How to Define Required Fields in Pydantic

A required field is a field that must be present in the data model. These fields are essential and cannot be left blank, as they are needed to function properly or complete a process. If a required field is not given a value, Pydantic will raise a ValueError exception. Fields can be defined as required fields in a variety of ways. However, Pydantic sets all data model fields “required” by default. This article will demonstrate how to define required fields in Pydantic. We will also discuss some good tips for using the required fields.

Benefits of Using Required Fields

There are several benefits to using the required fields in Pydantic.

• Ensuring data completeness: Required fields help us ensure that our data models are complete and valid. This can help prevent errors in your code and data processing.
• Preventing malicious input: Required fields can also be used to prevent malicious users from injecting invalid data into your system.
• Making code more readable: You can improve the readability as well as the understanding of your code by using the required fields.

How to Define Required Fields in Pydantic

In Pydantic, there are three methods for defining a field as a required field. Let’s explore each of them in detail.

Using Annotations

The simplest way to define a required field in Pydantic is by using annotations. Using annotations, a type of syntactic metadata, you can add more details about variables and attributes of classes. In Pydantic, annotations are used to indicate the expected data type of a field, and by default, all annotated fields are considered required until you make a field or fields optional.

In this example, full_name, height, and email are all required fields. If you create an instance of the Person class without providing values for these fields, Pydantic will raise a ValidationError indicating that the required fields are missing.

In this example, the full_name field is missing, and the height field is missing as well. Both of these fields are required, and the ValidationError provides clear information about the missing fields.

Using Ellipsis (…)

Another way to declare a field as required in Pydantic is by using the ellipsis (…). This is an explicit approach provided by Pydantic, to mark a field as required.

In this example, the fields name, price, and description are all defined as required using the ellipsis. This method makes it explicit and visible that particular fields cannot be skipped when creating an instance of the Product class.

In this example, the price field is missing, and the ValidationError clearly indicates the missing required field.

Using the Field Function

The Field function from the Pydantic module provides additional capabilities for customizing field validation and metadata. You can use the Field function to declare required fields and apply additional validation rules.

Here’s how you can define required fields using the Field function:

In this example, we use the Field function to define required fields street, city, and zip_code, along with additional validation rules and descriptions. The ellipsis “…” indicates that these fields must be defined as required fields.

In this example, the city field is missing, and the ValidationError provides information about the missing required field.

Required fields can be validated using other Pydantic features, such as constraints and types. For example, you could specify that a name field must be a string of at least 5 characters. You can use the Field decorator to customize the behavior of required fields. For example, you could specify a default value for the field or a message to be displayed if the field is not given a value.

Using Multiple Methods to Define Required Fields in a Single Pydantic Model

You can use multiple methods of defining required fields within a single Pydantic model. For instance, you can use annotations for some fields, ellipsis (…) for others, and the Field function for additional customization. Pydantic allows you to choose the best approach for your code organization and readability preferences. Consider the following example:

In this example, all the fields are required to be used. We have used three different methods to define the required fields. The name field uses the annotation, the department uses the ellipsis, and salary uses the Field function.

Tips for Using Required Fields

Following some good practices when defining the required fields in Pydantic is essential to create smooth and maintainable code. The following tips will help you define the required fields in Pydantic:

1. Use Clear and Descriptive Field Names: Choose meaningful names for your fields that clearly indicate their purpose. This helps other developers know what data is needed and reduces the chances of missing required fields.
2. Provide Informative Field Descriptions: When using the Field function to define required fields, provide descriptive descriptions that explain the purpose and expected format of the data.
3. Group Related Fields: If your data model has a large number of fields, consider grouping related fields into nested structures. This can help your code be more readable and make managing required fields easier.
4. Use custom messages for required fields: By default, Pydantic will raise a ValueError exception if a required field is not given a value. You can customize the error message by specifying the message argument to the Field decorator.

Conclusion

Pydantic, by default, makes the fields as required. However, you can explicitly define the field as required fields. By declaring fields as required, you ensure that your data models are accurate, complete, and aligned with your requirements. In this post, we covered three distinct methods for defining the required fields in Pydantic, i.e., annotations, the ellipsis (…), and the Field function. Additionally, we looked at some recommended practices for using required fields so you can effectively specify fields in your data model.