import os
from dataframer import Dataframer
client = Dataframer(
api_key=os.environ.get("DATAFRAMER_API_KEY"), # This is the default and can be omitted
)
anonymization_run = client.dataframer.anonymization_runs.retrieve(
"182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(anonymization_run.id)curl https://df-api.dataframer.ai/api/dataframer/anonymization-runs/$RUN_ID/ \
-H "Authorization: Bearer $DATAFRAMER_API_KEY"{
"id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"status": "PENDING",
"dataset_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"dataset_name": "<string>",
"pii_types": [
"<string>"
],
"detection_method": "<string>",
"llm_model_name": "<string>",
"created_by_email": "jsmith@example.com",
"duration_seconds": 123,
"results": {
"entity_summary": {},
"samples_processed": 123,
"entities_found": [
{
"start": 123,
"end": 123,
"label": "<string>",
"score": 123
}
],
"error": "<string>"
},
"anonymized_files": [
{
"id": "<string>",
"path": "<string>",
"size_in_bytes": 123,
"content_type": "<string>"
}
],
"completed_at": "2023-11-07T05:31:56Z",
"created_at": "2023-11-07T05:31:56Z",
"content_location": {
"data_plane": true,
"s3_key": "<string>",
"storage_uri": "<string>",
"sha256": "<string>",
"bytes_size": 123,
"version": 123,
"fields": {}
},
"content_unavailable_reason": "result_content_in_data_plane"
}Get Anonymization Run
Retrieve an anonymization run by ID. Also use this endpoint to poll for completion.
import os
from dataframer import Dataframer
client = Dataframer(
api_key=os.environ.get("DATAFRAMER_API_KEY"), # This is the default and can be omitted
)
anonymization_run = client.dataframer.anonymization_runs.retrieve(
"182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(anonymization_run.id)curl https://df-api.dataframer.ai/api/dataframer/anonymization-runs/$RUN_ID/ \
-H "Authorization: Bearer $DATAFRAMER_API_KEY"{
"id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"status": "PENDING",
"dataset_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"dataset_name": "<string>",
"pii_types": [
"<string>"
],
"detection_method": "<string>",
"llm_model_name": "<string>",
"created_by_email": "jsmith@example.com",
"duration_seconds": 123,
"results": {
"entity_summary": {},
"samples_processed": 123,
"entities_found": [
{
"start": 123,
"end": 123,
"label": "<string>",
"score": 123
}
],
"error": "<string>"
},
"anonymized_files": [
{
"id": "<string>",
"path": "<string>",
"size_in_bytes": 123,
"content_type": "<string>"
}
],
"completed_at": "2023-11-07T05:31:56Z",
"created_at": "2023-11-07T05:31:56Z",
"content_location": {
"data_plane": true,
"s3_key": "<string>",
"storage_uri": "<string>",
"sha256": "<string>",
"bytes_size": 123,
"version": 123,
"fields": {}
},
"content_unavailable_reason": "result_content_in_data_plane"
}Authorizations
API Key authentication. Format: "Bearer YOUR_API_KEY"
Path Parameters
UUID of the anonymization run.
Response
Anonymization run details
Unique identifier for the anonymization run.
Current status of the anonymization run.
PENDING, PROCESSING, SUCCEEDED, FAILED UUID of the seed dataset being anonymized.
Name of the seed dataset.
List of PII/PHI entity types being detected.
Entity detection method used.
LLM model name (when detection_method includes llm).
Email of the user who created this run.
Time taken to complete the run in seconds. Null until completed.
Anonymization results once the run completes.
Show child attributes
Show child attributes
List of anonymized output files produced by the run. Empty when content_unavailable_reason is set.
Show child attributes
Show child attributes
When the run completed (succeeded or failed).
When the run was created.
Where the run's output is stored in your own cloud (null unless your deployment is customer-hosted)
Show child attributes
Show child attributes
Set when results.entities_found and anonymized_files are empty because they are stored in your own cloud, not because no PII was found. The counts in results are still complete. Absent or null otherwise. The Python SDK reads the text from your data plane, fills the fields and clears this
result_content_in_data_plane