Skip to main content

File metadata tagging and organization.

Project description

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 for more in-depth documentation):

>>> 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.

Questions/Feedback

File an issue in the GitHub issue tracker.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for coda, version 0.1.0
Filename, size & hash File type Python version Upload date
coda-0.1.0-py2.py3-none-any.whl (11.4 kB) View hashes Wheel py2.py3
coda-0.1.0.tar.gz (26.9 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page