CKAN integration for Dataflows.
Project description
dataflows-ckan
Dataflows processors to work with CKAN.
Features
dump_to_ckan
processor
Contents
Getting Started
Installation
The package use semantic versioning. It means that major versions could include breaking changes. It's recommended to specify package
version range in your setup/requirements
file e.g. package>=1.0,<2.0
.
$ pip install dataflows-ckan
Examples
These processors have to be used as a part of data flow. For example:
flow = Flow(
load('data/data.csv'),
dump_to_ckan(
host,
api_key,
owner_org,
overwrite_existing_data=True,
push_to_datastore=False,
push_to_datastore_method='insert',
**options,
),
)
flow.process()
Documentation
dump_to_ckan
Saves the DataPackage to a CKAN instance.
Contributing
Create a virtual environment and install Poetry.
Then install the package in editable mode:
$ make install
Run the tests:
$ make test
Format your code:
$ make format
Changelog
v0.1.0
- an initial port from https://github.com/frictionlessdata/datapackage-pipelines-ckan based on the great work of @brew and @amercader
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
dataflows-ckan-0.2.0.tar.gz
(5.3 kB
view hashes)
Built Distribution
Close
Hashes for dataflows_ckan-0.2.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e02839accee0c57a4c4b5d5571c952312295de770c8dc548ce6fc7d179d814a2 |
|
MD5 | 0af1f572c403cbd9c324c170b4b30751 |
|
BLAKE2b-256 | aa2183d58e11d1bc205c5946a5243d35bb5a285539334a58ce7051e0e7530f5a |