide data validation, failing immediately if incorrect data types are used.

Custom validation logic can be added to Pydantic models using the validator decorator.

Pydantic supports complex data validation, such as ensuring strings are valid emails.

JSON serialization is built-in with Pydantic, allowing easy conversion of models to and from JSON.

Comparison between Pydantic and Python's built-in dataclasses, highlighting Pydantic's advantages in validation and JSON serialization.

Recommendation to use Pydantic for complex data models or when working with external APIs, and dataclasses for simpler needs.

Transcripts

Welcome to this video tutorial where I'm going to show

you how to use the Pydantic module in Python.

One of the biggest issues with Python as a programming

language is the lack of static typing.

Python uses dynamic typing, which means that

when you create a variable, you don't have to

declare its type, like this x for example.

Compare this to something like Java or C where

you actually have to declare the type upfront.

Once a Python variable is created, you can also override

it with a different type than what you created it with.

So here if I create x = 10, in the next line I can

override that with the word "hello" as a string.

And Python allows you to do this. This does

make it easier to get started with Python,

but it can cause a lot of problems later on.

For example, as your app gets bigger, it becomes

harder and harder to keep track of all your

variables and what type they should be.

It's also difficult when you have to work with functions

where the argument types aren't obvious.

For example, what is this "rect" argument supposed to be here?

It could be a tuple, but then it doesn't tell you

if the x-axis or the y-axis should come first.

But the biggest downside of using dynamic types by far is that

it allows you to accidentally create an invalid object.

By that, I mean an object with values that it shouldn't be allowed to have.

For example, here I'm trying to create a person

and the second argument is supposed to

be age, so it's supposed to be a number.

In the first example, I created correctly with 24 as an integer,

but in the second example, I created with 24 as a string.

And both of them might work at the beginning. Python will allow

you to do this and things can actually seem fine for a while.

But eventually, when you do try to use that age variable as a number, it will fail.

This can be really hard to debug because the failure

could occur at any time in your program.

And it could be hard to associate that failure with the actual cause.

Luckily, these days, Python has a lot of tools you can use to solve these problems.

This includes dataclasses and type-hinting, like in this code example here.

But today, we're going to be taking a look at Pydantic.

It's an external library and it gives you powerful

tools to model your data and solve all of these

problems that we've just been talking about.

Pydantic is a data validation library in Python.

It's used by some of the top Python modules out there,

notably HuggingFace, FastAPI, and LangChain.

Its main benefits are that by modeling your data, you get

better IDE support for type-hints and autocomplete.

You can also validate your data so that when

you create an object, you can be 100% sure that

it's valid and it won't fail you later.

And finally, if you ever need your data to be

in a universal format like JSON, Pydantic gives

you an easy way to serialize your objects.

This really comes in handy if you need your Python

app to talk to other apps on the internet,

or if you just want to save your data to disk.

Let's take a look at how all of that works.

First, make sure that you've installed Pydantic into your

Python environment. You can do it using this command.

To create a Pydantic model, first define a class

that inherits from the base model class.

Inside the class, define the fields of the model as class variables.

In this example, I'm creating a user model and

it's got three fields, a name, which is

a string, an email, also a string, and an account

ID, which is going to be an integer.

You can create an instance of the model like this and

then just pass in the data as keyword arguments.

You can also do this by unpacking a dictionary.

So this works well if you already have the data

and you just want to put it inside the model.

For example, you have a response from an external API.

If the data that you've passed in is valid, then

this user object will be successfully created.

You can then access each of the attributes of the user object like this.

I'm going to head over to my IDE so I can show you how this works in action.

I have my user model defined here and I think by

far the most useful feature of modeling your

data is that you get type hints in your IDE.

So what I mean is if I start typing out my user, I get

autocomplete and auto suggestions based on this model.

So here I've created this user object and I haven't

filled in the data yet, but if I mouse over it,

it actually tells me which arguments it accepts.

And here I can fill it in with the examples you saw earlier,

so a valid name, a valid email, and an account ID.

And now if I print the user, you can see that all of

this information is contained in this one object.

And of course the type hinting makes it easier to work with

when you actually need to use one of these models.

So for example here, if I'm printing the user,

I can just press a dot and then I get a list of

all the valid variables associated with it.

So for example, if I wanted email, I just start typing

and it knows that this user has an email attribute.

With type hints, your code becomes much easier to work with

because you don't have to remember everything yourself.

Your IDE does it for you, and this is especially useful

if you're working with really large code bases

or if you need to collaborate with other developers.

Pydantic also provides data validation right out of the box.

This means that if you try to create an object with the

wrong type of data, it will fail right then and there.

This is good because if your software has to fail, then

it's better that it fails as early as possible.

This will make it easier to debug.

So let's go back to our example here and see how that works.

If I try to create this user with an account ID that's

not an integer, for example if I turn it into a string

and I try to run it, I now get a validation error.

So I can see immediately that I tried to create

this object with the wrong type of data.

And in cases like these, I much rather it fail

right away with the descriptive error

message than silently succeed, but then fail

at some point much later down the line.

You can also validate more complex types of data.

For example, let's say I wanted to validate

that this string is actually a valid email.

First, let's change it to an invalid email, for example just Jack on its own.

So this is no longer an email, and if I run this it still

works because all this checks for is that it's a string.

But I can actually import a special data type called email string from Pydantic.

And if I replace this instead and run this again,

you'll now see that I get this validation error

and that this string here is not a valid email.

So let me change this back to a valid email again and see if that works.

And after fixing this value, the validation passes.

So I have an easy way to assert that this email

field always has a valid email string.

If none of the inbuilt validation types cover your needs,

you can also add custom validation logic to your model.

For example, let's say that we want to enforce that

all account IDs must be a positive number.

So we don't accept negative integers for our account ID.

This is what we can add to our class to make that happen.

First, we'll have to use this validator decorator from Pydantic.

And then we write a custom function.

This is going to be a class function.

And then inside the function, we can check if

the value is less than or equal to zero.

And if it is, we can raise a value error saying

that this is not a valid value for this field.

But if it is, we can return the value.

So let's go back to our code editor and try that out.

And here I've imported this validator decorator.

And this is the validation logic I'm adding as a class function of this user model.

And here you can change this validation condition

to whatever you want it to be for your app.

But in this case, I'm just checking that it's greater than zero.

So if I run this with my current data, it should still work.

And here you can see that it's fine.

But if I change this to a negative number, let's see what happens.

Now it fails with that validation error and

it says the account ID must be positive.

And here we can actually make the error message

really descriptive because we can print anything

we want here. And we can even print the value

that the user tried to create this model with.

Another great thing about Pydantic is that it provides

built-in support for JSON serialization.

Makes it really easy to convert Pydantic models to or from JSON.

To convert a Pydantic model to JSON, you can

call the JSON method on the model instance.

This will return a JSON string representation of the model's data.

So if you print it out, you'll see something like this.

And if you don't want a JSON string, but you

just want a plain Python dictionary object

instead, you can use this dict method.

If you have a JSON string that you want to convert back into

a Pydantic model, you can use the parse_raw method.

And since JSON is widely used and understood

across every major tech stack, this feature

will make it really easy to integrate your Python

code with external applications or APIs.

Finally, let's see how Pydantic compares to dataclasses, which

is Python's built-in module that solves a similar problem.

As great as Pydantic sounds, Python actually does ship with some

data modeling and type hinting capabilities on its own.

For example, you can already specify type hints

like this, and most IDEs should pick it up.

There's also an inbuilt module called "dataclass" in

Python that lets you create a class with fields.

So if you haven't used it before, this is what the syntax looks like.

It's very similar to Pydantic, except instead

of extending from a base model class, you're

using this "@dataclass" decorator instead.

As you can see, it's also really easy to use.

So how does this compare to Pydantic? Well, let's

take a look at some of the top criteria.

They actually both give you type hints in the IDE, which personally

is the biggest reason for using these libraries to me.

So both of them tick that box.

Dataclasses, however, does not give you any easy validation

or deep JSON serialization out of the box.

Now, if validation is a big deal for you, for example,

you have a lot of emails or you have a lot

of fields where the data type is very specific,

then you probably should go with Pydantic.

If you're using dataclass, then your JSON serialization

capability isn't as good out of the box as Pydantic.

But if your data is simple enough, you can still do some

basic serialization with a one-liner like this.

The one major advantage that dataclasses have over Pydantic

is that they're in-built into Python directly.

That means that it's more lightweight and you don't even have to install it.

For many users, this may be enough.

If you want some rough guidance as to which

module you should use, then I recommend Pydantic

if you have complex data models or you

need to do a lot of JSON serialization or

you need to work with a lot of external APIs.

But if data validation isn't important to you and your data

isn't super complex, you can get away with dataclasses.

And that's it for Pydantic.

If you haven't used it yet, then give it a try and let me know what you think.

If you've enjoyed this video and want to see more tutorials

like this, then please subscribe to the channel

and let me know in the comments what type of

topics or modules you'd like to see covered next.

Otherwise, I hope you found this useful and thank you for watching.