Crunch.io Client Library
Project description
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 >
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