Extensible table data structure that supports the introduction of user-defined workflow combinators and the use of these combinators in concise workflow descriptions.

This library is available as a package on PyPI:

python -m pip install metatable

The library can be imported in the usual ways:

import metatable
from metatable import *

This library makes it possible to work with tabular data that is represented as a list of lists (i.e., each row is a list of column values and a table is a list of rows):

>>> from metatable import *
>>> t = metatable([['a', 0], ['b', 1], ['c', 2]])
>>> list(iter(t))
[['a', 0], ['b', 1], ['c', 2]]

All rows in a metatable instance can be updated in-place using a symbolic representation (implemented using the symbolism library) of the transformation that must be applied to each row. For example, the transformation {1: column(0)} indicates that the value in the column having index 1 (i.e., the right-hand column) should be replaced with the value in the column having index 0 (i.e., the left-hand column):

>>> t.update({1: column(0)})
[['a', 'a'], ['b', 'b'], ['c', 'c']]

It is also possible to perform an update that removes rows based on a condition. The condition in the example below is the symbolic expression column(1) > symbol(0) (constructed using the symbolism library):

>>> from symbolism import symbol
>>> t = metatable([['a', 0], ['b', 1], ['c', 2]])
>>> t.update_filter({0: column(1)}, column(1) > symbol(0))
[[1, 1], [2, 2]]

There is also support for working with a tabular data set in which there is a header row containing column names:

>>> t = metatable([['char', 'num'], ['a', 0], ['b', 1]], header=True)
>>> t.update({1: column(0)})
[['char', 'num'], ['a', 'a'], ['b', 'b']]

All installation and development dependencies are fully specified in pyproject.toml. The project.optional-dependencies object is used to specify optional requirements for various development tasks. This makes it possible to specify additional options (such as docs, lint, and so on) when performing installation using pip:

python -m pip install .[docs,lint]

The documentation can be generated automatically from the source files using Sphinx:

python -m pip install .[docs]
cd docs
sphinx-apidoc -f -E --templatedir=_templates -o _source .. && make html

All unit tests are executed and their coverage is measured when using pytest (see the pyproject.toml file for configuration details):

python -m pip install .[test]
python -m pytest

Alternatively, all unit tests are included in the module itself and can be executed using doctest:

python src/metatable/metatable.py -v

Style conventions are enforced using Pylint:

python -m pip install .[lint]
python -m pylint src/metatable

In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.

The version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.

This library can be published as a package on PyPI by a package maintainer. First, install the dependencies required for packaging and publishing:

python -m pip install .[publish]

Ensure that the correct version number appears in pyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has an automation rule that activates and sets as the default all tagged versions. Create and push a tag for this version (replacing ?.?.? with the version number):

git tag ?.?.?
git push origin ?.?.?

Remove any old build/distribution files. Then, package the source into a distribution archive:

rm -rf build dist src/*.egg-info
python -m build --sdist --wheel .

Finally, upload the package distribution archive to PyPI:

python -m twine upload dist/*