Metadata-Version: 2.1
Name: pycrunch
Version: 0.4.2
Summary: Crunch.io Client Library
Home-page: https://github.com/Crunch-io/pycrunch
Author: Crunch.io
Author-email: dev@crunch.io
License: LGPL
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*
Provides-Extra: testing
Provides-Extra: pandas
Requires-Dist: requests (>=2.14.0)
Requires-Dist: six
Provides-Extra: pandas
Requires-Dist: pandas; (python_version=="2.7" or python_version>="3.5") and extra == 'pandas'
Provides-Extra: pandas
Requires-Dist: pandas (~=0.19.0); (python_version=="3.4") and extra == 'pandas'
Provides-Extra: testing
Requires-Dist: mock; extra == 'testing'
Requires-Dist: pytest; extra == 'testing'
Requires-Dist: pytest-cov; extra == 'testing'
Provides-Extra: testing
Requires-Dist: pandas; (python_version=="2.7" or python_version>="3.5") and extra == 'testing'
Provides-Extra: testing
Requires-Dist: pandas (~=0.19.0); (python_version=="3.4") and extra == 'testing'

pycrunch
========

A Python client library for Crunch.io.


Using pycrunch
--------------

To use pycrunch in your project, run:

    $ python setup.py develop

This will make the code in this directory available to other projects.

Getting started
---------------

Start a simple site session via:

    >>> import pycrunch
    >>> site = pycrunch.connect("me@mycompany.com", "yourpassword", "https://app.crunch.io/api/")

Or, if you have a crunch access token:

    >>> import pycrunch
    >>> site = pycrunch.connect_with_token("DFIJFIJWIEJIJFKSJLKKDJKFJSLLSLSL", "https://app.crunch.io/api/")

Then, you can browse the site. Use `print` to pretty-indent JSON payloads:

    >>> print site
    pycrunch.shoji.Catalog(**{
        "element": "shoji:catalog",
        "self": "https://app.crunch.io/api/",
        "description": "The API root.",
        "catalogs": {
            "datasets": "https://app.crunch.io/api/datasets/",
            "specifications": "https://app.crunch.io/api/specifications/",
            ...
        },
        "urls": {
            "logout_url": "https://app.crunch.io/api/logout/",
            ...
        },
        "views": {
            "migration": "https://app.crunch.io/api/migration/"
        }
    })

URI's in payloads' catalogs, views, fragments, and urls collections
are followable automatically:

    >>> print site.datasets
    pycrunch.shoji.Catalog(**{
        "self": "https://app.crunch.io/api/datasets/",
        "element": "shoji:catalog",
        "index": {
            "https://app.crunch.io/api/datasets/dbf9fca7b727/": {
                "owner_display_name": "me@mycompany.com",
                "description": "",
                "id": "dbf9fca7b727",
                "owner_id": "https://app.crunch.io/api/users/253b68/",
                "archived": false,
                "name": "Hog futures tracking (May 2014)"
            },
        },
        ...
    })

Each recognized JSON payload also automatically gives dotted-attribute
access to the members of each JSON object:

    >>> print site.datasets.index.values()[0]
    pycrunch.shoji.Tuple(**{
        "owner_display_name": "me@mycompany.com",
        "description": "",
        "id": "dbf9fca7b727",
        "owner_id": "https://app.crunch.io/api/users/253b68/",
        "archived": false,
        "name": "Hog futures tracking (May 2014)"
    })

Responses may also possess additional helpers, like the `entity` property of
each Tuple in a catalog's index, which follows the link to the Entity resource:

    >>> print site.datasets.index.values()[0].entity_url
    "https://app.crunch.io/api/datasets/dbf9fca7b727/"

    >>> print site.datasets.index.values()[0].entity
    pycrunch.shoji.Entity(**{
        "self": "https://app.crunch.io/api/datasets/dbf9fca7b727/",
        "element": "shoji:entity",
        "description": "Detail for a given dataset",
        "specification": "https://app.crunch.io/api/specifications/datasets/",
        "body": {
            "archived": false,
            "user_id": "253b68",
            "name": "Hog futures tracking (May 2014)"
            "weight": "https://app.crunch.io/api/datasets/dbf9fca7b727/variables/36f5404/",
            "creation_time": "2014-03-06T18:23:26.780752+00:00",
            "description": ""
        },
        "catalogs": {
            "batches": "https://app.crunch.io/api/datasets/dbf9fca7b727/batches/",
            "joins": "https://app.crunch.io/api/datasets/dbf9fca7b727/joins/",
            "variables": "https://app.crunch.io/api/datasets/dbf9fca7b727/variables/",
            "filters": "https://app.crunch.io/api/datasets/dbf9fca7b727/filters/",
            ...
        },
        "views": {
            "cube": "https://app.crunch.io/api/datasets/dbf9fca7b727/cube/",
            ...
        },
        "urls": {
            "revision_url": "https://app.crunch.io/api/datasets/dbf9fca7b727/revision/",
            ...
        },
        "fragments": {
            "table": "https://app.crunch.io/api/datasets/dbf9fca7b727/table/"
        }
    })

You typically add new resources to a Catalog via its `create` method:

    >>> ds = site.datasets.create({"body": {
            'name': "My first dataset"
        }}, refresh=True)
    >>> gender = ds.variables.create({"body": {
            'name': 'Gender',
            'alias': 'gender',
            'type': 'categorical',
            'categories': [
                {'id': -1, 'name': 'No Data', 'numeric_value': None, 'missing': True},
                {'id': 1, 'name': 'M', 'numeric_value': None, 'missing': False},
                {'id': 2, 'name': 'F', 'numeric_value': None, 'missing': False}
            ],
            'values': [1, 2, {"?": -1}, 2]
        }}, refresh=True)
    >>> print ds.table.data
    pycrunch.elements.JSONObject(**{
        "e7f361628": [
            1,
            2,
            {"?": -1},
            2
        ]
    })

To access a Pandas Dataframe of the data in your dataset:

    >>> from pycrunch import pandaslib as crunchpandas
    >>> df = crunchpandas.dataframe_from_dataset(site,'baadf00d000339d9faadg00beab11e')
    >>> print(df)
    < Draws a dataframe table >



