Filters

The Filters library provides an easy and readable way to create complex data validation and processing pipelines, including:

• Validating complex JSON structures in API requests or config files.
• Parsing timestamps and converting to UTC.
• Converting Unicode strings to NFC, normalizing line endings and removing unprintable characters.
• Decoding Base64, including URL-safe variants.

And much more!

The output from one filter can be piped into the input of another, enabling you to chain filters together to quickly and easily create complex data schemas and pipelines.

Examples

Validate a latitude position and round to manageable precision:

(
    f.Required |
    f.Decimal |
    f.Min(Decimal(-90)) |
    f.Max(Decimal(90)) |
    f.Round(to_nearest="0.000001")
).apply("-12.0431842")

Parse an incoming value as a datetime, convert to UTC and strip tzinfo:

f.Datetime(naive=True).apply("2015-04-08T15:11:22-05:00")

Convert every value in an iterable (e.g., list) to unicode and strip leading/trailing whitespace. This also applies Unicode normalization, strips unprintable characters and normalizes line endings automatically.

f.FilterRepeater(f.Unicode | f.Strip).apply([
    b"\xe2\x99\xaa ",
    b"\xe2\x94\x8f(\xc2\xb0.\xc2\xb0)\xe2\x94\x9b ",
    b"\xe2\x94\x97(\xc2\xb0.\xc2\xb0)\xe2\x94\x93 ",
    b"\xe2\x99\xaa ",
])

Parse a JSON string and check that it has correct structure:

(
    f.JsonDecode |
    f.FilterMapper(
        {
            "birthday":  f.Date,
            "gender":    f.CaseFold | f.Choice(choices={"f", "m", "n"}),

            "utcOffset":
                f.Decimal |
                f.Min(Decimal("-15")) |
                f.Max(Decimal("+15")) |
                f.Round(to_nearest="0.25"),
        },

        allow_extra_keys   = False,
        allow_missing_keys = False,
    )
).apply('{"birthday":"1879-03-14", "gender":"M", "utcOffset":"1"}')

Requirements

Filters is known to be compatible with the following Python versions:

• 3.13
• 3.12
• 3.11

Note

I'm only one person, so to keep from getting overwhelmed, I'm only committing to supporting the 3 most recent versions of Python.

Installation

Install the latest stable version via pip:

pip install phx-filters

Important

Make sure to install phx-filters, not filters. I created the latter at a previous job years ago, and after I left they never touched that project again and stopped responding to my emails — so in the end I had to fork it 🤷

Extensions

The following extensions are available:

• Django Filters: Adds filters designed to work with Django applications. To install:

  pip install phx-filters[django]
  

• ISO Filters: Adds filters for interpreting standard codes and identifiers. To install:

  pip install phx-filters[iso]
  

Tip

To install multiple extensions, separate them with commas, e.g.:

pip install phx-filters[django,iso]

Maintainers

To install the distribution for local development, some additional setup is required:

1. Install poetry (only needs to be done once).

2. Run the following command to install additional dependencies:

   poetry install --with=dev
   

3. Activate pre-commit hook:

   poetry run autohooks activate --mode=poetry
   

Running Unit Tests and Type Checker

Run the tests for all supported versions of Python using tox:

poetry run tox -p

Note

The first time this runs, it will take awhile, as mypy needs to build up its cache. Subsequent runs should be much faster.

If you just want to run unit tests in the current virtualenv (using pytest):

poetry run pytest

If you just want to run type checking in the current virtualenv (using mypy):

poetry run mypy src test

Documentation

To build the documentation locally:

1. Switch to the docs directory:

   cd docs
   

2. Build the documentation:

   make html
   

Releases

Steps to build releases are based on Packaging Python Projects Tutorial.

Important

Make sure to build releases off of the main branch, and check that all changes from develop have been merged before creating the release!

1. Build the Project

1. Delete artefacts from previous builds, if applicable:

   rm dist/*
   

2. Run the build:

   poetry build
   

3. The build artefacts will be located in the dist directory at the top level of the project.

2. Upload to PyPI

1. Create a PyPI API token (you only have to do this once).

2. Increment the version number in pyproject.toml.

3. Upload build artefacts to PyPI:

   poetry publish
   

3. Create GitHub Release

1. Create a tag and push to GitHub:

   git tag <version>
   git push <version>
   

   <version> must match the updated version number in pyproject.toml.

2. Go to the Releases page for the repo.

3. Click Draft a new release.

4. Select the tag that you created in step 1.

5. Specify the title of the release (e.g., {{ cookiecutter.project_name }} v1.2.3).

6. Write a description for the release. Make sure to include: - Credit for code contributed by community members. - Significant functionality that was added/changed/removed. - Any backwards-incompatible changes and/or migration instructions. - SHA256 hashes of the build artefacts.

7. GPG-sign the description for the release (ASCII-armoured).

8. Attach the build artefacts to the release.

9. Click Publish release.