notebooks/05_concat_fl_dat_analyze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a7194663-9bca-4021-b253-fade1be23a3d",
   "metadata": {},
   "source": [
    "### Concat all HIBP and Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5db6b26-317f-4daf-9d5b-7e4d384b7f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import re\n",
    "import logging\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91243c96-1c4b-4337-a104-f9841ae748a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing JSON files:  85%|███████████████████████████████████████████████████████████████████████▌            | 989560/1162292 [21:53<02:34, 1116.48file/s]IOStream.flush timed out\n",
      "Processing JSON files: 100%|████████████████████████████████████████████████████████████████████████████████████| 1162292/1162292 [25:25<00:00, 761.83file/s]\n"
     ]
    }
   ],
   "source": [
    "# Directory path containing JSON files\n",
    "directory = 'pwned/'\n",
    "\n",
    "logging.basicConfig(filename='error_log_read_json.log', level=logging.ERROR,\n",
    "                    format='%(asctime)s - %(levelname)s - %(message)s')\n",
    "\n",
    "# Get list of JSON file paths in the directory\n",
    "json_files = glob.glob(directory + '/*.json')\n",
    "\n",
    "# Define a function for processing a JSON file\n",
    "def process_json_file(file):\n",
    "    try:\n",
    "        df = pd.read_json(file)\n",
    "        file_name = os.path.basename(file).rstrip('.json')\n",
    "        df['email'] = file_name\n",
    "        return df\n",
    "    except ValueError as e:\n",
    "        logging.error(f\"Error reading file: {file}. Error message: {e}\")\n",
    "        return None\n",
    "\n",
    "# Create a pool of worker processes\n",
    "pool = Pool()\n",
    "\n",
    "# Create a progress bar with tqdm\n",
    "progress_bar = tqdm(total=len(json_files), desc=\"Processing JSON files\", unit=\"file\")\n",
    "\n",
    "# Process JSON files in parallel\n",
    "results = []\n",
    "for df in pool.imap_unordered(process_json_file, json_files):\n",
    "    results.append(df)\n",
    "    progress_bar.update(1)\n",
    "\n",
    "# Close the pool of worker processes\n",
    "pool.close()\n",
    "pool.join()\n",
    "\n",
    "# Close the progress bar\n",
    "progress_bar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "652c7b05-380b-4b7c-829c-d9a41926fff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the list of DataFrames into a single DataFrame\n",
    "combined_df = pd.concat(results, ignore_index=True)\n",
    "del results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e170234-2316-4b26-b101-5b9cecb29220",
   "metadata": {},
   "outputs": [],
   "source": [
    "breaches = pd.read_json('data/breaches.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e037b515-8279-4413-a88a-491163fb3b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8643150, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breached_email_df = pd.merge(combined_df, breaches, on='Name')\n",
    "breached_email_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d785bbfc-e7ab-41ac-a5e9-80b878b2f738",
   "metadata": {},
   "source": [
    "### Let's create an email level dataset for which we have breaches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6568e14-914f-4b02-916b-0b8646c257ab",
   "metadata": {},
   "source": [
    "#### Non-fab\n",
    "https://haveibeenpwned.com/FAQs#FabricatedBreach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a123c0e-ddb8-47f3-83d9-39d21920bb50",
   "metadata": {},
   "outputs": [],
   "source": [
    "breached_email_df['non_fab'] = ~breached_email_df['IsFabricated'].fillna(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e4142c6-d05f-47da-89ea-ed43c6b186b4",
   "metadata": {},
   "source": [
    "#### Serious Dataclasses Breached \n",
    "Either data that will help you hack your other accounts, e.g., Mother's Maiden Name, or sensitive personal information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed7c97c3-fc95-4be8-bae1-e74465de5973",
   "metadata": {},
   "outputs": [],
   "source": [
    "serious_dataclasses = [\"Audio recordings\",\n",
    "\"Auth tokens\", #account cracking\n",
    "\"Bank account numbers\",\n",
    "\"Biometric data\",\n",
    "\"Browsing histories\",\n",
    "\"Chat logs\",\n",
    "\"Credit card CVV\", #fin\n",
    "\"Credit cards\", #fin\n",
    "#\"Credit status information\", # don't know what this means\n",
    "\"Drinking habits\",\n",
    "\"Driver's licenses\",\n",
    "\"Drug habits\",\n",
    "\"Email messages\",\n",
    "\"Encrypted keys\", #account cracking\n",
    "\"Government issued IDs\",\n",
    "#\"Health insurance information\",\n",
    "\"Historical passwords\", #account cracking\n",
    "\"HIV statuses\",\n",
    "#\"Mothers maiden names\", #account cracking\n",
    "\"Partial credit card data\", #fin\n",
    "\"Passport numbers\",\n",
    "\"Password hints\",\n",
    "\"Passwords\", #account cracking\n",
    "\"Personal health data\",\n",
    "\"Photos\",\n",
    "\"PINs\", #account cracking\n",
    "\"Places of birth\", #account cracking\n",
    "\"Private messages\",\n",
    "\"Security questions and answers\", #account cracking\n",
    "\"Sexual fetishes\",\n",
    "\"Sexual orientations\",\n",
    "\"SMS messages\",\n",
    "\"Social security numbers\",\n",
    "\"Taxation records\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "544f4506-c21a-49c2-ad78-084a5b31a0e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "breached_email_df['serious'] = breached_email_df['DataClasses'].apply(lambda x: any(string in x for string in serious_dataclasses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4900d65b-76bb-40cd-8b09-d88409656c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "br_email_df = breached_email_df.assign(count=1).\\\n",
    "    groupby('email').\\\n",
    "    agg(total_breaches = ('count', 'sum'),\n",
    "    serious_breaches   = ('serious', 'sum'),\n",
    "    non_fab_breaches   = ('non_fab', 'sum'),\n",
    "    non_null_uniques   = ('Name', 'nunique')\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c11ade-cc44-44fb-9b7f-422ac4435aa5",
   "metadata": {},
   "source": [
    "### Join to Valid Email List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dc66c78-1197-4235-ae0b-f39ca85c10fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_emails = pd.read_csv(\"data/fl_valid_emails.csv\")\n",
    "# can also do less strict is_valid_email\n",
    "valid_emails = valid_emails[valid_emails['is_valid_email_dns'] == True] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70fef74e-2994-4288-8231-8f046cd4c452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge breaches to parsed\n",
    "valid_emails['email'] = valid_emails['email'].str.lower()\n",
    "br_email_df['email']  = br_email_df['email'].str.lower()\n",
    "fin_df = pd.merge(valid_emails, br_email_df, how = 'left', on = 'email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1dd0f17e-72e6-4a04-9be0-bf97282143f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "fin_df.fillna({'total_breaches': 0, \n",
    "               'serious_breaches': 0,\n",
    "               'non_fab_breaches': 0,\n",
    "               'non_null_uniques': 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41f96b33-91b6-4860-b77f-ce5813a32801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>total_breaches</th>\n",
       "      <th>serious_breaches</th>\n",
       "      <th>non_fab_breaches</th>\n",
       "      <th>non_null_uniques</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1348186.0</td>\n",
       "      <td>1348186.0</td>\n",
       "      <td>1348186.0</td>\n",
       "      <td>1348186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>390.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_breaches  serious_breaches  non_fab_breaches  non_null_uniques\n",
       "count       1348186.0         1348186.0         1348186.0         1348186.0\n",
       "mean              6.2               3.7               6.2               6.2\n",
       "std               5.9               3.9               5.9               5.9\n",
       "min               0.0               0.0               0.0               0.0\n",
       "25%               1.0               1.0               1.0               1.0\n",
       "50%               5.0               3.0               5.0               5.0\n",
       "75%              10.0               6.0              10.0              10.0\n",
       "max             390.0             333.0             389.0             390.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_df[['total_breaches', 'serious_breaches', 'non_fab_breaches', 'non_null_uniques']].describe().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9572cf99-d55e-4d75-9a1a-5486bb90dd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'|       |   total_breaches |   serious_breaches |   non_fab_breaches |   non_null_uniques |\\n|:------|-----------------:|-------------------:|-------------------:|-------------------:|\\n| count |      1.34819e+06 |        1.34819e+06 |        1.34819e+06 |        1.34819e+06 |\\n| mean  |      6.22427     |        3.65431     |        6.22425     |        6.22427     |\\n| std   |      5.865       |        3.90145     |        5.86484     |        5.865       |\\n| min   |      0           |        0           |        0           |        0           |\\n| 25%   |      1           |        1           |        1           |        1           |\\n| 50%   |      5           |        3           |        5           |        5           |\\n| 75%   |     10           |        6           |       10           |       10           |\\n| max   |    390           |      333           |      389           |      390           |'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin_df[['total_breaches', 'serious_breaches', 'non_fab_breaches', 'non_null_uniques']].describe().to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cec5b8a8-3378-429c-8cd4-ecea4d3a3709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8392714358404553"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fin_df['total_breaches'] > 0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff50550c-a52e-462a-8f28-b0188881e36c",
   "metadata": {},
   "source": [
    "### Digital Gap: Sociodemographic Predictors of Breaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9de7c56f-3abc-44f9-ad1b-8bf7e368a2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_summary_statistics(data_df, groupby_column, value_column, percentiles=None):\n",
    "    \"\"\"\n",
    "    Calculate summary statistics for a given DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "        data_df (DataFrame): The DataFrame containing the data.\n",
    "        groupby_column (str): The column to group by.\n",
    "        value_column (str): The column for which to calculate statistics.\n",
    "        percentiles (list, optional): List of percentiles to calculate. Default is None.\n",
    "\n",
    "    Returns:\n",
    "        DataFrame: A DataFrame with the summary statistics.\n",
    "    \"\"\"\n",
    "    if percentiles is None:\n",
    "        percentiles = [25, 50, 75]\n",
    "\n",
    "    # Calculate summary statistics\n",
    "    summary_stats = data_df.groupby(groupby_column)[value_column].agg(['count', 'mean', 'std', 'min', 'max']).round(1)\n",
    "\n",
    "    # Calculate percentile values\n",
    "    percentile_values = data_df.groupby(groupby_column)[value_column].apply(lambda x: pd.Series(np.nanpercentile(x, percentiles), index=percentiles)).unstack().round(1)\n",
    "\n",
    "    # Merge summary statistics and percentile values\n",
    "    summary_with_percentiles = pd.merge(summary_stats, percentile_values, left_index=True, right_index=True)\n",
    "\n",
    "    # Reorder columns\n",
    "    desired_columns = ['count', 'mean', 'std', 'min'] + percentiles + ['max']\n",
    "    summary_with_percentiles = summary_with_percentiles.reindex(columns=desired_columns).reset_index()\n",
    "\n",
    "    return summary_with_percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7f3c3fa-07ef-4d76-8287-7ed63c60d37b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gender   count  mean  std  min   25   50    75    max\n",
      "0      F  721828   6.4  5.8  0.0  2.0  5.0  10.0  157.0\n",
      "1      M  605040   6.0  5.9  0.0  1.0  5.0   9.0  390.0\n",
      "2      U   21296   4.8  6.0  0.0  0.0  3.0   7.0  265.0\n"
     ]
    }
   ],
   "source": [
    "gen_sum = calculate_summary_statistics(fin_df, \n",
    "                                   groupby_column='gender', \n",
    "                                   value_column='total_breaches',\n",
    "                                   percentiles = [25, 50, 75])\n",
    "print(gen_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f56a497-bfbf-4a4c-a14b-fdc68121ef71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'|    | gender   |   count |   mean |   std |   min |   25 |   50 |   75 |   max |\\n|---:|:---------|--------:|-------:|------:|------:|-----:|-----:|-----:|------:|\\n|  0 | F        |  721828 |    6.4 |   5.8 |     0 |    2 |    5 |   10 |   157 |\\n|  1 | M        |  605040 |    6   |   5.9 |     0 |    1 |    5 |    9 |   390 |\\n|  2 | U        |   21296 |    4.8 |   6   |     0 |    0 |    3 |    7 |   265 |'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_sum.to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4fb5a893-0b67-4391-adff-f7085af46ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           race_lit   count  mean  std  min   25   50    75    max\n",
      "0             Asian   30518   5.9  5.8  0.0  1.0  4.0   9.0  154.0\n",
      "1          Hispanic  317399   5.8  5.7  0.0  1.0  4.0   9.0  210.0\n",
      "2      Multi-Racial   10046   5.7  5.8  0.0  1.0  4.0   9.0  145.0\n",
      "3          NH Black  184947   5.8  5.7  0.0  1.0  5.0   9.0  265.0\n",
      "4          NH White  750527   6.5  6.0  0.0  2.0  5.0  10.0  390.0\n",
      "5  Native Americans    3764   5.8  5.7  0.0  1.0  4.0   9.0   42.0\n",
      "6             Other   27028   6.2  5.9  0.0  1.0  5.0   9.0  123.0\n",
      "7           Unknown   23957   5.3  5.6  0.0  1.0  4.0   8.0  195.0\n"
     ]
    }
   ],
   "source": [
    "race_sum = calculate_summary_statistics(fin_df, \n",
    "                                   groupby_column='race_lit', \n",
    "                                   value_column='total_breaches',\n",
    "                                   percentiles = [25, 50, 75])\n",
    "print(race_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c06be44-368d-44ed-b277-be4be421e7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'|    | race_lit         |   count |   mean |   std |   min |   25 |   50 |   75 |   max |\\n|---:|:-----------------|--------:|-------:|------:|------:|-----:|-----:|-----:|------:|\\n|  0 | Asian            |   30518 |    5.9 |   5.8 |     0 |    1 |    4 |    9 |   154 |\\n|  1 | Hispanic         |  317399 |    5.8 |   5.7 |     0 |    1 |    4 |    9 |   210 |\\n|  2 | Multi-Racial     |   10046 |    5.7 |   5.8 |     0 |    1 |    4 |    9 |   145 |\\n|  3 | NH Black         |  184947 |    5.8 |   5.7 |     0 |    1 |    5 |    9 |   265 |\\n|  4 | NH White         |  750527 |    6.5 |   6   |     0 |    2 |    5 |   10 |   390 |\\n|  5 | Native Americans |    3764 |    5.8 |   5.7 |     0 |    1 |    4 |    9 |    42 |\\n|  6 | Other            |   27028 |    6.2 |   5.9 |     0 |    1 |    5 |    9 |   123 |\\n|  7 | Unknown          |   23957 |    5.3 |   5.6 |     0 |    1 |    4 |    8 |   195 |'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_sum.to_markdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b0acd0ff-e533-4020-9422-ee6ac297de6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_lab\n",
       "25--50    596147\n",
       "50--65    330168\n",
       "65+       255495\n",
       "<25       166376\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recode Age\n",
    "\n",
    "fin_df['age'] = 2022 - pd.to_datetime(fin_df['birth_yr'], format='%m/%d/%Y').dt.year\n",
    "age_buckets = [0, 25, 50, 65, 120]\n",
    "labels = ['<25', '25--50', '50--65', '65+'] \n",
    "\n",
    "fin_df['age_lab'] = pd.cut(fin_df['age'], bins = age_buckets, labels = labels)\n",
    "fin_df['age_lab'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5fd95008-3484-43b1-a981-614bab72e7b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  age_lab   count  mean  std  min   25   50    75    max\n",
      "0     <25  166376   4.6  4.9  0.0  1.0  3.0   7.0  265.0\n",
      "1  25--50  596147   6.6  5.9  0.0  2.0  5.0  10.0  390.0\n",
      "2  50--65  330168   6.6  6.1  0.0  2.0  5.0  10.0  210.0\n",
      "3     65+  255495   5.8  5.7  0.0  1.0  4.0   9.0  234.0\n"
     ]
    }
   ],
   "source": [
    "print(calculate_summary_statistics(fin_df, \n",
    "                                   groupby_column='age_lab', \n",
    "                                   value_column='total_breaches',\n",
    "                                   percentiles = [25, 50, 75]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40abe06b-4056-4935-9487-b815f86b10b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "lowess = sm.nonparametric.lowess\n",
    "\n",
    "lowess_model = lowess(fin_df['total_breaches'], fin_df['age'], frac=0.07)\n",
    "\n",
    "# Create the plot using seaborn and matplotlib\n",
    "sns.set_style(\"whitegrid\")  # Set the plot style\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(lowess_model[:, 0], lowess_model[:, 1], color='red', linewidth=2)\n",
    "\n",
    "plt.xlabel('Age', fontsize=14)\n",
    "plt.ylabel('Total Breaches', fontsize=14)\n",
    "plt.title('Lowess Smoothed Plot of Breaches', fontsize=16)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Adjust tick label sizes\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(MultipleLocator(base=10))\n",
    "plt.xlim(min(fin_df['age']), max(fin_df['age']))\n",
    "\n",
    "# Add a light gray background to the plot\n",
    "plt.gca().set_facecolor('#f0f0f0')\n",
    "\n",
    "# Remove right and top spines\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figs/age_breaches.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56cbe4ac-9394-4c4b-afac-e7a0f0811eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's winsorize or we can lowess over medians\n",
    "from scipy.stats.mstats import winsorize\n",
    "winsorized_breaches = winsorize(fin_df['total_breaches'], limits=(0.0, 0.2))\n",
    "\n",
    "lowess_model = lowess(winsorized_breaches, fin_df['age'], frac=0.07)\n",
    "\n",
    "# Create the plot using seaborn and matplotlib\n",
    "sns.set_style(\"whitegrid\")  # Set the plot style\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(lowess_model[:, 0], lowess_model[:, 1], color='red', linewidth=2)\n",
    "\n",
    "plt.xlabel('Age', fontsize=14)\n",
    "plt.ylabel('Total Breaches (Winsorized)', fontsize=14)\n",
    "plt.title('Lowess Smoothed Plot of Winsorized Breaches', fontsize=16)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Adjust tick label sizes\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(MultipleLocator(base=10))\n",
    "plt.xlim(min(fin_df['age']), max(fin_df['age']))\n",
    "\n",
    "# Add a light gray background to the plot\n",
    "plt.gca().set_facecolor('#f0f0f0')\n",
    "\n",
    "# Remove right and top spines\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('figs/age_winsorized_breaches.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94edd18-8805-43b0-b204-b2d71ef9506d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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