mirror of
https://github.com/data-privacy-stack/presidio.git
synced 2026-07-26 12:50:56 -05:00
23888dcab9
Revertsa725ecfa("updates to docs and markdown") andbee25c35("docs and md renames"), which were pushed to main by mistake. The migration work is preserved on a separate branch and will be reintroduced via a proper branch/PR.
617 lines
25 KiB
Plaintext
617 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bcddce7b",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# download presidio\n",
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"#!pip install presidio_analyzer presidio_anonymizer\n",
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"#!python -m spacy download en_core_web_lg\n",
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"#!pip install pandas"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3345f1c4",
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"metadata": {},
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"source": [
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"###### Path to notebook: [https://www.github.com/microsoft/presidio/blob/main/docs/samples/python/batch_processing.ipynb](https://www.github.com/microsoft/presidio/blob/main/docs/samples/python/batch_processing.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "gothic-trademark",
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"metadata": {},
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"source": [
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"# Run Presidio on structured / semi-structured data\n",
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"\n",
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"This sample shows how Presidio could be potentially extended to handle the anonymization of a table or data frame.\n",
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"It introduces methods for the analysis and anonymization of both lists and dicts. \n",
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"\n",
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"Note: this sample input here is a Pandas DataFrame and a JSON file, but it can be used in other scenarios such as querying SQL data or using Spark DataFrames.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "roman-allergy",
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"metadata": {},
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"source": [
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"### Set up imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "extensive-greensboro",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List, Optional, Dict, Union, Iterator, Iterable\n",
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"import collections\n",
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"from dataclasses import dataclass\n",
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"import pprint\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"from presidio_analyzer import AnalyzerEngine, BatchAnalyzerEngine, RecognizerResult, DictAnalyzerResult\n",
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"from presidio_anonymizer import AnonymizerEngine, BatchAnonymizerEngine\n",
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"from presidio_anonymizer.entities import EngineResult\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fiscal-affair",
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"metadata": {},
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"source": [
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"## Example using sample tabular data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bright-maple",
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"metadata": {},
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"outputs": [],
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"source": [
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"columns = [\"name phrase\", \"phone number phrase\", \"integer\", \"boolean\" ]\n",
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"sample_data = [\n",
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" ('Charlie likes this', 'Please call 212-555-1234 after 2pm', 1, True),\n",
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" ('You should talk to Mike', 'his number is 978-428-7111', 2, False),\n",
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" ('Mary had a little startup', 'Phone number: 202-342-1234', 3, False)\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "russian-proceeding",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name phrase</th>\n",
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" <th>phone number phrase</th>\n",
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" <th>integer</th>\n",
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" <th>boolean</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Charlie likes this</td>\n",
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" <td>Please call 212-555-1234 after 2pm</td>\n",
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" <td>1</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>You should talk to Mike</td>\n",
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" <td>his number is 978-428-7111</td>\n",
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" <td>2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Mary had a little startup</td>\n",
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" <td>Phone number: 202-342-1234</td>\n",
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" <td>3</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name phrase phone number phrase integer \\\n",
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"0 Charlie likes this Please call 212-555-1234 after 2pm 1 \n",
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"1 You should talk to Mike his number is 978-428-7111 2 \n",
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"2 Mary had a little startup Phone number: 202-342-1234 3 \n",
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"\n",
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" boolean \n",
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"0 True \n",
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"1 False \n",
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"2 False "
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create Pandas DataFrame\n",
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"df = pd.DataFrame(sample_data,columns=columns)\n",
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"\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "brazilian-punch",
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"metadata": {},
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"outputs": [],
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"source": [
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"# DataFrame to dict\n",
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"df_dict = df.to_dict(orient=\"list\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fixed-commerce",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'boolean': [True, False, False],\n",
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" 'integer': [1, 2, 3],\n",
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" 'name phrase': ['Charlie likes this',\n",
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" 'You should talk to Mike',\n",
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" 'Mary had a little startup'],\n",
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" 'phone number phrase': ['Please call 212-555-1234 after 2pm',\n",
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" 'his number is 978-428-7111',\n",
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" 'Phone number: 202-342-1234']}\n"
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]
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}
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],
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"source": [
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"pprint.pprint(df_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "verified-spirituality",
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"metadata": {},
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"outputs": [],
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"source": [
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"analyzer = AnalyzerEngine()\n",
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"batch_analyzer = BatchAnalyzerEngine(analyzer_engine=analyzer)\n",
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"batch_anonymizer = BatchAnonymizerEngine()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "narrative-freeze",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[DictAnalyzerResult(key='name phrase', value=['Charlie likes this', 'You should talk to Mike', 'Mary had a little startup'], recognizer_results=[[type: PERSON, start: 0, end: 7, score: 0.85], [type: PERSON, start: 19, end: 23, score: 0.85], [type: PERSON, start: 0, end: 4, score: 0.85]]),\n",
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" DictAnalyzerResult(key='phone number phrase', value=['Please call 212-555-1234 after 2pm', 'his number is 978-428-7111', 'Phone number: 202-342-1234'], recognizer_results=[[type: DATE_TIME, start: 31, end: 34, score: 0.85, type: PHONE_NUMBER, start: 12, end: 24, score: 0.75], [type: PHONE_NUMBER, start: 14, end: 26, score: 0.75], [type: PHONE_NUMBER, start: 14, end: 26, score: 0.75]]),\n",
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" DictAnalyzerResult(key='integer', value=[1, 2, 3], recognizer_results=[[], [], []]),\n",
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" DictAnalyzerResult(key='boolean', value=[True, False, False], recognizer_results=[[], [], []])]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"analyzer_results = batch_analyzer.analyze_dict(df_dict, language=\"en\")\n",
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"analyzer_results = list(analyzer_results)\n",
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"analyzer_results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "rural-month",
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"metadata": {},
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"outputs": [],
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"source": [
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"anonymizer_results = batch_anonymizer.anonymize_dict(analyzer_results)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "acute-mauritius",
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"metadata": {},
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"outputs": [],
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"source": [
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"scrubbed_df = pd.DataFrame(anonymizer_results)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "irish-phoenix",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name phrase</th>\n",
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" <th>phone number phrase</th>\n",
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" <th>integer</th>\n",
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" <th>boolean</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td><PERSON> likes this</td>\n",
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" <td>Please call <PHONE_NUMBER> after <DATE_TIME></td>\n",
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" <td>1</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>You should talk to <PERSON></td>\n",
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" <td>his number is <PHONE_NUMBER></td>\n",
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" <td>2</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td><PERSON> had a little startup</td>\n",
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" <td>Phone number: <PHONE_NUMBER></td>\n",
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" <td>3</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name phrase \\\n",
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"0 <PERSON> likes this \n",
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"1 You should talk to <PERSON> \n",
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"2 <PERSON> had a little startup \n",
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"\n",
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" phone number phrase integer boolean \n",
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"0 Please call <PHONE_NUMBER> after <DATE_TIME> 1 True \n",
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"1 his number is <PHONE_NUMBER> 2 False \n",
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"2 Phone number: <PHONE_NUMBER> 3 False "
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scrubbed_df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1cb4b006",
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"metadata": {},
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"source": [
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"## Example using JSON"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "1063019b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'key_a': {'key_a1': 'My phone number is 212-121-1424'},\n",
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" 'key_b': {'www.abc.com'},\n",
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" 'key_c': 3,\n",
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" 'names': ['James Bond', 'Clark Kent', 'Hakeem Olajuwon', 'No name here!']}\n"
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]
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}
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],
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"source": [
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"nested_dict = {\n",
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" \"key_a\": {\"key_a1\": \"My phone number is 212-121-1424\"},\n",
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" \"key_b\": {\"www.abc.com\"},\n",
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" \"key_c\": 3,\n",
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" \"names\": [\"James Bond\", \"Clark Kent\", \"Hakeem Olajuwon\", \"No name here!\"]\n",
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"}\n",
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"\n",
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"pprint.pprint(nested_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "e3c09b4b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'key_a': {'key_a1': 'My phone number is <PHONE_NUMBER>'},\n",
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" 'key_b': ['<URL>'],\n",
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" 'key_c': 3,\n",
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" 'names': ['<PERSON>', '<PERSON>', '<PERSON>', 'No name here!']}\n"
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]
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}
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],
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"source": [
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"# Analyze dict\n",
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"analyzer_results = batch_analyzer.analyze_dict(input_dict = nested_dict, language=\"en\")\n",
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"\n",
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"# Anonymize dict\n",
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"anonymizer_results = batch_anonymizer.anonymize_dict(analyzer_results = analyzer_results)\n",
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"pprint.pprint(anonymizer_results)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e593eb11",
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"metadata": {},
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"source": [
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"### Ignoring specific keys"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "84b2ef95",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'key_a': {'key_a1': 'My phone number is 212-121-1424'},\n",
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" 'key_b': ['<URL>'],\n",
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" 'key_c': 3,\n",
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" 'names': ['James Bond', 'Clark Kent', 'Hakeem Olajuwon', 'No name here!']}\n"
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]
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}
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],
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"source": [
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"keys_to_skip=[\"key_a1\", \"names\"]\n",
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"analyzer_results = batch_analyzer.analyze_dict(input_dict = nested_dict, language=\"en\", keys_to_skip=keys_to_skip)\n",
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"\n",
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"# Anonymize dict\n",
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"anonymizer_results = batch_anonymizer.anonymize_dict(analyzer_results = analyzer_results)\n",
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"pprint.pprint(anonymizer_results)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd0cde2a",
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"metadata": {},
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"source": [
|
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"### Ignoring nested keys"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "93ed8769",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'key_a': {'key_a1': 'My phone number is 212-121-1424'},\n",
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" 'key_b': ['<URL>'],\n",
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" 'key_c': 3,\n",
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" 'names': ['<PERSON>', '<PERSON>', '<PERSON>', 'No name here!']}\n"
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]
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}
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],
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"source": [
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"keys_to_skip = [\"key_a.key_a1\"]\n",
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"\n",
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"analyzer_results = batch_analyzer.analyze_dict(input_dict = nested_dict, language=\"en\", keys_to_skip=keys_to_skip)\n",
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"\n",
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"# Anonymize dict\n",
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"anonymizer_results = batch_anonymizer.anonymize_dict(analyzer_results = analyzer_results)\n",
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"pprint.pprint(anonymizer_results)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa0ab530",
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"metadata": {},
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"source": [
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"#### **Note!**\n",
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"\n",
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"JSON files with objects within lists, e.g.:\n",
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"```\n",
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"{\n",
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" \"key\": [\n",
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" {\n",
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" \"key2\": \"Peter Parker\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"key3\": \"555-1234\"\n",
|
|
" }\n",
|
|
" ]\n",
|
|
"}\n",
|
|
"```\n",
|
|
"\n",
|
|
"Are not yet supported. Consider breaking the JSON to parts if needed."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c708ff56",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Multiprocessing\n",
|
|
"\n",
|
|
"`BatchAnalyzerEngine` builds upon spaCy's pipelines. For more info about multiprocessing, see https://spacy.io/usage/processing-pipelines#multiprocessing.\n",
|
|
"\n",
|
|
"In Presidio, one can pass the `n_process` argument and the `batch_size` parameter to define how processing is done in parallel."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "81316c6c",
|
|
"metadata": {},
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "09a80e87",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Monitor] Active Python processes: 4 - [38773, 38774, 45860, 109966]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[Monitor] Active Python processes: 8 - [38773, 38774, 45860, 109966, 109973, 109976, 109977, 109978]\n",
|
|
"[Monitor] Active Python processes: 8 - [38773, 38774, 45860, 109966, 109973, 109976, 109977, 109978]\n",
|
|
"[Monitor] Active Python processes: 8 - [38773, 38774, 45860, 109966, 109973, 109976, 109977, 109978]\n",
|
|
"[Monitor] Active Python processes: 8 - [38773, 38774, 45860, 109966, 109973, 109976, 109977, 109978]\n",
|
|
"[Monitor] Active Python processes: 4 - [38773, 38774, 45860, 109966]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import multiprocessing\n",
|
|
"import psutil\n",
|
|
"import time\n",
|
|
"\n",
|
|
"def analyze_batch_multiprocess(n_process=12, batch_size=4):\n",
|
|
" \"\"\"Run BatchAnalyzer with `n_process` processes and batch size of `batch_size`.\"\"\"\n",
|
|
" list_of_texts = [\"My name is mike\"]*1000\n",
|
|
"\n",
|
|
" results = batch_analyzer.analyze_iterator(\n",
|
|
" texts=list_of_texts, \n",
|
|
" language=\"en\",\n",
|
|
" n_process=n_process, \n",
|
|
" batch_size=batch_size\n",
|
|
" )\n",
|
|
"\n",
|
|
" return list(results)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"def monitor_processes():\n",
|
|
" \"\"\"Monitor all Python processes dynamically.\"\"\"\n",
|
|
" while True:\n",
|
|
" processes = [p for p in psutil.process_iter(attrs=['pid', 'name']) if \"python\" in p.info['name']]\n",
|
|
" print(f\"[Monitor] Active Python processes: {len(processes)} - {[p.info['pid'] for p in processes]}\")\n",
|
|
" time.sleep(1)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Run interactive monitoring\n",
|
|
"monitor_proc = multiprocessing.Process(target=monitor_processes, daemon=True)\n",
|
|
"monitor_proc.start()\n",
|
|
"\n",
|
|
"# Run the batch analyzer process\n",
|
|
"analyze_batch_multiprocess(n_process=4, batch_size=2)\n",
|
|
"\n",
|
|
"# Wait for everything to conclude\n",
|
|
"time.sleep(1) \n",
|
|
"\n",
|
|
"# Clean up (not needed if daemon=True, but useful if stopping manually)\n",
|
|
"monitor_proc.terminate()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7b7b6c64",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "presidio-analyzer-sAyh6tzK-py3.12",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|