Skip to main content

pydantic-yaml-parser

Install

pip install pydantic_yaml_parser

Background & Usage

1. Loading YAML into Pydantic models

To load YAML into a Pydantic model, you have to first convert the YAML string to a dict. For example, given the below yaml file:

yaml_string="""
setting_1: Hello
setting_2:
    - kind: name
      sublist:
          - first
          - second
"""

You might define a Pydantic model to represent it like this:

from pydantic import BaseModel
from typing import List

class Setting2(BaseModel):
    kind: str
    sublist: List

class Test(BaseModel):
    setting_1: str
    setting_2: List[Setting2]

You can load the yaml file into a dict, and into the Pydantic model like so using pyyaml:

import yaml
yml_dict = yaml.safe_load(yaml_string)

# use `parse_obj` to load a dict
Test.parse_obj(yml_dict)
Test(setting_1='Hello', setting_2=[Setting2(kind='name', sublist=['first', 'second'])])

2. The Pydantic validation error message

However, let’s say there is an error in your yaml file such that you accidentally set sublist to false, instead of setting sublist to list type, you will get an error message that looks like this:

yaml_string="""
setting_1: ok
setting_2:
    - kind: name
      sublist: false
"""
yaml_dict_error = yaml.safe_load(yaml_string)
Test.parse_obj(yaml_dict_error)
ValidationError: 1 validation error for Test
setting_2 -> 0 -> sublist
  value is not a valid list (type=type_error.list)

This error message is a bit confusing, especially for those with no prior experience with pydantic!

3. Human readable error messages with pydantic_yaml_parser

When you use pydantic_yaml_parser you get an error message that is much clearer:

from pydantic_yaml_parser.yaml import YamlModel

class Test(YamlModel):
    setting_1: str
    setting_2: List[Setting2]
Test.from_dict(yaml_dict_error)
ValueError: Configuration error(s) for YAML:
 - value is not a valid list: `sublist` for element 0 in the list for `setting_2`

Further Reading

For more examples of error validation see these docs.

Metadata

Release files for pydantic-yaml-parser 0.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pydantic-yaml-parser 0.0.4
File Size Uploaded Provenance
pydantic-yaml-parser-0.0.4.tar.gz 8.8 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for pydantic-yaml-parser 0.0.4
File Interpreter ABI Platform
pydantic_yaml_parser-0.0.4-py3-none-any.whl Python 3 none any Details

Total release size: 17.3 kB

Release files / pydantic-yaml-parser-0.0.4.tar.gz

Download URL pydantic-yaml-parser-0.0.4.tar.gz
Size 8.8 kB
Tags Source
SHA-256 checksum
How to use checksums
1739c1850d782022a423f93585cf68f7ed1725fd26e516a549e7c4ea7f9476b8
BLAKE2b-256 checksum
How to use checksums
8a26ce6824701a4eab87bfafd20abac45823bfcce578080b8284f9a407da41ec
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.12

Release files / pydantic_yaml_parser-0.0.4-py3-none-any.whl

Download URL pydantic_yaml_parser-0.0.4-py3-none-any.whl
Size 8.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
fdea6be667ad6532c66c87bd186d972b56fd067b103596daaa27643f89be8aee
BLAKE2b-256 checksum
How to use checksums
ffe9ae27295af79cc054c01cd242ce8f1f0fa6b07645983caa2ac83a4411a3d9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.12

Security for pydantic-yaml-parser 0.0.4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page