semvec/pypi-packages huggingface dataset:
coda 0.1.0
File metadata tagging and organization.
License: Apache-2.0
Keywords: coda,data,science,analysis,file,organization,metadata

coda
====

Coda is a file system organizer, designed for data scientists who frequently deal
with large amounts of heterogeneous data. In this age where data rules all, being able to
efficiently search and label those data is paramount to maintaining productivity. Coda
allows you to tag files with arbitrary metadata, so that you can stay organized when
managing/analyzing large datasets over time. 

As a quick example of how coda might be useful for organizing an arbitrary dataset,
see the following example (see the `documentation <http://coda.readthedocs.io/en/latest/>`_
for more in-depth documentation):

.. code-block:: python

    >>> import coda
    >>>
    >>> # generate a collection of files from a directory
    >>> cl = coda.Collection('/path/to/test/data')
    >>>
    >>> # show all of the files in the structure
    >>> print cl
    /path/to/test/data/type1.txt
    /path/to/test/data/type1.csv
    /path/to/test/data/type2.txt
    /path/to/test/data/type2.csv
    >>>
    >>> # set properties about the collection
    >>> cl.group = 'test'
    >>> cl.cohort = 'My Cohort'
    >>>
    >>> # add the files in the collection to the database
    >>> # for tracking and retrieval later
    >>> coda.add(cl)
    >>>
    >>> # do the same with a training dataset
    >>> cl = coda.Collection('/path/to/train/data', metadata={'group': 'train'})
    >>> coda.add(cl)
    >>>
    >>> # wait ... add one more file in a different location to
    >>> # the training set
    >>> fi = coda.File('/my/special/training/file.csv')
    >>> fi.group = 'train'
    >>> coda.add(fi)
    >>>
    >>> # ... later in time ...
    >>>
    >>> # query all of our training files
    >>> cl = coda.find({'group': 'train'})
    >>> print cl
    /path/to/train/data/type1.txt
    /path/to/train/data/type1.csv
    /path/to/train/data/type2.txt
    /path/to/train/data/type2.csv
    /my/special/training/file.csv
    >>>
    >>> # filter those by csv files
    >>> print cl.filter(lambda x: '.csv' in x.name)
    /path/to/train/data/type1.csv
    /path/to/train/data/type2.csv
    /my/special/training/file.csv
    >>>
    >>> # tag the special file with new metadata
    >>> cl.files[-1].special = True
    >>> coda.add(cl.files[-1])
    >>>
    >>> # query it back (for the example)
    >>> fi = coda.find_one({'special': True})
    >>> print fi.metadata
    {'group': 'train', 'special': True}


Documentation
-------------

For installation and usage instructions please see the `documentation <http://coda.readthedocs.io/en/latest/>`_.


Questions/Feedback
------------------

File an issue in the `GitHub issue tracker <https://github.com/bprinty/coda/issues>`_.