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presidio/docs/samples/python/keep_entities.ipynb
2026-06-28 10:27:33 +03:00

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"source": [
"# download presidio\n",
"!pip install presidio_analyzer presidio_anonymizer\n",
"!python -m spacy download en_core_web_lg"
]
},
{
"cell_type": "markdown",
"id": "3345f1c4",
"metadata": {},
"source": [
"###### Path to notebook: [https://www.github.com/data-privacy-stack/presidio/blob/main/docs/samples/python/keep_entities.ipynb](https://www.github.com/data-privacy-stack/presidio/blob/main/docs/samples/python/keep_entities.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "gothic-trademark",
"metadata": {},
"source": [
"# Keeping some PIIs from being anonymized\n",
"\n",
"This sample shows how to use Presidio's `keep` anonymizer to keep some of the identified PIIs in the output string"
]
},
{
"cell_type": "markdown",
"id": "roman-allergy",
"metadata": {},
"source": [
"### Set up imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "extensive-greensboro",
"metadata": {},
"outputs": [],
"source": [
"from presidio_anonymizer import AnonymizerEngine\n",
"from presidio_anonymizer.entities import RecognizerResult, OperatorConfig"
]
},
{
"cell_type": "markdown",
"id": "metropolitan-atlantic",
"metadata": {},
"source": [
"### Presidio Anonymizer: Keep person names\n",
"\n",
"This example input has 2 PIIs, an person name and a location. We configure the anonymizer to replace the location name with a placeholder, but keep the person name unmodified."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "medium-ridge",
"metadata": {},
"outputs": [],
"source": [
"engine = AnonymizerEngine()\n",
"\n",
"# Invoke the anonymize function with the text,\n",
"# analyzer results (potentially coming from presidio-analyzer)\n",
"# and 'keep' operator on <PERSON> PIIs\n",
"anonymize_result = engine.anonymize(\n",
" text=\"My name is James Bond, I live in London\",\n",
" analyzer_results=[\n",
" RecognizerResult(entity_type=\"PERSON\", start=11, end=21, score=0.8),\n",
" RecognizerResult(entity_type=\"LOCATION\", start=33, end=39, score=0.8),\n",
" ],\n",
" operators={\n",
" \"PERSON\": OperatorConfig(\"keep\"),\n",
" \"DEFAULT\": OperatorConfig(\"replace\"),\n",
" },\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1d2cabaa-4aa6-49cf-875d-4bdf407215b4",
"metadata": {},
"source": [
"### Result: Name unmodified, but tracked\n",
"\n",
"The person name is preserved in the result text, but remains tracked in the items list."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "421c2914-9b75-4c33-a270-e410d91d036b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"text: My name is James Bond, I live in <LOCATION>\n",
"items:\n",
"[\n",
" {'start': 33, 'end': 43, 'entity_type': 'LOCATION', 'text': '<LOCATION>', 'operator': 'replace'},\n",
" {'start': 11, 'end': 21, 'entity_type': 'PERSON', 'text': 'James Bond', 'operator': 'keep'}\n",
"]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymize_result"
]
}
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"display_name": "Python 3 (ipykernel)",
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