Metadata-Version: 2.1
Name: intake
Version: 0.4.2
Summary: Data load and catalog system
Home-page: https://github.com/intake/intake
Maintainer: Martin Durant
Maintainer-email: mdurant@anaconda.com
License: BSD
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: appdirs
Requires-Dist: cloudpickle
Requires-Dist: dask (>=0.17.0)
Requires-Dist: holoviews
Requires-Dist: jinja2
Requires-Dist: msgpack-numpy
Requires-Dist: msgpack-python
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: python-snappy
Requires-Dist: pyyaml
Requires-Dist: requests
Requires-Dist: six
Requires-Dist: toolz
Requires-Dist: tornado (>=4.5.1)

# Intake: A general interface for loading data

![Logo](https://github.com/intake/intake/raw/master/logo-small.png)

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Intake is a lightweight set of tools for loading and sharing data in data science projects.
Intake helps you:

* Load data from a variety of formats (see the [current list of known plugins](http://intake.readthedocs.io/en/latest/plugin-directory.html)) into containers you already know, like Pandas dataframes, Python lists, NumPy arrays, and more.
* Convert boilerplate data loading code into reusable Intake plugins
* Describe data sets in catalog files for easy reuse and sharing between projects and with others.
* Share catalog information (and data sets) over the network with the Intake server

Documentation is available at [Read the Docs](http://intake.readthedocs.io/en/latest).

Status of intake and related packages is available at [Status Dashboard](https://intake.github.io/status)

Install
-------

Recommended method using conda:
```bash
conda install -c conda-forge intake
```


Development Environment
----------------------------
 * Create development Python environment.
 * `pip install -r requirements.txt`
 * `python setup.py develop`
 * Verify development environment by running the unit tests with `py.test`.


