A Python library to consume the RavenPack API.
Please consider migrating your script to the new API 1.0.
The Python client helps managing the API calls to the RavenPack dataset server in a Pythonic way, here are some examples of usage, you can find more in the tests.
To use the API you need to have a RavenPack API KEY.
Start defining an instance of the RPApi giving your key (or you can set it in your
environment like export RP_API_KEY=your_api_key).
from ravenpackapi.core import RPApi
api = RPApi(api_key='your_api_key')Then, for example to get a file with all the events for the entity 69ADD9 given a data range you can use:
# specify your query:
# here everything about 69ADD9 in 2015
query = dict(
start_date='2015-12-01', end_date='2016-01-01',
return_type='dump',
entities=['69ADD9']
)
data_filename = 'output.csv'
# do the query and save it to file
analytics = api.get_analytics_file(filename=data_filename,
**query)Here is an example of the entity mapping endpoint to get the RavenPack entity ids given some known company
entities = [{'ticker': 'AAPL', 'name': 'Apple Inc.'},
{'ticker': 'JPM'},
{'listing': 'XNYS:DVN'}]
# get the ravenpack entity id using the mapping API
mapping = api.get_entity_mapping(entities)
rp_entities_id = [e['RP_ENTITIES'][0]['RP_ENTITY_ID'] for e in mapping['IDENTIFIERS_MAPPED']]
print("Entities mapped to this ids: {}".format(rp_entities_id))RavenPack APIs support a wide variety of filters, See the documention. Here some examples:
rp_entities_id = ['D8442A'] # query news about Apple Inc.
# I create a common base query querying 2016
query = dict(
start_date='2016-01-01', end_date='2017-01-01',
return_type='dump',
)
# get the analytics with event_relevance >= 90
query.update(dict(
entities=rp_entities_id,
filters=dict(
event_relevance={'$gte': 90}
),
))
# then we can enumerate all the analytics
for row in api.get_analytics(**query):
print(row)
# or save them to a csv file
api.get_analytics_file('test.csv', **query)