Updated disclaimer and repo emoji.

README.md

@@ -1,8 +1,8 @@
 ---
 title: Anonymizer
-emoji: 🏢
-colorFrom: red
-colorTo: green
+emoji: 🕵️
+colorFrom: yellow
+colorTo: red
 sdk: streamlit
 sdk_version: 1.17.0
 app_file: app.py

app.py

@@ -3,7 +3,9 @@ import streamlit as st
 from streamlit_extras.let_it_rain import rain
 
 # Options
-DISCLAIMER = "*Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam urna sem, bibendum efficitur pellentesque a, sollicitudin pharetra urna. Nam vel lectus vitae elit luctus feugiat a a purus. Aenean mollis quis ipsum sed ornare. Nunc sit amet ultricies tellus. Vivamus vulputate sem id molestie viverra. Etiam egestas lobortis enim, sit amet lobortis ligula sollicitudin vel. Nunc eget ipsum sollicitudin, convallis.*"
+DISCLAIMER = """
+    *This app processes data using 2-anonymity, an implementation of the k-anonymity framework. While this is a great start to anonymizing your data, it is by no means perfect, and should be used with caution. For example, some sets of sensitive features which may clearly be identified by a human could be missed by our algorithm. Please keep this in mind.*
+    """
 K = 2
 
 # Page Config

k-anonymize.ipynb

@@ -0,0 +1,816 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import combinations\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data from file into a pandas df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File 'hr.csv' loaded successfully.                                                                                      \n",
+      "Found 311 rows, 36 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATADIR=\"data/\"\n",
+    "FILENAME=None\n",
+    "\n",
+    "while FILENAME is None:\n",
+    "    \n",
+    "    file_candidate = input(\"Enter file name:\")\n",
+    "    if file_candidate == \"\": break\n",
+    "    \n",
+    "    try:\n",
+    "        print(f\"Assesing file '{file_candidate}'...\".ljust(120), end=\"\\r\")\n",
+    "        file_path = DATADIR + file_candidate\n",
+    "        extension = file_candidate.split(\".\")[-1] \n",
+    "        match extension:\n",
+    "            case \"csv\":\n",
+    "                df = pd.read_csv(file_path)\n",
+    "            case \"json\":\n",
+    "                df = pd.read_json(file_path)\n",
+    "            case \"xlsx\":\n",
+    "                df = pd.read_excel(file_path)\n",
+    "            case _:\n",
+    "                print(f\"Error: Invalid extension '{extension}'\")\n",
+    "                continue\n",
+    "        print(f\"File '{file_candidate}' loaded successfully.\")\n",
+    "        rows, columns = df.shape\n",
+    "        print(f\"Found {rows} rows, {columns} columns\")\n",
+    "        FILENAME = file_candidate\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"Error: '{file_candidate}' doesn't exist in {os.getcwd()}/{DATADIR}\")\n",
+    "    except Exception as error:\n",
+    "        print(f\"Error: Unable to read file '{file_candidate}' ({str(type(error))}: {error})\".ljust(120))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean data to remove duplicates and rows with missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DROP_MISSING = False\n",
+    "REMOVE_DUPLICATES = True\n",
+    "\n",
+    "df = df.dropna(how=\"any\" if DROP_MISSING else \"all\")\n",
+    "if REMOVE_DUPLICATES: df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Anonymize data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+      "/var/folders/7t/d7j4tqwj061958h80lldp0dh0000gn/T/ipykernel_19712/355045301.py:38: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Employee_Name</th>\n",
+       "      <th>EmpID</th>\n",
+       "      <th>MarriedID</th>\n",
+       "      <th>MaritalStatusID</th>\n",
+       "      <th>GenderID</th>\n",
+       "      <th>EmpStatusID</th>\n",
+       "      <th>DeptID</th>\n",
+       "      <th>PerfScoreID</th>\n",
+       "      <th>FromDiversityJobFairID</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ManagerName</th>\n",
+       "      <th>ManagerID</th>\n",
+       "      <th>RecruitmentSource</th>\n",
+       "      <th>PerformanceScore</th>\n",
+       "      <th>EngagementSurvey</th>\n",
+       "      <th>EmpSatisfaction</th>\n",
+       "      <th>SpecialProjectsCount</th>\n",
+       "      <th>LastPerformanceReview_Date</th>\n",
+       "      <th>DaysLateLast30</th>\n",
+       "      <th>Absences</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10022, 10042)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(62065, 63381)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Michael Albert</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>Exceeds</td>\n",
+       "      <td>(4.52, 4.68)</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1/17/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10064, 10084)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(92328, 104437)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Simon Roup</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Indeed</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(4.9, 5.0)</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10190, 10210)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(64816, 66825)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kissy Sullivan</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(2.9, 3.18)</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10085, 10105)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(64816, 66825)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Elijiah Gray</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>Indeed</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(4.7, 4.88)</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1/3/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10064, 10084)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(47837, 51259)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Webster Butler</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>Google Search</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(5.0, 5.0)</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2/1/2016</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10127, 10147)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(64816, 66825)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kissy Sullivan</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(3.99, 4.1)</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2/28/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(47837, 51259)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Brannon Miller</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Google Search</td>\n",
+       "      <td>PIP</td>\n",
+       "      <td>(3.19, 3.5)</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10001, 10021)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Janet King</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Employee Referral</td>\n",
+       "      <td>Exceeds</td>\n",
+       "      <td>(4.52, 4.68)</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2/21/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10043, 10063)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(77692, 90100)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Simon Roup</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Employee Referral</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(5.0, 5.0)</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2/1/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>None</td>\n",
+       "      <td>(10252, 10271)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>(45046, 47750)</td>\n",
+       "      <td>...</td>\n",
+       "      <td>David Stanley</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>LinkedIn</td>\n",
+       "      <td>Fully Meets</td>\n",
+       "      <td>(4.5, 4.52)</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1/30/2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>311 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Employee_Name           EmpID  MarriedID  MaritalStatusID  GenderID  \\\n",
+       "0            None  (10022, 10042)          0                0         1   \n",
+       "1            None  (10064, 10084)          1                1         1   \n",
+       "2            None  (10190, 10210)          1                1         0   \n",
+       "3            None  (10085, 10105)          1                1         0   \n",
+       "4            None  (10064, 10084)          0                2         0   \n",
+       "..            ...             ...        ...              ...       ...   \n",
+       "306          None  (10127, 10147)          0                0         1   \n",
+       "307          None            None          0                0         0   \n",
+       "308          None  (10001, 10021)          0                0         0   \n",
+       "309          None  (10043, 10063)          0                0         0   \n",
+       "310          None  (10252, 10271)          0                4         0   \n",
+       "\n",
+       "     EmpStatusID  DeptID  PerfScoreID  FromDiversityJobFairID  \\\n",
+       "0              1       5            4                       0   \n",
+       "1              5       3            3                       0   \n",
+       "2              5       5            3                       0   \n",
+       "3              1       5            3                       0   \n",
+       "4              5       5            3                       0   \n",
+       "..           ...     ...          ...                     ...   \n",
+       "306            1       5            3                       0   \n",
+       "307            5       5            1                       0   \n",
+       "308            1       3            4                       0   \n",
+       "309            1       3            3                       0   \n",
+       "310            1       5            3                       0   \n",
+       "\n",
+       "              Salary  ...     ManagerName  ManagerID  RecruitmentSource  \\\n",
+       "0     (62065, 63381)  ...  Michael Albert       22.0           LinkedIn   \n",
+       "1    (92328, 104437)  ...      Simon Roup        4.0             Indeed   \n",
+       "2     (64816, 66825)  ...  Kissy Sullivan       20.0           LinkedIn   \n",
+       "3     (64816, 66825)  ...    Elijiah Gray       16.0             Indeed   \n",
+       "4     (47837, 51259)  ...  Webster Butler       39.0      Google Search   \n",
+       "..               ...  ...             ...        ...                ...   \n",
+       "306   (64816, 66825)  ...  Kissy Sullivan       20.0           LinkedIn   \n",
+       "307   (47837, 51259)  ...  Brannon Miller       12.0      Google Search   \n",
+       "308             None  ...      Janet King        2.0  Employee Referral   \n",
+       "309   (77692, 90100)  ...      Simon Roup        4.0  Employee Referral   \n",
+       "310   (45046, 47750)  ...   David Stanley       14.0           LinkedIn   \n",
+       "\n",
+       "    PerformanceScore EngagementSurvey EmpSatisfaction SpecialProjectsCount  \\\n",
+       "0            Exceeds     (4.52, 4.68)               5                    0   \n",
+       "1        Fully Meets       (4.9, 5.0)               3                    6   \n",
+       "2        Fully Meets      (2.9, 3.18)               3                    0   \n",
+       "3        Fully Meets      (4.7, 4.88)               5                    0   \n",
+       "4        Fully Meets       (5.0, 5.0)               4                    0   \n",
+       "..               ...              ...             ...                  ...   \n",
+       "306      Fully Meets      (3.99, 4.1)               4                    0   \n",
+       "307              PIP      (3.19, 3.5)               2                    0   \n",
+       "308          Exceeds     (4.52, 4.68)               5                    6   \n",
+       "309      Fully Meets       (5.0, 5.0)               3                    5   \n",
+       "310      Fully Meets      (4.5, 4.52)               5                    0   \n",
+       "\n",
+       "    LastPerformanceReview_Date DaysLateLast30 Absences  \n",
+       "0                    1/17/2019              0        1  \n",
+       "1                         None              0       17  \n",
+       "2                         None              0        3  \n",
+       "3                     1/3/2019              0       15  \n",
+       "4                     2/1/2016              0        2  \n",
+       "..                         ...            ...      ...  \n",
+       "306                  2/28/2019              0       13  \n",
+       "307                       None              5        4  \n",
+       "308                  2/21/2019              0       16  \n",
+       "309                   2/1/2019              0       11  \n",
+       "310                  1/30/2019              0        2  \n",
+       "\n",
+       "[311 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K = 2\n",
+    "MAX_CATEGORICAL_SIZE = 50\n",
+    "BIN_SIZE = 20\n",
+    "SENSITIVITY_MINIMUM = 2\n",
+    "\n",
+    "def column_combinations(df, k):\n",
+    "    return list(combinations(df.columns, k))\n",
+    "\n",
+    "def k_redact(df, k):\n",
+    "    kwise_combinations = column_combinations(df, k) \n",
+    "    \n",
+    "    for columns in kwise_combinations:\n",
+    "        df_search = df.loc[:, columns]\n",
+    "        sensitive_data = [\n",
+    "            (columns, key)\n",
+    "            for key, value\n",
+    "            in df_search.value_counts().to_dict().items()\n",
+    "            if value == 1\n",
+    "            ]\n",
+    "        if not sensitive_data: continue\n",
+    "        for columns, values in sensitive_data:\n",
+    "            for column, value in zip(columns, values):\n",
+    "                df_search = df_search.loc[df[column] == value]\n",
+    "                if df_search.shape[0] == 1:\n",
+    "                    for column in columns:\n",
+    "                        df_search[column] = None\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def sensitive_values(series, sensitivity_minimum):\n",
+    "    return {key\n",
+    "        for key, value\n",
+    "        in series.value_counts().to_dict().items()\n",
+    "        if value < sensitivity_minimum\n",
+    "        }\n",
+    "\n",
+    "def drop_sensitive(series, sensitivity_minimum):\n",
+    "    series.loc[series.isin(sensitive_values(series, sensitivity_minimum))] = None\n",
+    "\n",
+    "def bin_numeric(df, to_process, bin_size, sensitivity_minimum):\n",
+    "    processed = set()\n",
+    "    rows, _ = df.shape\n",
+    "    num_bins = rows//bin_size\n",
+    "    for column_name in to_process:\n",
+    "        column = df[column_name]\n",
+    "        if column.dtype.kind not in \"biufc\": continue\n",
+    "        array = sorted(np.array(column))\n",
+    "        array_min, array_max = array[0], array[-1]\n",
+    "        splits = [array_min] + list(np.array_split(array, num_bins)) + [array_max]\n",
+    "        bins = [\n",
+    "            (np.min(split), np.max(split))\n",
+    "            for split\n",
+    "            in (splits[i] for i in range(num_bins))\n",
+    "            ]\n",
+    "        result = [None] * rows\n",
+    "        for bin_min, bin_max in bins:\n",
+    "            for i, value in enumerate(column):\n",
+    "                if bin_min <= value <= bin_max:\n",
+    "                    result[i] = (bin_min, bin_max)\n",
+    "        df[column_name] = result\n",
+    "        drop_sensitive(df[column_name], sensitivity_minimum)\n",
+    "        processed.add(column_name)\n",
+    "    return df, to_process - processed\n",
+    "\n",
+    "def find_categorical(df, to_process, max_categorical_size, sensitivity_minimum):\n",
+    "    processed = set()\n",
+    "    for column_name in to_process:\n",
+    "        column = df[column_name]\n",
+    "        if column.nunique() <= max_categorical_size:\n",
+    "            drop_sensitive(column, sensitivity_minimum)\n",
+    "            processed.add(column_name)\n",
+    "    return df, to_process - processed\n",
+    "\n",
+    "def redact(df, to_process, sensitivity_minimum):\n",
+    "    processed = set()\n",
+    "    for column_name in to_process:\n",
+    "        column = df[column_name]\n",
+    "        \n",
+    "        is_object = column.dtype == object\n",
+    "        if not is_object: continue\n",
+    "\n",
+    "        # Check if any unique values exist, and redact them\n",
+    "        drop_sensitive(column, sensitivity_minimum)\n",
+    "        processed.add(column_name)\n",
+    "\n",
+    "    return df, to_process - processed\n",
+    "\n",
+    "def anonymize(df, max_categorical_size, bin_size, sensitivity_minimum):\n",
+    "    to_process = set(df.columns)\n",
+    "    df, to_process = redact(df, to_process, sensitivity_minimum)\n",
+    "    df, to_process = find_categorical(df, to_process, max_categorical_size, sensitivity_minimum)\n",
+    "    df, to_process = bin_numeric(df, to_process, bin_size, sensitivity_minimum)\n",
+    "    return df, to_process\n",
+    "\n",
+    "def data_anonymizer(df, k, max_categorical_size, bin_size, sensitivity_minimum):\n",
+    "    start_dtypes = df.dtypes.to_dict()\n",
+    "    df, unprocessed = anonymize(df, max_categorical_size, bin_size, sensitivity_minimum)\n",
+    "    df = k_redact(df, k)\n",
+    "    end_dtypes = df.dtypes.to_dict()\n",
+    "\n",
+    "    # Type correction\n",
+    "    for column in df.columns:\n",
+    "        start_type, end_type  = start_dtypes[column], end_dtypes[column]\n",
+    "        if start_type == end_type: continue\n",
+    "        if start_type.kind == \"i\" and end_type.kind == \"f\":\n",
+    "            df[column] = df[column].astype(\"Int64\")\n",
+    "\n",
+    "    return df, unprocessed\n",
+    "\n",
+    "df, unprocessed_columns = data_anonymizer(df, K, MAX_CATEGORICAL_SIZE, BIN_SIZE, SENSITIVITY_MINIMUM)\n",
+    "if unprocessed_columns: print(f\"Failed to process columns '{unprocessed_columns}'\")\n",
+    "df"
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
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