Understanding and Implementing Schemas in Python

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Understanding and Implementing Schemas in Python Introduction In the world of programming, particularly in the context of data management and validation, schemas play a vital role. A schema is essentially a blueprint or a predefined structure that defines the expected format, data types, and constraints for a given data entity. In this blog, we will delve into the concept of schemas in Python, exploring what they are, why they are important, and how you can implement them in your projects. What is a Schema? A schema serves as a contract between different components of a system, ensuring that data is consistent, valid, and well-structured. It defines the rules for how data should be organized, what fields it should contain, and what types of values those fields can hold. In essence, a schema acts as a set of rules that data must adhere to in order to be considered valid. Why Are Schemas Important? Data Validation: Schemas provide a way to validate incoming data. When data is received o...
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Difference between filter() and filter_by() method in flask, python and SQLAlchemy

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Difference between filter() and filter_by() method in flask, python and SQLAlchemy 

 

In Flask using SQLAlchemy, you can use either filter() or filter_by() method to apply filters on queries to retrieve data from the database.

filter() method is used to apply complex filters on queries. It takes SQLAlchemy expressions as arguments that describe the filters you want to apply. For example, you can use it to filter users whose name contains "John" and email contains "gmail.com" as follows:

from myapp.models import User users = User.query.filter(User.name.like('%John%'), User.email.like('%gmail.com%')).all()

In this example, User.query returns a query object that can be further refined using the filter() method. We pass the two filter expressions as arguments to the filter() method, joined by an AND operator. The like() method is used to match patterns in the values of the name and email columns.

On the other hand, filter_by() method is used to apply simple filters on queries. It takes keyword arguments that describe the filters you want to apply. For example, you can use it to filter users whose name is "John" as follows:

from myapp.models import User users = User.query.filter_by(name='John').all()

In this example, User.query returns a query object that can be further refined using the filter_by() method. We pass the name='John' filter expression as a keyword argument to the filter_by() method.

Both filter() and filter_by() methods return a query object that can be further refined using other query methods or executed to retrieve the data.


Happy Learning!! Happy Coding!!

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