semvec/pypi-packages huggingface dataset:
tableau 0.0.3
Tableau is a collection of helper classes for building test fixtures and seed data
License: MIT License
Keywords: test testing fixture seed

Tableau
=======

Tableau is a collection of helper classes for building test fixtures and seed data.

- Model composition without any predefined schemas.  The model object relationships are automatically deduced through the value annotations::

    from tableau import Datum

    foo = Datum(
      'Foo', ('id'), # schema name / identifiers
      id=1,
      field_a=1,
      field_b=2,
      collection=one_to_many([
        Datum(
          'Bar', auto('id'), # id will be automagically generated by the walker
          field_c=1
          ),
        Datum(
          'Bar', auto('id'),
          field_c=1
          ),
        ],
        referring_fields='foo_id'
        )
      )

- Export the model object graph to plain ANSI SQL statements, in the ORM-agnostic manner::

    import sys
    from tableau import Datum, DataSuite, DataWalker
    from tableau.sql import SQLGenerator

    # ...

    suite = DataSuite()
    DataWalker(suite)(foo)
    SQLGenerator(sys.stdout, encoding='utf-8')(suite)

  The above yields the following SQL statements::

    INSERT INTO `Foo` (`id`, `field_a`, `field_b`) VALUES
    (1, 1, 2);
    INSERT INTO `Bar` (`id`, `field_c`, `foo_id`) VALUES
    (1, 1, 1),
    (2, 1, 1);

- Automatically mapping the existing SQLAlchemy tables / declarative classes
  to the `Data`::

    from tableau.sqla import newSADatum

    # metadata = ...
    # Base = ...
    # session = ...

    class Foo(Base):
      __tablename__ = 'foos'
      id = Column(Integer, primary_key=True)
      field = Column(String)

      def some_model_specific_method(self):
        return self.field

    Datum = newSADatum(metadata, Base)
    datum = Datum(
      'foos', ('id'),
      field='test'
      )
    print datum.some_model_specific_method() # 'test'
    session.add(datum) # it can even be added to the session!


Change history
--------------

0.0.0
    Unreleased.

0.0.1
    First release. No docs!

0.0.2
    Support mapping to SQLAlchemy tables and declarative classses.
    Still no docs!

0.0.3
    Fix critical bugs in 0.0.2.
knyfe 0.4.2
A utility for rapid exploration and preprocessing of datasets.
License: MIT License
Keywords: science,data,preprocessing

knyfe knyfe is a python utility for rapid exploration and preprocessing of datasets. Use it when you have some kind of dataset and you want to get a feel for how it is composed, run some simple tests on it, or prepare it for further processing. The great thing about knyfe is that you don't have to know much about how your dataset is designed. You shouldn't have to remember in which variable resides in which column of your data matrix or how your `structs` are nested. Just get shit done.

Links
`````

* `website & documentation <http://maebert.github.com/knyfe>`_
* `GitHub Repo <https://github.com/maebert/knyfe>`_