presidio/docs/samples/python/getting_entity_values.ipynb

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   "execution_count": null,
   "id": "bcddce7b",
   "metadata": {
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   "outputs": [],
   "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/microsoft/presidio/blob/main/docs/samples/python/getting_entity_values.ipynb](https://www.github.com/microsoft/presidio/blob/main/docs/samples/python/getting_entity_values.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjusted-jurisdiction",
   "metadata": {},
   "source": [
    "# Getting a list of all identified texts\n",
    "\n",
    "This sample illustrates how to get a list of all the identified PII entities using Presidio Analyzer for detection and a custom Presidio Anonymizer operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "noted-lounge",
   "metadata": {},
   "outputs": [],
   "source": [
    "from presidio_analyzer import AnalyzerEngine\n",
    "from presidio_anonymizer import AnonymizerEngine\n",
    "from presidio_anonymizer.entities import OperatorConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "clinical-surface",
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzer = AnalyzerEngine()\n",
    "anonymizer = AnonymizerEngine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "referenced-argument",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_to_analyze = \"Hi my name is Charles Darwin and my email is cdarwin@hmsbeagle.org\"\n",
    "analyzer_results = analyzer.analyze(text_to_analyze, language=\"en\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "innovative-audio",
   "metadata": {},
   "source": [
    "A naive approach for getting the text values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "congressional-wiring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('cdarwin@hmsbeagle.org', 45, 66),\n",
       " ('Charles Darwin', 14, 28),\n",
       " ('hmsbeagle.org', 53, 66)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(text_to_analyze[res.start:res.end], res.start, res.end) for res in analyzer_results]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informal-evanescence",
   "metadata": {},
   "source": [
    "Another option is to set up a custom operator* which runs an identity function (`lambda x: x`). This operator doesn't really anonymize, but replaces the identified value with itself. This is useful as the Anonymizer handles the overlaps automatically. \n",
    "\n",
    "> In this example, the URL (hmsbeagle.org) is contained in the email address, so it's ommitted from the final result.\n",
    "\n",
    "\\* an `Operator` is usually either an `Anonymizer` or `Deanonymizer` on the presidio-anonymizer library/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "growing-motivation",
   "metadata": {},
   "outputs": [],
   "source": [
    "anonymized_results = anonymizer.anonymize(\n",
    "        text=text_to_analyze,\n",
    "        analyzer_results=analyzer_results,            \n",
    "        operators={\"DEFAULT\": OperatorConfig(\"custom\", {\"lambda\": lambda x: x})}        \n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hydraulic-association",
   "metadata": {},
   "source": [
    "The operator defined here is `DEFAULT`, meaning it will be used for all entities. The `OperatorConfig` is a custom one and the labmda is the identity function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "according-rates",
   "metadata": {},
   "source": [
    "Output text, start and end locations for each detected entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "serial-arcade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('cdarwin@hmsbeagle.org', 45, 66), ('Charles Darwin', 14, 28)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(item.text, item.start, item.end) for item in anonymized_results.items]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e64444",
   "metadata": {},
   "source": [
    "A third option would be to use the `keep` operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3833bc43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('cdarwin@hmsbeagle.org', 45, 66), ('Charles Darwin', 14, 28)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anonymized_results_with_keep = anonymizer.anonymize(\n",
    "        text=text_to_analyze,\n",
    "        analyzer_results=analyzer_results,            \n",
    "        operators={\"DEFAULT\": OperatorConfig(\"keep\")}        \n",
    "    )\n",
    "[(item.text, item.start, item.end) for item in anonymized_results_with_keep.items]"
   ]
  }
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