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YAML is more robust about embedding other serialization formats such as JSON or XML within a YAML file. YAML has the ability to reference other items within a YAML file using "anchors." Thus it can handle relational information as one might find in a MySQL database.

In fact the YAML homepage is itself valid YAML, yet it is easy for a human to read.

python parser to read array of strings yaml data example

First import yaml module using import statement Read the file using open method safe_load method read the file content and convert to dictionary pyhton object enclose file reading try and expect block to hand exceptions Let's see another example for reading array of yaml data.

The schema is passed to the Validator and validated with validate (). Cerberus works by defining a validation schema for data. It is designed to be easily extensible, allowing for custom validation.

Cerberus is a Python validation library which provides powerful yet simple and lightweight data validation functionality.The entrance point for yaml.schemas is location in user and workspace settings.

Your schema must be a relative path and not an absolute path. In other words, the schema (placed on the left) is applied to the glob pattern on the right. yaml.schemas applies a schema to a file.DriConfig provides a clean interface between your Python code and these YAML configuration files. The usage of YAML files to store configurations and parameters is widely accepted in the Python community, especially in Data Science environments.