Sanic and Data Validation using Pydantic - Deepstash
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  • pydantic class allows custom data types to be defined or we can extend validation with methods on a model decorated with the validator decorator
  • Here, The datatype of name, grade, roll, email, phone, subjects, friends must be as mentioned in pydantic class else it will raise an error (pydantic validation error)


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  • In Python “serialization” does nothing else than just convert the given data structure (e.g. a dict ) into its valid JSON object. JSON is a format that encodes objects in a string. Serialization means to convert an object into that string, and deserialization is its inverse operation (convert string -> object).
  • When transmitting a student in a file, the student is required to be byte strings, but Pydntic(here Student) objects are seldom in this format. Serialization can convert these Pydantic objects into byte strings for such use.
  • We need JSON Serializable objects to provide HTTP Response.


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  • We will request data in JSON format and pass the data to the Student class, Which will Return the Student object and store it in the student variable.
  • before inserting the student object into DB, we converted the Student object to a python dictionary and then inserted it into the database.
  • for the response that we just inserted, we again converted the Student object to a python dictionary.


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  • We pass id and mention that id_ is of type integer,
  • We will request data to be updated in JSON format and pass the data to the Student class, Which will Return the Student object and store it in the student variable.
  • before inserting student objects into DB, we check whether the provided id exists or not.
  • if id_ exists, we converted the Student object to python dictionary and then inserted it into the database and provide Success message.
  • else we provide the error message.


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  • We pass id and mention that id_ is of type integer,
  • we check whether or not id_ exists in the database, if exists, delete the data and provide a response that the data is deleted
  • else we provide the error message.

In this way we can validate our data, we can use custom validators in the pydantic model too, like if we require to accept the name that must contain space then you can specify a validator for that,


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Data validation using Pydantic in python

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