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Dow Jones Factiva Analytics Python Library

https://readthedocs.org/projects/factiva-analytics-python/badge/?version=latest&style=plastic

This library simplifies the integration to Factiva Analytics API services that delivers premium news content.

The following services are currently implemented.

  • auth: Contains tools to handle UserKey authentication and account statistics.
  • Snapshots: Allows to run each snapshot creation, monitoring, download and local exploration, in an individual manner. Also allows to run the whole process within a single method.
  • Streams: In addition to creating and getting stream details, contains the methods to easily implement a stream listener and push the content to other locations appropriate for high-available setups.
  • Taxonomy: Operations that return taxonomies applied to classify news content.
  • ArticleFetcher: Gets article's content by unique identifiers (AN), for display purposes only.

Installation

To install this library, run the following commands.

$ pip install --upgrade factiva-analytics

Using Library services

Most Factiva Analytics services are implemented in this library. There may be a delay (commonly weeks) when new features are released and their operations are implemented in this package.

Getting Account Information

Create an AccountInfo instance that contains a summary of the account's basic information and usage statistics.

from factiva.analytics import AccountInfo
u = AccountInfo(
    user_key='abcd1234abcd1234abcd1234abcd1234'  # Not needed if the ENV variable FACTIVA_USERKEY is set
)
print(u)
<'factiva.analytics.AccountInfo'>
├─user_key: <'factiva.analytics.UserKey'>
│  ├─key: ****************************1234
│  └─cloud_token: **********************YKB12sJrkHXX
├─account_name: AccName1234
├─account_type: account_with_contract_limits
├─active_product: DNA
├─max_allowed_extracted_documents: 8,000,000
├─max_allowed_extractions: 20
├─currently_running_extractions: 0
├─total_extracted_documents: 5,493,078
├─total_extractions: 4
├─total_stream_instances: 0
├─total_stream_subscriptions: 0
├─extractions_list: <NotLoaded>
├─streams_list: <NotLoaded>
├─enabled_company_identifiers:
│  ├─[1]: sedol
│  ├─[3]: cusip
│  ├─[4]: isin
│  └─[5]: ticker_exchange
├─remaining_documents: 2,506,922
└─remaining_extractions: 16

Snapshot Explain

Creates an API request that tests the query and returns the number of matching items in the archive.

from factiva.analytics import SnapshotExplain
my_query = "publication_datetime >= '2023-01-01 00:00:00' AND UPPER(source_code) = 'DJDN'"
my_explain = SnapshotExplain(
    user_key='abcd1234abcd1234abcd1234abcd1234',  # Not needed if the ENV variable FACTIVA_USERKEY is set
    query=my_query)
my_explain.process_job()  # This operation can take several seconds to complete
print(my_explain)
<'factiva.analytics.SnapshotExplain'>
├─user_key: <'factiva.analytics.UserKey'>
│  ├─key: ****************************1234
│  └─cloud_token: **********************YKB12sJrkHXX
├─query: <'factiva.analytics.SnapshotExplainQuery'>
│  ├─where: publication_datetime >= '2023-01-01 00:00:00' AND UPPER(source_code) = 'DJDN'
│  ├─includes: <NotSet>
│  ├─excludes: <NotSet>
│  ├─include_lists: <NotSet>
│  └─exclude_lists: <NotSet>
├─job_response: <'factiva.analytics.SnapshotExplainJobResponse'>
│  ├─job_id: 3ee35a80-0406-4f2b-a999-3e4eb5aa94d8
│  ├─job_link: https://api.dowjones...8/_explain
│  ├─job_state: JOB_STATE_DONE
│  ├─volume_estimate: 2,482,057
│  └─errors: <NoErrors>
└─samples: <NotRetrieved>

Snapshot Extraction

Create a new snapshot and download to a local repository just require a few lines of code.

from factiva.analytics import SnapshotExtraction
my_query = "publication_datetime >= '2023-01-01 00:00:00' AND UPPER(source_code) = 'DJDN'"
my_snapshot = SnapshotExtraction(
    user_key='abcd1234abcd1234abcd1234abcd1234',  # Can be ommited if exist as env variable
    query=my_query)
my_snapshot.process_job()  # This operation can take several minutes to complete

After the process completes, the output files are stored in a subfolder named as the Extraction Job ID.

In the previous code a new snapshot is created using my_query as selection criteria and user_key for user authentication. After the job is being validated internally, a Snapshot Id is obtained along with the list of files to download. Files are automatically downloaded to a folder named equal to the snapshot ID, and contents are loaded as a Pandas DataFrame to the variable news_articles. This process may take several minutes, but automates the extraction process significantly.
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