scripts/bad_domains.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bad Domains: Visits to Malware/Phishing Sites by Age, Education, and Race\n",
    "\n",
    "We pair passively observed domain level browsing data from comScore with [pydomains](https://github.com/themains/pydomains), a Python package that provides multiple ways to infer the kind of content hosted by a domain, to examine if the old, the less well educated, and minorities more frequently visit (spend more time) on websites implicated in distributing malware or engaged in phishing than their complementary groups.\n",
    "\n",
    "Two caveats. The browsing data is at the machine level. And the demographics data is at the household level.\n",
    "\n",
    "Topline: The most educated most frequently visit (spend most time on) phishing/malware websites. Part of the reason is because they are online more often. When we split the entire sample by race, Asians and Whites more frequently visit (spend more time on) malware/phishing websites than other racial groups. Again, it seems part of the reason is that Asians/Whites spend more time online. When we split by age, we see that the older people more frequently visit (spend most time on) phishing/malware sites. Here there is some evidence that it is because they are choosing worse than younger people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary libraries \n",
    "%matplotlib inline \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as tkr\n",
    "\n",
    "import pandas as pd\n",
    "import gc\n",
    "\n",
    "# set aesthetics\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load browsing data for 2016 grouped by domain and machine ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine_id</th>\n",
       "      <th>domain_name</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17549714</td>\n",
       "      <td>100dayloans.com</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17549714</td>\n",
       "      <td>1fbusa.com</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17549714</td>\n",
       "      <td>2020panel.com</td>\n",
       "      <td>91</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17549714</td>\n",
       "      <td>247-inc.net</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17549714</td>\n",
       "      <td>4salelocal.net</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   machine_id      domain_name  total_time  total_visits\n",
       "0    17549714  100dayloans.com           0             1\n",
       "1    17549714       1fbusa.com          43            18\n",
       "2    17549714    2020panel.com          91            22\n",
       "3    17549714      247-inc.net          46            32\n",
       "4    17549714   4salelocal.net           1             1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YEAR = 2016\n",
    "gdf = pd.read_csv('/opt/data/comscore/pydomains/app2/cs%04d_grp_machine_domain.csv.bz2' % YEAR)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many machines do we have the data from?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81407"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gdf.machine_id.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Kind of Content Hosted by a Domain\n",
    "\n",
    "We use [pydomains](https://github.com/themains/pydomains) to get the kind of content hosted by each of the domains in comScore. (We make the data freely available [here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DXSNFA).) We only load the relevant columns: predictions about whether a domain is engaged in phishing from an LSTM model based on PhishTank data and predictions about whether a domain distributes malware from an LSTM model based on Toulouse data and from an LSTM model based on Malware data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domain_name</th>\n",
       "      <th>pred_phish_2017_prob</th>\n",
       "      <th>pred_malware_2017_prob</th>\n",
       "      <th>pred_toulouse_2017_prob_malware</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>realmadridvsbarcelonalivestream.com</td>\n",
       "      <td>0.999508</td>\n",
       "      <td>0.907737</td>\n",
       "      <td>0.065995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>smartphonerankings.com</td>\n",
       "      <td>0.787181</td>\n",
       "      <td>0.252708</td>\n",
       "      <td>0.004863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sdale.org</td>\n",
       "      <td>0.039262</td>\n",
       "      <td>0.007850</td>\n",
       "      <td>0.009953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>twentyfoursevenrp.com</td>\n",
       "      <td>0.911047</td>\n",
       "      <td>0.979689</td>\n",
       "      <td>0.018030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>beachhousepublishing.com</td>\n",
       "      <td>0.818236</td>\n",
       "      <td>0.579768</td>\n",
       "      <td>0.008558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           domain_name  pred_phish_2017_prob  \\\n",
       "0  realmadridvsbarcelonalivestream.com              0.999508   \n",
       "1               smartphonerankings.com              0.787181   \n",
       "2                            sdale.org              0.039262   \n",
       "3                twentyfoursevenrp.com              0.911047   \n",
       "4             beachhousepublishing.com              0.818236   \n",
       "\n",
       "   pred_malware_2017_prob  pred_toulouse_2017_prob_malware  \n",
       "0                0.907737                         0.065995  \n",
       "1                0.252708                         0.004863  \n",
       "2                0.007850                         0.009953  \n",
       "3                0.979689                         0.018030  \n",
       "4                0.579768                         0.008558  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pydom_df = pd.read_csv('/opt/data/comscore/pydomains/cs%04d_unique_domains_pydomains.csv.bz2' % YEAR, usecols=['domain_names', 'pred_phish_2017_prob', 'pred_toulouse_2017_prob_malware', 'pred_malware_2017_prob'])\n",
    "# rename column\n",
    "pydom_df.rename(columns={'domain_names': 'domain_name'}, inplace=True)\n",
    "pydom_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine_id</th>\n",
       "      <th>domain_name</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>pred_phish_2017_prob</th>\n",
       "      <th>pred_malware_2017_prob</th>\n",
       "      <th>pred_toulouse_2017_prob_malware</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17549714</td>\n",
       "      <td>100dayloans.com</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.608718</td>\n",
       "      <td>0.130488</td>\n",
       "      <td>0.012926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17549714</td>\n",
       "      <td>1fbusa.com</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>0.093295</td>\n",
       "      <td>0.148021</td>\n",
       "      <td>0.025634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17549714</td>\n",
       "      <td>2020panel.com</td>\n",
       "      <td>91</td>\n",
       "      <td>22</td>\n",
       "      <td>0.030941</td>\n",
       "      <td>0.041042</td>\n",
       "      <td>0.009577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17549714</td>\n",
       "      <td>247-inc.net</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>0.172064</td>\n",
       "      <td>0.112687</td>\n",
       "      <td>0.022789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17549714</td>\n",
       "      <td>4salelocal.net</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.202896</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.016723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   machine_id      domain_name  total_time  total_visits  \\\n",
       "0    17549714  100dayloans.com           0             1   \n",
       "1    17549714       1fbusa.com          43            18   \n",
       "2    17549714    2020panel.com          91            22   \n",
       "3    17549714      247-inc.net          46            32   \n",
       "4    17549714   4salelocal.net           1             1   \n",
       "\n",
       "   pred_phish_2017_prob  pred_malware_2017_prob  \\\n",
       "0              0.608718                0.130488   \n",
       "1              0.093295                0.148021   \n",
       "2              0.030941                0.041042   \n",
       "3              0.172064                0.112687   \n",
       "4              0.202896                0.782387   \n",
       "\n",
       "   pred_toulouse_2017_prob_malware  \n",
       "0                         0.012926  \n",
       "1                         0.025634  \n",
       "2                         0.009577  \n",
       "3                         0.022789  \n",
       "4                         0.016723  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Left join\n",
    "pdf = gdf.merge(pydom_df, how='left', on='domain_name')\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['machine_id', 'domain_name', 'total_time', 'total_visits',\n",
       "       'pred_phish_2017_prob', 'pred_malware_2017_prob',\n",
       "       'pred_toulouse_2017_prob_malware'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete to keep the memory from filling up\n",
    "%xdel gdf\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phishing Model\n",
    "\n",
    "For across race, age, and education inferences to hold, measurement error should be orthogonal to race, income, and education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf['total_time_phishing'] = pdf['pred_phish_2017_prob'] * pdf['total_time']\n",
    "pdf['total_visits_phishing'] = pdf['pred_phish_2017_prob'] * pdf['total_visits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Malware Model\n",
    "\n",
    "Same caveat as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf['total_time_malware'] = pdf['pred_malware_2017_prob'] * pdf['total_time']\n",
    "pdf['total_visits_malware'] = pdf['pred_malware_2017_prob'] * pdf['total_visits']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toulouse Model (for malware only)\n",
    "\n",
    "Since we have two measures of Malware, for comparison, for Toulouse, we just use the label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine_id</th>\n",
       "      <th>domain_name</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>pred_phish_2017_prob</th>\n",
       "      <th>pred_malware_2017_prob</th>\n",
       "      <th>pred_toulouse_2017_prob_malware</th>\n",
       "      <th>total_time_phishing</th>\n",
       "      <th>total_visits_phishing</th>\n",
       "      <th>total_time_malware</th>\n",
       "      <th>total_visits_malware</th>\n",
       "      <th>total_time_tl_malware</th>\n",
       "      <th>total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17549714</td>\n",
       "      <td>100dayloans.com</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.608718</td>\n",
       "      <td>0.130488</td>\n",
       "      <td>0.012926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.608718</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130488</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17549714</td>\n",
       "      <td>1fbusa.com</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>0.093295</td>\n",
       "      <td>0.148021</td>\n",
       "      <td>0.025634</td>\n",
       "      <td>4.011689</td>\n",
       "      <td>1.679312</td>\n",
       "      <td>6.364887</td>\n",
       "      <td>2.664371</td>\n",
       "      <td>1.102276</td>\n",
       "      <td>0.461418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17549714</td>\n",
       "      <td>2020panel.com</td>\n",
       "      <td>91</td>\n",
       "      <td>22</td>\n",
       "      <td>0.030941</td>\n",
       "      <td>0.041042</td>\n",
       "      <td>0.009577</td>\n",
       "      <td>2.815621</td>\n",
       "      <td>0.680700</td>\n",
       "      <td>3.734866</td>\n",
       "      <td>0.902935</td>\n",
       "      <td>0.871481</td>\n",
       "      <td>0.210688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17549714</td>\n",
       "      <td>247-inc.net</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>0.172064</td>\n",
       "      <td>0.112687</td>\n",
       "      <td>0.022789</td>\n",
       "      <td>7.914924</td>\n",
       "      <td>5.506034</td>\n",
       "      <td>5.183611</td>\n",
       "      <td>3.605990</td>\n",
       "      <td>1.048310</td>\n",
       "      <td>0.729259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17549714</td>\n",
       "      <td>4salelocal.net</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.202896</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.016723</td>\n",
       "      <td>0.202896</td>\n",
       "      <td>0.202896</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.016723</td>\n",
       "      <td>0.016723</td>\n",
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      ],
      "text/plain": [
       "   machine_id      domain_name  total_time  total_visits  \\\n",
       "0    17549714  100dayloans.com           0             1   \n",
       "1    17549714       1fbusa.com          43            18   \n",
       "2    17549714    2020panel.com          91            22   \n",
       "3    17549714      247-inc.net          46            32   \n",
       "4    17549714   4salelocal.net           1             1   \n",
       "\n",
       "   pred_phish_2017_prob  pred_malware_2017_prob  \\\n",
       "0              0.608718                0.130488   \n",
       "1              0.093295                0.148021   \n",
       "2              0.030941                0.041042   \n",
       "3              0.172064                0.112687   \n",
       "4              0.202896                0.782387   \n",
       "\n",
       "   pred_toulouse_2017_prob_malware  total_time_phishing  \\\n",
       "0                         0.012926             0.000000   \n",
       "1                         0.025634             4.011689   \n",
       "2                         0.009577             2.815621   \n",
       "3                         0.022789             7.914924   \n",
       "4                         0.016723             0.202896   \n",
       "\n",
       "   total_visits_phishing  total_time_malware  total_visits_malware  \\\n",
       "0               0.608718            0.000000              0.130488   \n",
       "1               1.679312            6.364887              2.664371   \n",
       "2               0.680700            3.734866              0.902935   \n",
       "3               5.506034            5.183611              3.605990   \n",
       "4               0.202896            0.782387              0.782387   \n",
       "\n",
       "   total_time_tl_malware  total_visits_tl_malware  \n",
       "0               0.000000                 0.012926  \n",
       "1               1.102276                 0.461418  \n",
       "2               0.871481                 0.210688  \n",
       "3               1.048310                 0.729259  \n",
       "4               0.016723                 0.016723  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pdf['total_time_tl_phishing'] = pdf['pred_toulouse_2017_prob_phishing']* pdf['total_time']\n",
    "#pdf['total_visits_tl_phishing'] = pdf['pred_toulouse_2017_prob_phishing']* pdf['total_visits']\n",
    "\n",
    "pdf['total_time_tl_malware'] = pdf['pred_toulouse_2017_prob_malware'] * pdf['total_time']\n",
    "pdf['total_visits_tl_malware'] = pdf['pred_toulouse_2017_prob_malware'] * pdf['total_visits']\n",
    "\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final dataset is at the machine_id level. We want to know how much time, how many visits, what proportion of time, and what proportion of visits for each person spent on websites implicated in phishing and distributing malware. \n",
    "\n",
    "We start by filtering the data to keep only phishing and malware domains (for each measure). And then simply groupby machine_id. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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_time</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>total_time_phishing</th>\n",
       "      <th>total_visits_phishing</th>\n",
       "      <th>total_time_malware</th>\n",
       "      <th>total_visits_malware</th>\n",
       "      <th>total_time_tl_malware</th>\n",
       "      <th>total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17549714</th>\n",
       "      <td>36193</td>\n",
       "      <td>4147</td>\n",
       "      <td>7707.853361</td>\n",
       "      <td>999.141693</td>\n",
       "      <td>5495.432937</td>\n",
       "      <td>629.030801</td>\n",
       "      <td>697.654959</td>\n",
       "      <td>63.880703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66614909</th>\n",
       "      <td>8240</td>\n",
       "      <td>1540</td>\n",
       "      <td>993.097239</td>\n",
       "      <td>276.081771</td>\n",
       "      <td>572.591542</td>\n",
       "      <td>162.889070</td>\n",
       "      <td>182.696737</td>\n",
       "      <td>24.294017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66859433</th>\n",
       "      <td>138</td>\n",
       "      <td>9</td>\n",
       "      <td>18.703637</td>\n",
       "      <td>1.890376</td>\n",
       "      <td>4.687678</td>\n",
       "      <td>0.773177</td>\n",
       "      <td>6.711733</td>\n",
       "      <td>0.200808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69370447</th>\n",
       "      <td>1810</td>\n",
       "      <td>105</td>\n",
       "      <td>237.190307</td>\n",
       "      <td>21.306237</td>\n",
       "      <td>74.130916</td>\n",
       "      <td>7.105947</td>\n",
       "      <td>11.387083</td>\n",
       "      <td>1.418917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70605319</th>\n",
       "      <td>3099</td>\n",
       "      <td>288</td>\n",
       "      <td>366.957849</td>\n",
       "      <td>49.022182</td>\n",
       "      <td>186.569234</td>\n",
       "      <td>30.742320</td>\n",
       "      <td>47.281493</td>\n",
       "      <td>4.877688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_time  total_visits  total_time_phishing  \\\n",
       "machine_id                                                  \n",
       "17549714         36193          4147          7707.853361   \n",
       "66614909          8240          1540           993.097239   \n",
       "66859433           138             9            18.703637   \n",
       "69370447          1810           105           237.190307   \n",
       "70605319          3099           288           366.957849   \n",
       "\n",
       "            total_visits_phishing  total_time_malware  total_visits_malware  \\\n",
       "machine_id                                                                    \n",
       "17549714               999.141693         5495.432937            629.030801   \n",
       "66614909               276.081771          572.591542            162.889070   \n",
       "66859433                 1.890376            4.687678              0.773177   \n",
       "69370447                21.306237           74.130916              7.105947   \n",
       "70605319                49.022182          186.569234             30.742320   \n",
       "\n",
       "            total_time_tl_malware  total_visits_tl_malware  \n",
       "machine_id                                                  \n",
       "17549714               697.654959                63.880703  \n",
       "66614909               182.696737                24.294017  \n",
       "66859433                 6.711733                 0.200808  \n",
       "69370447                11.387083                 1.418917  \n",
       "70605319                47.281493                 4.877688  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = ['phishing', 'malware', 'tl_malware']\n",
    "aggs = {'total_time': sum, 'total_visits': sum}\n",
    "for c in cats:\n",
    "    aggs['total_time_{0:s}'.format(c)] = sum\n",
    "    aggs['total_visits_{0:s}'.format(c)] = sum\n",
    "gdf = pdf.groupby(['machine_id']).agg(aggs)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load household level demographics data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>machine_id</th>\n",
       "      <th>hoh_most_education</th>\n",
       "      <th>hoh_oldest_age</th>\n",
       "      <th>racial_background</th>\n",
       "      <th>country_of_origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.141700e+04</td>\n",
       "      <td>81417.000000</td>\n",
       "      <td>81417.000000</td>\n",
       "      <td>81417.000000</td>\n",
       "      <td>81417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.824559e+08</td>\n",
       "      <td>37.414508</td>\n",
       "      <td>7.233919</td>\n",
       "      <td>2.162509</td>\n",
       "      <td>0.115676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.349811e+07</td>\n",
       "      <td>46.215620</td>\n",
       "      <td>2.783556</td>\n",
       "      <td>1.916148</td>\n",
       "      <td>0.319838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.754971e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-88.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.731818e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.854283e+08</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.933604e+08</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.011002e+08</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         machine_id  hoh_most_education  hoh_oldest_age  racial_background  \\\n",
       "count  8.141700e+04        81417.000000    81417.000000       81417.000000   \n",
       "mean   1.824559e+08           37.414508        7.233919           2.162509   \n",
       "std    1.349811e+07           46.215620        2.783556           1.916148   \n",
       "min    1.754971e+07            1.000000        1.000000         -88.000000   \n",
       "25%    1.731818e+08            2.000000        5.000000           1.000000   \n",
       "50%    1.854283e+08            4.000000        8.000000           1.000000   \n",
       "75%    1.933604e+08           99.000000        9.000000           3.000000   \n",
       "max    2.011002e+08           99.000000       99.000000           5.000000   \n",
       "\n",
       "       country_of_origin  \n",
       "count       81417.000000  \n",
       "mean            0.115676  \n",
       "std             0.319838  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%             0.000000  \n",
       "75%             0.000000  \n",
       "max             1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_df = pd.read_csv('/opt/data/comscore/demographics_by_machine_id/demographics2016.csv', usecols = ['machine_id', 'racial_background', 'country_of_origin', 'hoh_oldest_age', 'hoh_most_education'])\n",
    "dem_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Demographic Codes to Semantic Labels\n",
    "\n",
    "Let's translate numerical labels to semantic labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White      0.58\n",
       "Other      0.23\n",
       "Black      0.12\n",
       "Asian      0.07\n",
       "Missing    0.00\n",
       "Name: racial_background, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race = {1: 'White', \n",
    "        2: 'Black', \n",
    "        3: 'Asian', \n",
    "        5: 'Other',\n",
    "        -88: 'Missing'}\n",
    "\n",
    "dem_df['racial_background'] = dem_df['racial_background'].replace(race)\n",
    "(dem_df['racial_background'].value_counts()/dem_df['racial_background'].value_counts().sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Non-Hispanic    0.88\n",
       "Hispanic        0.12\n",
       "Name: country_of_origin, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_df['country_of_origin'] = dem_df['country_of_origin'].replace({0: 'Non-Hispanic', \n",
    "                                                                   1: 'Hispanic'})\n",
    "(dem_df['country_of_origin'].value_counts()/dem_df['country_of_origin'].value_counts().sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65 and over    0.15\n",
       "50-54          0.15\n",
       "45-49          0.13\n",
       "55-59          0.11\n",
       "40-44          0.10\n",
       "35-39          0.08\n",
       "60-64          0.08\n",
       "30-34          0.07\n",
       "25-29          0.06\n",
       "21-24          0.03\n",
       "18-20          0.02\n",
       "Missing        0.00\n",
       "Name: hoh_oldest_age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = {1: '18-20', \n",
    "       2: '21-24',\n",
    "       3: '25-29', \n",
    "       4: '30-34',\n",
    "       5: '35-39',\n",
    "       6: '40-44',\n",
    "       7: '45-49',\n",
    "       8: '50-54',\n",
    "       9: '55-59',\n",
    "       10: '60-64',\n",
    "       11: '65 and over',\n",
    "       99: 'Missing'}\n",
    "\n",
    "dem_df['hoh_oldest_age'] = dem_df['hoh_oldest_age'].replace(age)\n",
    "(dem_df['hoh_oldest_age'].value_counts()/dem_df['hoh_oldest_age'].value_counts().sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Missing                              0.36\n",
       "Some college but no degree           0.26\n",
       "Associate degree                     0.21\n",
       "Bachelor’s degree                    0.14\n",
       "High school diploma or equivalent    0.03\n",
       "Graduate degree                      0.01\n",
       "Name: hoh_most_education, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu = {#FIXME: no sample for less than HS in year 2016\n",
    "       #0: 'Less than a high school diploma',\n",
    "       1: 'High school diploma or equivalent', \n",
    "       2: 'Some college but no degree', \n",
    "       3: 'Associate degree', \n",
    "       4: 'Bachelor’s degree',\n",
    "       5: 'Graduate degree',\n",
    "       99: 'Missing'}\n",
    "\n",
    "dem_df['hoh_most_education'] = dem_df['hoh_most_education'].replace(edu)\n",
    "(dem_df['hoh_most_education'].value_counts()/dem_df['hoh_most_education'].value_counts().sum()).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge browsing data with demographics data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf = gdf.merge(dem_df, how='left', on='machine_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given the data are pretty big, we delete gdf and call the garbage collector\n",
    "%xdel gdf\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine_id</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>total_time_phishing</th>\n",
       "      <th>total_visits_phishing</th>\n",
       "      <th>total_time_malware</th>\n",
       "      <th>total_visits_malware</th>\n",
       "      <th>total_time_tl_malware</th>\n",
       "      <th>total_visits_tl_malware</th>\n",
       "      <th>hoh_most_education</th>\n",
       "      <th>hoh_oldest_age</th>\n",
       "      <th>racial_background</th>\n",
       "      <th>country_of_origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17549714</td>\n",
       "      <td>36193</td>\n",
       "      <td>4147</td>\n",
       "      <td>7707.853361</td>\n",
       "      <td>999.141693</td>\n",
       "      <td>5495.432937</td>\n",
       "      <td>629.030801</td>\n",
       "      <td>697.654959</td>\n",
       "      <td>63.880703</td>\n",
       "      <td>Missing</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66614909</td>\n",
       "      <td>8240</td>\n",
       "      <td>1540</td>\n",
       "      <td>993.097239</td>\n",
       "      <td>276.081771</td>\n",
       "      <td>572.591542</td>\n",
       "      <td>162.889070</td>\n",
       "      <td>182.696737</td>\n",
       "      <td>24.294017</td>\n",
       "      <td>Some college but no degree</td>\n",
       "      <td>60-64</td>\n",
       "      <td>White</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66859433</td>\n",
       "      <td>138</td>\n",
       "      <td>9</td>\n",
       "      <td>18.703637</td>\n",
       "      <td>1.890376</td>\n",
       "      <td>4.687678</td>\n",
       "      <td>0.773177</td>\n",
       "      <td>6.711733</td>\n",
       "      <td>0.200808</td>\n",
       "      <td>Missing</td>\n",
       "      <td>25-29</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69370447</td>\n",
       "      <td>1810</td>\n",
       "      <td>105</td>\n",
       "      <td>237.190307</td>\n",
       "      <td>21.306237</td>\n",
       "      <td>74.130916</td>\n",
       "      <td>7.105947</td>\n",
       "      <td>11.387083</td>\n",
       "      <td>1.418917</td>\n",
       "      <td>Missing</td>\n",
       "      <td>55-59</td>\n",
       "      <td>Other</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70605319</td>\n",
       "      <td>3099</td>\n",
       "      <td>288</td>\n",
       "      <td>366.957849</td>\n",
       "      <td>49.022182</td>\n",
       "      <td>186.569234</td>\n",
       "      <td>30.742320</td>\n",
       "      <td>47.281493</td>\n",
       "      <td>4.877688</td>\n",
       "      <td>Missing</td>\n",
       "      <td>21-24</td>\n",
       "      <td>Other</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   machine_id  total_time  total_visits  total_time_phishing  \\\n",
       "0    17549714       36193          4147          7707.853361   \n",
       "1    66614909        8240          1540           993.097239   \n",
       "2    66859433         138             9            18.703637   \n",
       "3    69370447        1810           105           237.190307   \n",
       "4    70605319        3099           288           366.957849   \n",
       "\n",
       "   total_visits_phishing  total_time_malware  total_visits_malware  \\\n",
       "0             999.141693         5495.432937            629.030801   \n",
       "1             276.081771          572.591542            162.889070   \n",
       "2               1.890376            4.687678              0.773177   \n",
       "3              21.306237           74.130916              7.105947   \n",
       "4              49.022182          186.569234             30.742320   \n",
       "\n",
       "   total_time_tl_malware  total_visits_tl_malware          hoh_most_education  \\\n",
       "0             697.654959                63.880703                     Missing   \n",
       "1             182.696737                24.294017  Some college but no degree   \n",
       "2               6.711733                 0.200808                     Missing   \n",
       "3              11.387083                 1.418917                     Missing   \n",
       "4              47.281493                 4.877688                     Missing   \n",
       "\n",
       "  hoh_oldest_age racial_background country_of_origin  \n",
       "0          40-44             White      Non-Hispanic  \n",
       "1          60-64             White          Hispanic  \n",
       "2          25-29             White      Non-Hispanic  \n",
       "3          55-59             Other      Non-Hispanic  \n",
       "4          21-24             Other      Non-Hispanic  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine_id</th>\n",
       "      <th>total_time</th>\n",
       "      <th>total_visits</th>\n",
       "      <th>total_time_phishing</th>\n",
       "      <th>total_visits_phishing</th>\n",
       "      <th>total_time_malware</th>\n",
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       "      <th>total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
       "      <td>81407</td>\n",
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       "      <td>81407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
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       "      <td>14915</td>\n",
       "      <td>981</td>\n",
       "      <td>2332</td>\n",
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       "      <td>1607</td>\n",
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       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>29915</td>\n",
       "      <td>1546</td>\n",
       "      <td>7289</td>\n",
       "      <td>350</td>\n",
       "      <td>5968</td>\n",
       "      <td>222</td>\n",
       "      <td>796</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17549714</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>173182743</td>\n",
       "      <td>1286</td>\n",
       "      <td>146</td>\n",
       "      <td>169</td>\n",
       "      <td>23</td>\n",
       "      <td>109</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>185429062</td>\n",
       "      <td>4998</td>\n",
       "      <td>469</td>\n",
       "      <td>667</td>\n",
       "      <td>78</td>\n",
       "      <td>430</td>\n",
       "      <td>49</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>193360555</td>\n",
       "      <td>15719</td>\n",
       "      <td>1242</td>\n",
       "      <td>2119</td>\n",
       "      <td>218</td>\n",
       "      <td>1378</td>\n",
       "      <td>138</td>\n",
       "      <td>249</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>201100249</td>\n",
       "      <td>1383660</td>\n",
       "      <td>99484</td>\n",
       "      <td>455084</td>\n",
       "      <td>30493</td>\n",
       "      <td>434907</td>\n",
       "      <td>18297</td>\n",
       "      <td>98970</td>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       machine_id  total_time  total_visits  total_time_phishing  \\\n",
       "count       81407       81407         81407                81407   \n",
       "mean    182457976       14915           981                 2332   \n",
       "std      13496576       29915          1546                 7289   \n",
       "min      17549714           0             1                    0   \n",
       "25%     173182743        1286           146                  169   \n",
       "50%     185429062        4998           469                  667   \n",
       "75%     193360555       15719          1242                 2119   \n",
       "max     201100249     1383660         99484               455084   \n",
       "\n",
       "       total_visits_phishing  total_time_malware  total_visits_malware  \\\n",
       "count                  81407               81407                 81407   \n",
       "mean                     180                1607                   115   \n",
       "std                      350                5968                   222   \n",
       "min                        0                   0                     0   \n",
       "25%                       23                 109                    14   \n",
       "50%                       78                 430                    49   \n",
       "75%                      218                1378                   138   \n",
       "max                    30493              434907                 18297   \n",
       "\n",
       "       total_time_tl_malware  total_visits_tl_malware  \n",
       "count                  81407                    81407  \n",
       "mean                     260                       16  \n",
       "std                      796                       26  \n",
       "min                        0                        0  \n",
       "25%                       19                        2  \n",
       "50%                       76                        7  \n",
       "75%                      249                       20  \n",
       "max                    98970                     1340  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.describe().astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "#### By Education\n",
    "\n",
    "We start by tracking total time spent and total number of visits to phishing and malware sites by education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_time_phishing</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_visits_phishing</th>\n",
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       "      <th></th>\n",
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       "      <th>hoh_most_education</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Associate degree</th>\n",
       "      <td>16810</td>\n",
       "      <td>2821</td>\n",
       "      <td>9036</td>\n",
       "      <td>0</td>\n",
       "      <td>336</td>\n",
       "      <td>991</td>\n",
       "      <td>2664</td>\n",
       "      <td>455084</td>\n",
       "      <td>16810</td>\n",
       "      <td>220</td>\n",
       "      <td>439</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>116</td>\n",
       "      <td>275</td>\n",
       "      <td>30493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor’s degree</th>\n",
       "      <td>11342</td>\n",
       "      <td>2977</td>\n",
       "      <td>6680</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>1146</td>\n",
       "      <td>2996</td>\n",
       "      <td>151887</td>\n",
       "      <td>11342</td>\n",
       "      <td>246</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>137</td>\n",
       "      <td>318</td>\n",
       "      <td>6925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate degree</th>\n",
       "      <td>539</td>\n",
       "      <td>3292</td>\n",
       "      <td>6433</td>\n",
       "      <td>0</td>\n",
       "      <td>399</td>\n",
       "      <td>1415</td>\n",
       "      <td>3722</td>\n",
       "      <td>88502</td>\n",
       "      <td>539</td>\n",
       "      <td>277</td>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>168</td>\n",
       "      <td>399</td>\n",
       "      <td>2228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school diploma or equivalent</th>\n",
       "      <td>2489</td>\n",
       "      <td>2276</td>\n",
       "      <td>6135</td>\n",
       "      <td>0</td>\n",
       "      <td>249</td>\n",
       "      <td>759</td>\n",
       "      <td>2168</td>\n",
       "      <td>190064</td>\n",
       "      <td>2489</td>\n",
       "      <td>172</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>86</td>\n",
       "      <td>211</td>\n",
       "      <td>9378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>29319</td>\n",
       "      <td>1532</td>\n",
       "      <td>5636</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>265</td>\n",
       "      <td>1075</td>\n",
       "      <td>335871</td>\n",
       "      <td>29319</td>\n",
       "      <td>110</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>110</td>\n",
       "      <td>22199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college but no degree</th>\n",
       "      <td>20908</td>\n",
       "      <td>2691</td>\n",
       "      <td>8092</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "      <td>898</td>\n",
       "      <td>2538</td>\n",
       "      <td>406021</td>\n",
       "      <td>20908</td>\n",
       "      <td>210</td>\n",
       "      <td>362</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>103</td>\n",
       "      <td>257</td>\n",
       "      <td>26028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  total_time_phishing                       \\\n",
       "                                                count  mean   std min  25%   \n",
       "hoh_most_education                                                           \n",
       "Associate degree                                16810  2821  9036   0  336   \n",
       "Bachelor’s degree                               11342  2977  6680   0  376   \n",
       "Graduate degree                                   539  3292  6433   0  399   \n",
       "High school diploma or equivalent                2489  2276  6135   0  249   \n",
       "Missing                                         29319  1532  5636   0   55   \n",
       "Some college but no degree                      20908  2691  8092   0  297   \n",
       "\n",
       "                                                      total_visits_phishing  \\\n",
       "                                    50%   75%     max                 count   \n",
       "hoh_most_education                                                            \n",
       "Associate degree                    991  2664  455084                 16810   \n",
       "Bachelor’s degree                  1146  2996  151887                 11342   \n",
       "Graduate degree                    1415  3722   88502                   539   \n",
       "High school diploma or equivalent   759  2168  190064                  2489   \n",
       "Missing                             265  1075  335871                 29319   \n",
       "Some college but no degree          898  2538  406021                 20908   \n",
       "\n",
       "                                                                      \n",
       "                                  mean  std min 25%  50%  75%    max  \n",
       "hoh_most_education                                                    \n",
       "Associate degree                   220  439   0  42  116  275  30493  \n",
       "Bachelor’s degree                  246  323   0  48  137  318   6925  \n",
       "Graduate degree                    277  313   0  52  168  399   2228  \n",
       "High school diploma or equivalent  172  308   0  32   86  211   9378  \n",
       "Missing                            110  278   0   9   33  110  22199  \n",
       "Some college but no degree         210  362   0  38  103  257  26028  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc = mdf.groupby(['hoh_most_education'])['total_time_phishing', 'total_visits_phishing'].describe().astype(int)\n",
    "abc.to_csv('./bad_educ.csv')\n",
    "mdf.groupby(['hoh_most_education'])['total_time_phishing', 'total_visits_phishing'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing that jumps out is the sharp right skew. Given the skew, we focus on the medians. There a slightly surprising pattern emerges: the greater the education level of the most educated person in the household, the more frequent the visits (identified by the median but holds for 75th percentile) to phishing sites. For instance, households where graduate education is the highest level of education visit phishing related sites more often (median = 8) than households where the most educated person just has a bachelor's degree (median = 7). When we look at the time, the pattern is slightly less clear but consistent.\n",
    "\n",
    "Looking at visits and time spent on malware sites (see below), the pattern is broadly the same. And this is true regardless of what way we measure malware sites---Toulouse or Malware data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_time_malware</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_visits_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hoh_most_education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Associate degree</th>\n",
       "      <td>16810</td>\n",
       "      <td>1926</td>\n",
       "      <td>7594</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>634</td>\n",
       "      <td>1745</td>\n",
       "      <td>434907</td>\n",
       "      <td>16810</td>\n",
       "      <td>139</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>73</td>\n",
       "      <td>173</td>\n",
       "      <td>18297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor’s degree</th>\n",
       "      <td>11342</td>\n",
       "      <td>1982</td>\n",
       "      <td>4878</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>727</td>\n",
       "      <td>1964</td>\n",
       "      <td>150629</td>\n",
       "      <td>11342</td>\n",
       "      <td>154</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>85</td>\n",
       "      <td>200</td>\n",
       "      <td>4641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate degree</th>\n",
       "      <td>539</td>\n",
       "      <td>2176</td>\n",
       "      <td>4426</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>886</td>\n",
       "      <td>2374</td>\n",
       "      <td>51478</td>\n",
       "      <td>539</td>\n",
       "      <td>176</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>104</td>\n",
       "      <td>249</td>\n",
       "      <td>1378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school diploma or equivalent</th>\n",
       "      <td>2489</td>\n",
       "      <td>1582</td>\n",
       "      <td>4518</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>485</td>\n",
       "      <td>1425</td>\n",
       "      <td>122099</td>\n",
       "      <td>2489</td>\n",
       "      <td>109</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>54</td>\n",
       "      <td>133</td>\n",
       "      <td>6076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>29319</td>\n",
       "      <td>1053</td>\n",
       "      <td>4356</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>174</td>\n",
       "      <td>711</td>\n",
       "      <td>245677</td>\n",
       "      <td>29319</td>\n",
       "      <td>71</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>71</td>\n",
       "      <td>13899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college but no degree</th>\n",
       "      <td>20908</td>\n",
       "      <td>1912</td>\n",
       "      <td>7012</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>576</td>\n",
       "      <td>1679</td>\n",
       "      <td>401863</td>\n",
       "      <td>20908</td>\n",
       "      <td>134</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>164</td>\n",
       "      <td>16532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  total_time_malware                       \\\n",
       "                                               count  mean   std min  25%   \n",
       "hoh_most_education                                                          \n",
       "Associate degree                               16810  1926  7594   0  213   \n",
       "Bachelor’s degree                              11342  1982  4878   0  239   \n",
       "Graduate degree                                  539  2176  4426   0  250   \n",
       "High school diploma or equivalent               2489  1582  4518   0  156   \n",
       "Missing                                        29319  1053  4356   0   36   \n",
       "Some college but no degree                     20908  1912  7012   0  191   \n",
       "\n",
       "                                                     total_visits_malware  \\\n",
       "                                   50%   75%     max                count   \n",
       "hoh_most_education                                                          \n",
       "Associate degree                   634  1745  434907                16810   \n",
       "Bachelor’s degree                  727  1964  150629                11342   \n",
       "Graduate degree                    886  2374   51478                  539   \n",
       "High school diploma or equivalent  485  1425  122099                 2489   \n",
       "Missing                            174   711  245677                29319   \n",
       "Some college but no degree         576  1679  401863                20908   \n",
       "\n",
       "                                                                      \n",
       "                                  mean  std min 25%  50%  75%    max  \n",
       "hoh_most_education                                                    \n",
       "Associate degree                   139  271   0  26   73  173  18297  \n",
       "Bachelor’s degree                  154  205   0  30   85  200   4641  \n",
       "Graduate degree                    176  205   0  31  104  249   1378  \n",
       "High school diploma or equivalent  109  198   0  20   54  133   6076  \n",
       "Missing                             71  178   0   6   21   71  13899  \n",
       "Some college but no degree         134  233   0  24   65  164  16532  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_most_education'])['total_time_malware', 'total_visits_malware'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=['total_time_phishing', 'total_time_malware'], var_name='model', value_name='total_time')\n",
    "adf['model'] = adf['model'].replace({'total_time_phishing':'Phishing (PhishTank)', 'total_time_malware':'Malware (Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"total_time\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total time spent on Phishing/Malware domains')\n",
    "\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_time_phishing_malware_educ.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=['total_visits_phishing', 'total_visits_malware'], var_name='model', value_name='total_visits')\n",
    "adf['model'] = adf['model'].replace({'total_visits_phishing':'Phishing (PhishTank)', 'total_visits_malware':'Malware (Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"total_visits\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total visits to Phishing/Malware domains')\n",
    "\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_visits_phishing_malware_educ.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">total_time_tl_malware</th>\n",
       "      <th colspan=\"9\" halign=\"left\">total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>85%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>85%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hoh_most_education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Associate degree</th>\n",
       "      <td>16810</td>\n",
       "      <td>317</td>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>114</td>\n",
       "      <td>321</td>\n",
       "      <td>533</td>\n",
       "      <td>77749</td>\n",
       "      <td>16810</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor’s degree</th>\n",
       "      <td>11342</td>\n",
       "      <td>360</td>\n",
       "      <td>920</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>136</td>\n",
       "      <td>379</td>\n",
       "      <td>621</td>\n",
       "      <td>51806</td>\n",
       "      <td>11342</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate degree</th>\n",
       "      <td>539</td>\n",
       "      <td>379</td>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>185</td>\n",
       "      <td>449</td>\n",
       "      <td>740</td>\n",
       "      <td>6802</td>\n",
       "      <td>539</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school diploma or equivalent</th>\n",
       "      <td>2489</td>\n",
       "      <td>247</td>\n",
       "      <td>498</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>86</td>\n",
       "      <td>252</td>\n",
       "      <td>456</td>\n",
       "      <td>12114</td>\n",
       "      <td>2489</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>29319</td>\n",
       "      <td>162</td>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>119</td>\n",
       "      <td>244</td>\n",
       "      <td>53732</td>\n",
       "      <td>29319</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college but no degree</th>\n",
       "      <td>20908</td>\n",
       "      <td>299</td>\n",
       "      <td>917</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>102</td>\n",
       "      <td>298</td>\n",
       "      <td>523</td>\n",
       "      <td>98970</td>\n",
       "      <td>20908</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  total_time_tl_malware                    \\\n",
       "                                                  count mean  std min 25%   \n",
       "hoh_most_education                                                          \n",
       "Associate degree                                  16810  317  901   0  38   \n",
       "Bachelor’s degree                                 11342  360  920   0  44   \n",
       "Graduate degree                                     539  379  587   0  50   \n",
       "High school diploma or equivalent                  2489  247  498   0  29   \n",
       "Missing                                           29319  162  570   0   6   \n",
       "Some college but no degree                        20908  299  917   0  34   \n",
       "\n",
       "                                                         \\\n",
       "                                   50%  75%  85%    max   \n",
       "hoh_most_education                                        \n",
       "Associate degree                   114  321  533  77749   \n",
       "Bachelor’s degree                  136  379  621  51806   \n",
       "Graduate degree                    185  449  740   6802   \n",
       "High school diploma or equivalent   86  252  456  12114   \n",
       "Missing                             30  119  244  53732   \n",
       "Some college but no degree         102  298  523  98970   \n",
       "\n",
       "                                  total_visits_tl_malware                   \\\n",
       "                                                    count mean std min 25%   \n",
       "hoh_most_education                                                           \n",
       "Associate degree                                    16810   20  30   0   4   \n",
       "Bachelor’s degree                                   11342   22  28   0   5   \n",
       "Graduate degree                                       539   26  32   0   5   \n",
       "High school diploma or equivalent                    2489   16  24   0   3   \n",
       "Missing                                             29319   10  21   0   1   \n",
       "Some college but no degree                          20908   18  26   0   4   \n",
       "\n",
       "                                                     \n",
       "                                  50% 75% 85%   max  \n",
       "hoh_most_education                                   \n",
       "Associate degree                   11  25  37  1340  \n",
       "Bachelor’s degree                  13  30  43   527  \n",
       "Graduate degree                    16  35  50   327  \n",
       "High school diploma or equivalent   8  19  29   679  \n",
       "Missing                             3  10  18   870  \n",
       "Some college but no degree         10  24  36   956  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_most_education'])['total_time_tl_malware', 'total_visits_tl_malware'].describe(percentiles = [.25, .5, .75, .85]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=['total_time_tl_malware'], var_name='model', value_name='total_time')\n",
    "g = sns.catplot(x=\"total_time\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('Malware (Toulouse)');\n",
    "g.set_xlabels('Total time spent on Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_time_tl_malware_educ.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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/X/L6BAUFsXr1arp06UJ4eDjfffddhcb4/aV5JSwWC5MnT2bcuHGlHl+/fn25x3JycsJisQBQUFBwxW169+5NWloaJpOJnTt3lpoRnDhxIoMHD2bv3r1XnMkrLi5my5YtNGzYsMxzv3+/i4qKys0pIiIiInKzKmUJ8KZNm9K4cWN+/PFHAGJiYiq0f05ODtnZ2QwaNIhZs2ZZZ1L69+/Pxo0bATCbzWRnZ5d7DFdXV5o2bcr+/fsBMBqNeHp6lvv41c7F1dXVun1kZGS523722Wfk5+eTlZXFd999V2YBgz59+lj3P378OKdPn6Zdu3bXejmscnJyuP322yksLCw3R58+fayXBe7du5fmzZtbZ6jK4+XlxbZt26wzNZmZmZw7dw5PT0927tzJpUuXuHjxIl988YV1n9atW1tnEMtbEdDBwYEhQ4awePFi2rdvT/PmzQHIzs6mZcuWAOWuhujl5cU///lP69dJSUlXPYfGjRtb84uIiIiIVKZKm0l67bXXeOWVV3B0dMTT0/Oaf1G3lZOTwzPPPGOdEQoKCgJg9uzZzJkzh23btuHo6Mj8+fO5/fbbyz3OG2+8YV2gwd3dncWLF1/18fIsXryYWbNm4eDgUGrhht/r3LkzAQEBZGVl8cwzz9CyZUtOnjxpfX78+PHMnz8fPz8/nJycWLx4calZkWuZOnUqDz/8MAaDgZ49e16xFDz77LPMmjULPz8/GjVqxOuvv37N43p5eXHs2DHrTJKLiwtLly6lW7du+Pr64u/vj8FgKFX6nnjiCaZNm8aWLVsYNGhQucf29fVl7NixpXI8++yzTJ06ldtuu41+/fqVeo1KzJ49m+DgYPz8/DCbzfTp0+eqy4M3b96c++67j+HDhzNw4EDdlyQiIiIilcbBUnIN1U3KycmhcePGALz77rucOXOm1M33tY3tyn61VW05x6SkJOs9bNUlMDCQ3NzcKy5SUdnjHDj2K5furbmfzeV8+PIqlzU5463C+XAU9+ueJKkDSu5J0ve6iNhLpc0kffXVV6xZswaz2cxdd911XTMaIiIiIiIiNU2llSRfX1/ramZ1wXPPPWfvCFWuLpxjVfH19bV+5pOIiFRMXfr7hIjUTPoUSpEq4OPjc83FJ0RE5Mp8fHzsHUFE6rhKWd1ORERERESktlBJEhERERERsaGSJCIiIiIiYkMlSURERERExIYWbhC5xTnmmqyfRVQTOeaeA6jRGW8VjrkmoJW9Y4iIiNR6Kkkit7AOHTrYO8I1mUwNADAYDHZOUhu0uiXecxERkVudSpLILazkU+lFREREpPLoniQREREREREbKkkiIiIiIiI2VJJERERERERsqCSJiIiIiIjYUEkSERERERGxodXtRGqRkJAQUlJS7B2jwkwmE6Blwmu6Dh06aEVFERGpE1SSRGqRlJQUfk78nrubmO0dpULOZjsB4HLhmJ2TSHnSLjrZO4KIiEi1UUkSqWXubmLmlT4X7R2jQl7d3wTglstdl5S8RyIiInWB7kkSERERERGxoZIkIiIiIiJiQyVJRERERETEhkqSiIiIiIiIDZUkkSoQFxfHnj177B1DRERqobi4OOLi4uwdQ6RWU0kSqQIxMTHs3r3b3jFERKQWiomJISYmxt4xRGo1lSQREREREREbKkkiIiIiIiI2VJJERERERERs1OiSFBYWxrBhw/Dz88Pf358ff/zR3pGuKDw8nODgYABCQ0NZt25dlY7Xu3fvCm2flJTEV199VUVp/mvv3r089dRTVT6OiIiIiEhVqmfvAOVJSEjgyy+/5JNPPqFBgwaYTCYKCwvtHeuWlJSURGJiIoMGDbJ3lOtmNptxcnKydwwRERERqYNqbEk6e/YszZs3p0GDBgAYDAbrc3v27OGNN97AbDbTvXt3FixYQIMGDfD29mbYsGF8/fXXODk5sXDhQpYvX05qaiqTJk3i0UcfBWDt2rXExsZSUFDA0KFDCQwMLDP+119/zVtvvYXZbKZ58+Z88MEHnD9/nlmzZpGenk6jRo0IDg6mS5cu5Z5DWloaCxYsICsrC2dnZxYuXEj79u1JS0tjxowZ5OXl4e3tzYYNG0hISLjubACLFi0iPj6eFi1a8NZbb2EwGJgwYQIzZ87Ew8MDk8nE2LFjiYuLIyQkhEuXLnHgwAGeeuopfH19rccJDw/n888/Jy8vj/T0dIYMGcLMmTMBiIqKYs2aNVgsFgYNGsSLL754xddp0aJFNGrUiPvvv9/6eG5uLgsXLiQ5OZmioiKeffZZhgwZQl5eHkFBQSQnJ3PPPfdw5swZ5s6di4eHB7179+aRRx5h9+7dzJ07l4yMDP75z39SWFhIz549mTdvHk5OTuzatYvQ0FAKCgpwd3dn8eLFNG7cuNz3wR5MJhNnz54t9/2rKsnJyTS1OFTrmFI3/KfAgfTk5Gr/nhaRspKTk3Fzc7N3DJFarcZebjdgwABOnz7NQw89xPz58/nuu+8AyM/PJygoiLfeeovIyEjMZjMbN2607nfnnXdiNBrp06cPQUFBrFixgi1bthAaGgrArl27SE1NZevWrRiNRg4dOsS+fftKjW0ymZgzZw4hISFs376dFStWAJcvpbv33nuJjIzk+eef56WXXrrqOcyZM4c5c+YQHh7OSy+9xIIFCwB47bXXCAgIIDIyklatWlm3v55scLmAdO/enejoaDw9PVm5cmW5GRo0aEBgYCC+vr4YjcZSBalEUlISb7/9NpGRkcTGxnL69GkyMzNZtmwZH3zwARERERw8eJCdO3eW2i8/P585c+bwzjvvEB4eztmzZ63PvfPOOzzwwANs3bqVDRs2sHTpUnJzc9m4cSO33XYbMTExTJ06lUOHDpU6rx49erB9+3aaN29ObGwsmzZtwmg04ujoSGRkJCaTibCwMN5//30++eQTunfvzvvvv3/V90FEREREpCJq7ExS48aNCQ8PZ//+/ezdu5fnn3+e6dOnc++999KmTRvuueceAEaNGsVHH33ExIkTARg8eDAAnTp1Ijc3lyZNmgCXy8KFCxeIj48nPj6ekSNHApf/Yn7ixAk8PT2tY//www/06dMHd3d3AJo1awbAgQMHrGWrf//+nD9/nosXL14xf05ODgkJCUydOtX6WEFBgfX4q1atAsDPz48lS5YAXFc2AEdHR2vZ8ff359lnn63Yi/s7/fv3x9XVFYD27duTkZHB+fPn6du3r3UGz8/Pj3379jFkyBDrfr/88gtt2rShbdu2AIwYMYItW7YAlwvf559/znvvvQdcLlSnT5/mwIEDBAQEAJffo86dO1uP5+TkxEMPPQRcni1MTExk7NixAFy6dAk3Nzd+/PFHUlJSrLOChYWF9OrV66bOvyoYDAacnZ0JCQmp1nEDAwO5dKJssRa5Wbc1sNCybcdq/54WkbI0oytS9WpsSYLLf2nu168f/fr1o1OnTkRERHDvvfdedZ/69esDl4tEyaV6JV8XFRVhsViYPHky48aNq9LsFouFpk2bYjQaK7TPjWRzcLh8eZWTkxMWiwX4byG7Hravk5OTE2azuULjlyckJIR27dpd9/YNGza03odksVgYNWoU06dPL7XN559/zoABA1i+fHmlZBQRERER+b0ae7ndL7/8wokTJ6xfJyUlcdddd3HPPfeQkZFBamoqAEajscxMy9V4eXmxbds2cnJyAMjMzOTcuXOltunVqxf79+8nPT0dgPPnzwPQp08ftm/fDlxeya158+bWmarfa9KkCW3atCE2Nha4/Jf+I0eOANCzZ08+/fRTAKKjoyuUDaC4uJgdO3YAEBkZab0XqHXr1iQmJgIQFxdn3b5x48bWY16vHj16sG/fPkwmE2az2Xppn6127dqRkZFBWlraFc/lww8/tJa2w4cPA3DfffdZX5OUlBR+/vnnK47fv39/duzYYT3/8+fPk5GRQa9evfj++++t739ubi7Hjx+v0LmJiIiIiFxNjZ1Jys3N5dVXX+XChQs4OTnxhz/8geDgYBo2bMjixYuZOnWqdeGGkkuvroeXlxfHjh2zzta4uLiwdOnSUjdAGgwGgoODee655yguLsbNzY3333+fZ599llmzZuHn50ejRo14/fXXrzrW0qVLmT9/PmFhYRQVFeHr60uXLl2YNWsWL774ImFhYQwcONBatK4nW8njP/30E2FhYRgMBt5++20AnnjiCaZNm8aWLVtKrWTXr18/3n33Xfz9/css3FCeO+64g+nTp/P4449bF26wvdQOLs/8BAcHM3nyZOvCDSVl7JlnnmHRokWMGDGC4uJi2rRpw5o1axg/fjxBQUH4+vrSrl07OnToYL3Uz1aHDh2YNm0aTzzxBMXFxdSvX5+5c+fSq1cvFi9ezAsvvGCdLZs2bZr18ksRERERkZvlYCn5p36pNnl5eTg7O+Pg4EB0dDRRUVGEhYXZO1a1MJvNFBUV0bBhQ9LS0pg4cSJxcXGlLvmrbElJSXTt2rXKjn8lgYGB5Obmsnbt2mof99KJfbzS58r3ytVUr+6//A8Ft1ruuuTV/U1wbuupe5JEaoCSe5L08yhSdWrsTFJtdujQIYKDg633LS1atMjekapNXl4eAQEB1vvD5s2bV6UFSURERESkolSS7MD23qa6pkmTJoSHh9s7hoiIiIhIuVSSRKqAr68vp06dsncMERGpha7n3mIRuTkqSSJVwMfHh6SkJHvHEBGRWsjHx8feEURqvRq7BLiIiIiIiIg9qCSJiIiIiIjYUEkSERERERGxoZIkIiIiIiJiQws3iNQyaRedrB/OeqtIzXYCuOVy1yVpF53oZO8QIiIi1UQlSaQW6dChg70j3JDbTSYAnA0GOyeR8nTi1v3+EhERqSiVJJFaJDAw0N4RRERERG55uidJRERERETEhkqSiIiIiIiIDZUkERERERERGypJIiIiIiIiNrRwg0gtFRISQkpKir1jVCrT/62CZ9AqeLeMDh06aEERERG55agkidRSKSkpJBxKgGb2TlKJzl/+JTUv1b455Pqct3cAERGRG6OSJFKbNYPiPxXbO0Wlcfzy8hXCtemcarOS90tERORWo/+DiYiIiIiI2FBJEhERERERsaGSJCIiIiIiYkMlSURERERExIZKkoiIiIiIiA2VJJEqEBcXx549e+wdQ0RE5KbExcURFxdn7xgi1U4lSaQKxMTEsHv3bnvHEBERuSkxMTHExMTYO4ZItVNJEhERERERsaGSJCIiIiIiYkMlSURERERExIZKkp3s3LmTzp07c+zYsSofKzMzk8DAwKtuc/LkSSIjI29qnNDQUNatW3dTxxARERERsTeVJDuJiori/vvvJzo6usrHatmyJSEhIVfdJiMjg6ioqCrPcj2KiorsHUFERERE6rB69g5QF+Xk5HDgwAE2bNjA008/bZ3lOXPmDM8//zwXL17EbDYzf/58evfuzezZs0lMTMTBwYExY8YwceJEkpKSmDdvHnl5edx9990sWrSI2267jdTUVObNm4fJZMLJyYkVK1bg6OjI008/TVRUFCdPnmTmzJnk5eUBMGfOHO677z7efPNNjh07hr+/P6NGjWLChAksW7aM7777joKCAh577DHGjRtX5lzCwsKIiIjAYDBw55130q1bNwDS0tJYsGABWVlZODs7s3DhQtq3b09aWhozZswgLy8Pb29vNmzYQEJCAnv37mXFihU0bdqU48ePExMTU+74a9euJTY2loKCAoYOHXrNWTJ7MJlMnD171q7ZkpOT9c8gYl+XLn8f1sSfURG5PsnJybi5udk7hki1U0myg88++4yBAwdyzz330Lx5cxITE+nevTtRUVF4eXkxZcoUzGYzeXl5JCUlkZmZaZ3luXDhAgAzZ85kzpw59O3blxUrVrBy5Upmz57NjBkzmDx5MkOHDiU/P5/i4mLOnTtnHdvNzY3333+fhg0bcuLECV544QXCw8OZPn067733HmvWrAHg448/xtXVlW3btlFQUMC4ceMYMGAA7u7u1mMlJiYSExNDREQEZrOZUaNGWUvSnDlzWLBgAW3btuXHH39kwYIFbNiwgddee42AgACGDx/Opk2bSr0uhw8fJjIyEnd393LHT01NJTU1la1bt2KxWJgyZQr79u3D08VvnV4AACAASURBVNOzSt8zEREREak7VJLsIDo6moCAAAB8fX2Jjo6me/fueHh4MGvWLIqKihgyZAhdu3bF3d2d9PR0Fi5cyKBBg/Dy8iI7O5vs7Gz69u0LwKhRo5g6dSoXL14kMzOToUOHAtCwYcMyYxcVFREcHMyRI0dwdHTkxIkTV8wYHx/P0aNH2bFjBwDZ2dmkpqaWKkn79+9nyJAhNGrUCABvb2/g8kxZQkICU6dOtW5bUFAAwA8//MCqVasA8PPzY8mSJdZtPDw8rMcvb/z4+Hji4+MZOXIkALm5uZw4caLGlSSDwYCzs/M1L3OsSoGBgSRkJNhtfBGcoWPrjnb9ORCRm6OZYKmrVJKq2fnz5/n222/5+eefcXBwwGw24+DgwMyZM/H09OTDDz/kq6++IigoiL/97W+MHDkSo9HIrl272Lx5M7GxscyaNeuGx1+/fj0tWrTAaDRSXFxMjx49rridxWLhlVdeYeDAgRUew2Kx0LRpU4xGY4X2c3Fxueb4u3btYvLkyVe89E9EREREpDLojoVqtmPHDvz9/fniiy/4/PPP+eqrr2jTpg379+8nIyODFi1a8Je//IWHH36YQ4cOYTKZsFgsPPTQQ0ybNo3Dhw/j6upK06ZN2b9/PwBGoxFPT0+aNGlCq1at2LlzJ3B59qbk3qMS2dnZ3H777Tg6OmI0GjGbzQA0btyYnJwc63ZeXl5s2rSJwsJCAI4fP05ubm6pY3l6erJz504uXbrExYsX+eKLLwBo0qQJbdq0ITY2FrhceI4cOQJAz549+fTTTwGuumhFeeN7eXmxbds2a9bMzMxSlxOKiIiIiNwszSRVs6ioKP73f/+31GMPPvggUVFR9OrVi3Xr1lGvXj1cXFx44403OHPmDC+//DLFxcUAvPDCCwC88cYb1oUb3N3dWbx4MQBLlixh7ty5rFixgvr167NixQocHBysY40fP57nnnuOiIgIBg4caJ296dy5M46OjowYMYLRo0cTEBBARkYGo0ePxmKx0Lx5c1avXl0qd7du3fD19cXf3x+DwYCHh4f1uaVLlzJ//nzCwsIoKirC19eXLl26MGvWLF588UXCwsIYOHAgTZo0ueLr9PDDD19xfC8vL44dO2adSXJxcWHp0qW6qVREREREKo2DxWKx2DuE1B15eXk4Ozvj4OBAdHQ0UVFRhIWFVemYSUlJdO3atUrH+L3AwEByc3NZu3ZttY77+wwJGQkU/6nYbhkqm+OXlye/a9M51WaOXzrSu3Vv3ZMkcgsruSdJP8dS12gmSarVoUOHCA4Ott63tGjRIntHEhEREREpRSVJqlWfPn3Yvn27vWOIiIiIiJRLJUmkCvj6+nLq1Cl7xxAREbkpvr6+9o4gYhcqSSJVwMfHh6SkJHvHEBERuSk+Pj72jiBiF1oCXERERERExIZKkoiIiIiIiA2VJBERERERERsqSSIiIiIiIja0cINIbXb+vx/AWiucv/xLrTqn2uw80NreIURERCpOJUmklurQoYO9I1Q6UyMTAAaDwc5J5Lq0rp3fhyIiUvupJInUUoGBgfaOICIiInJL0jUrIiIiIiIiNlSSREREREREbKgkiYiIiIiI2FBJEhERERERsaGFG0TqkJCQEFJSUuwdo9qZTFoVr67q0KGDFjEREZEKU0kSqUNSUlI48sMPtLJ3kGqW+X+/Oqal2TWHVK9f7R1ARERuWSpJInVMK2ASDvaOUa3WYQHq3nnXdSXvu4iISEXpniQREREREREbKkkiIiIiIiI2VJJERERERERsqCSJiIiIiIjYUEkSERERERGxoZIkUgXi4uLYs2ePvWOIiIhIFYqLiyMuLs7eMaQKqCSJVIGYmBh2795t7xgiIiJShWJiYoiJibF3DKkCKkkiIiIiIiI2VJJERERERERsqCSJiIiIiIjYUEmqBl27dsXf358RI0YwatQovv/++xs6TlBQUIVvDuzdu3eFxxk/fjwJCQkV3u9GxxMRERERqUnq2TtAXeDs7IzRaATgm2++Yfny5Xz44Yd2TlVWUVERFosFgF69etk9S716+vYUERERkeqnv4VWs4sXL9K0aVMAcnJyeOaZZ7hw4QJFRUVMnTqVIUOGABAREcG6detwcHCgc+fOLF26FID9+/ezfv16zp49y4svvoiPjw8Aa9euJTY2loKCAoYOHUpgYGCpcS0WC0uWLOGbb77BwcGBKVOm4Ovry969e1mxYgVNmzbl+PHjREVFsXLlShwcHDCbzcyePZvExEQcHBwYM2YMEydOLHXc9PR0ZsyYQW5uLt7e3qWeKy/TqlWr2L59OwaDgTvvvJNu3boxadIkJkyYQJcuXThw4ADDhw+nb9++vP766+Tm5tK8eXMWL17MHXfcQVpaGgsWLCArKwtnZ2cWLlxI+/btK/29uhkmk4mzZ8+WeR/sLTk5mUb2DiFSTS4CZ5OTa9zPoYjUHsnJybi5udk7hlQBlaRqcOnSJfz9/cnPz+fs2bN88MEHADRs2JBVq1bRpEkTTCYTjzzyCIMHDyYlJYWwsDA2bdqEwWDg/Pnz1mOdOXOGjRs38ssvvzBlyhR8fHzYtWsXqampbN26FYvFwpQpU9i3bx+enp7W/T799FOOHDmC0WgkKyuLsWPH0qdPHwAOHz5MZGQk7u7uABgMBgCSkpLIzMwkKioKgAsXLpQ5t9dee41HH32UkSNH8tFHH1kfLy9Tw4YN+fTTT9m+fTuFhYWMHj2abt26WfcrLCwkPDycwsJCJkyYwOrVqzEYDMTExPDWW2+xePFi5syZw4IFC2jbti0//vgjCxYsYMOGDZX1domIiIhIHaeSVA1sL7dLSEjgpZdeIioqCovFwvLly9m3bx+Ojo5kZmby22+/8e233+Lj42MtK82aNbMea8iQITg6OtKhQwd+++03AOLj44mPj2fkyJEA5ObmcuLEiVIl6cCBAwwbNgwnJydatGiBp6cnBw8epEmTJnh4eFgLki13d3fS09NZuHAhgwYNwsvLq8w2CQkJhIaGAuDv78+yZcuumiknJ4fBgwfTsGFDGjZsyJ///OdSx/P19QXg+PHj/Pzzz/ztb38DoLi4mNtvv52cnBwSEhKYOnWqdZ+CgoLrfi+qi8FgwNnZmZCQEHtHKSUwMJDzP/xg7xgi1aIJ0KZjxxr3cygitYdmqmsvlaRq1rt3b7KysjCZTHz11VeYTCbCw8OpX78+3t7e5OfnX3X/Bg0alHnMYrEwefJkxo0bd0OZXFxcrvj4bbfdhtFoZNeuXWzevJnY2FgWL15cZjsHB4frzrR+/fqrZmnUqJF1/44dO/Lxxx+Xer7kcsWS0ikiIiIiUtm0ul01O3bsGGazmWbNmpGdnY2bmxv169fn22+/JSMjA4AHHniAuLg4srKyAEpdbnclXl5ebNu2jZycHAAyMzM5d+5cqW369OlDbGwsZrMZk8nE/v376dGjx1WPazKZsFgsPPTQQ0ybNo3Dhw+X2aZ3795ER0cDsH379mtmuu+++/jiiy/Iz88nJyeHL7/88opj33PPPZhMJusqe4WFhSQnJ9OkSRPatGlDbGwscLlMHTly5KrnISIiIiJSEZpJqgYl9yTB5b/Uv/HGGzg5OeHn58eUKVPw8/Oje/futGvXDoCOHTvy9NNPM2HCBBwdHbn33nt5/fXXyz2+l5cXx44ds87auLi4sHTp0lI3Eg4dOpSEhAT8/f1xcHDgxRdf5Pbbb+eXX34p97hnzpzh5Zdfpri4GIAXXnihzDazZ89mxowZrF27ttTCDeVl6tGjB97e3owYMQI3Nzc6deqEq6trmeM2aNCAkJAQXn31VbKzszGbzTz++ON07NiRpUuXMn/+fMLCwigqKsLX15cuXbqUex4iIiIiIhXhYClZ81mkmuTk5NC4cWPy8vJ47LHHWLhwYanFGypbUlISXbt2rbLjX0lgYCC5ubmsXbu2Wse9lpJ7kiZR9hLJ2mwdl/+Yq2vnXdetw0KzXr10T5KIVJmSe5L050zto5kkqXZz584lJSWF/Px8Ro0aVaUFSURERESkolSSpNq9+eab9o5Q5Xx9fTl16pS9Y4iIiEgVKlmVV2oflSSRKuDj40NSUpK9Y4iIiEgV8vHxsXcEqSJa3U5ERERERMSGSpKIiIiIiIgNlSQREREREREbKkkiIiIiIiI2VJJERERERERsaHU7kTrmV/774ap1xen/+7WunXdd9yvQzN4hRETklqSSJFKHdOjQwd4R7KLYZAKgmcFg5yRSnZpRd7/nRUTk5qgkidQhgYGB9o4gIiIiUuPpniQREREREREbKkkiIiIiIiI2VJJERERERERsqCSJiIiIiIjY0MINInVUSEgIKSkp9o5RK5j+b/U8g1bPkwro0KGDFlMREamhVJJE6qiUlBQOHUyimcsd9o5yyzufewaAvCx9DpNcn5LvGRERqZlUkkTqsGYud/DnLuPsHeOW98WRzQB6LeW6lXzPiIhIzaR7kkRERERERGyoJImIiIiIiNhQSRIREREREbGhkiQiIiIiImJDJUmkCsTFxbFnzx57xxARERGp8eLi4oiLi7N3jFJUkkSqQExMDLt377Z3DBEREZEaLyYmhpiYGHvHKEUlSURERERExIZKkoiIiIiIiA2VJBERERERERsqSSIiIiIiIjZUkuzkt99+Y/r06QwePJjRo0fzyCOP8O9///umjhkaGsq6detuaN+TJ08SGRlpt/FFRERERGoKlSQ7sFgs/P3vf6dPnz589tlnhIeHs3z5cn799dcy2xYVFVVLpoyMDKKioqplrGuprnMWEREREbmSevYOUBd9++231K9fn0cffdT6WOvWrZkwYQIA4eHhfPrpp+Tm5lJcXMyaNWt45plnuHDhAkVFRUydOpUhQ4YAEBYWRkREBAaDgTvvvJNu3boBMGHCBGbOnImHhwcmk4mxY8fy+eefc/LkSWbOnEleXh4Ac+bM4b777uPNN9/k2LFj+Pv7M2rUKCZMmMCyZcv47rvvKCgo4LHHHmPcuHFlzqW88dPS0liwYAFZWVk4OzuzcOFC2rdvT1paGjNmzCAvLw9vb282bNhAQkICe/fuZcWKFTRt2pTjx48TExNT7vhr164lNjaWgoIChg4dSmBgYNW9WTfIZDJx9uzZGpmtRHJyMo7mhvaOIVInXSrMITnZVKP/jBARqS7Jycm4ubnZO0YpKkl2kJyczL333nvVbQ4fPsz27dtp1qwZRUVFrFq1iiZNmmAymXjkkUcYPHgwhw4dIiYmhoiICMxmM6NGjbKWlPK4ubnx/vvv07BhQ06cOMELL7xAeHg406dP57333mPNmjUAfPzxx7i6urJt2zYKCgoYN24cAwYMwN3d3XqsxMTEcsefM2cOCxYsoG3btvz4448sWLCADRs28NprrxEQEMDw4cPZtGlTmXOOjIzE3d293PFTU1NJTU1l69atWCwWpkyZwr59+/D09LyRt0JEREREpAyVpBpgwYIFHDhwgPr167Nt2zYABgwYQLNmzYDLl+ctX76cffv24ejoSGZmJr/99hv79+9nyJAhNGrUCABvb+9rjlVUVERwcDBHjhzB0dGREydOXHG7+Ph4jh49yo4dOwDIzs4mNTW1VEkqb/ycnBwSEhKYOnWqdduCggIAfvjhB1atWgWAn58fS5YssW7j4eFhPX5548fHxxMfH8/IkSMByM3N5cSJEzWuJBkMBpydnQkJCbF3lHIFBgaSceycvWOI1EnO9RvTuv3dNfrPCBGR6lITZ9VVkuygY8eOfPrpp9av582bZ70krkRJ8QCIjIzEZDIRHh5O/fr18fb2Jj8//6pjODk5YbFYgP8WFID169fTokULjEYjxcXF9OjR44r7WywWXnnlFQYOHFjh87NYLDRt2hSj0Vih/VxcXK45/q5du5g8efIVL/0TEREREakMWrjBDh544AHy8/PZuHGj9bFLly6Vu312djZubm7Ur1+fb7/9loyMDAA8PT3ZuXMnly5d4uLFi3zxxRfWfVq3bk1iYiIAcXFxpY51++234+joiNFoxGw2A9C4cWNycnKs23l5ebFp0yYKCwsBOH78OLm5uaVylTd+kyZNaNOmDbGxscDlwnPkyBEAevbsaS2I0dHR5Z5zeeN7eXmxbds2a9bMzEzOndNsiIiIiIhUHs0k2YGDgwOrVq1i8eLFrF27FoPBQKNGjZgxY8YVt/fz82PKlCn4+fnRvXt32rVrB0C3bt3w9fXF398fg8GAh4eHdZ8nnniCadOmsWXLFgYNGmR9fPz48Tz33HNEREQwcOBA6+xN586dcXR0ZMSIEYwePZqAgAAyMjIYPXo0FouF5s2bs3r16lK5rjb+0qVLmT9/PmFhYRQVFeHr60uXLl2YNWsWL774ImFhYQwcOJAmTZpc8ZwffvjhK47v5eXFsWPHrDNJLi4uLF26tMbd7CciIiIity4HS8k1WSLVIC8vD2dnZxwcHIiOjiYqKoqwsLAqHTMpKYmuXbtW6Ri/FxgYSG5uLmvXrq3WcSui5J6kP3fRpYs364sjmwH0Wsp1++LIZlq3d9M9SSIi/PeepJr0Z6JmkqRaHTp0iODgYOt9S4sWLbJ3JBERERGRUlSSpFr16dOH7du32ztGlfP19eXUqVP2jiEiIiJS4/n6+to7QhkqSSJVwMfHh6SkJHvHEBEREanxfHx87B2hDK1uJyIiIiIiYkMlSURERERExIZKkoiIiIiIiA2VJBERERERERtauEGkDjufe8b6GT9y487nngHQaynX7XzuGVqjD8EWEampVJJE6qgOHTrYO0Kt0cjkAIDBYLBzErlVtMZNP4MiIjWYSpJIHVXy6dYiIiIiUpruSRIREREREbGhkiQiIiIiImJDJUlERERERMSGSpKIiIiIiIgNlSQREREREREbWt1OpJqFhISQkpJSKccymUxA7V16ukOHDlqFT0RERKqdSpJINUtJSSHxxx9xbXDzP37ZBUWXf808fdPHqmlKzk1ERESkuqkkidiBa4N69G3Z/KaP811mFkClHKumKTk3ERERkeqme5JERERERERsqCSJiIiIiIjYUEkSERERERGxoZIkIiIiIiJiQyVJpArExcWxZ88ee8cQqXZxcXHExcXZO4aIiMhNUUkSqQIxMTHs3r3b3jFEql1MTAwxMTH2jiEiInJTVJJERERERERsqCSJiIiIiIjYUEkSERERERGxoZIkFda5c2dmzJhh/bqoqIgHHniAp556CoDPPvuMd999t8LHHTduXKVlFBERERG5UfXsHUBuPS4uLiQnJ3Pp0iWcnZ2Jj4+nZcuW1ucHDx7M4MGDK3zczZs3V2ZMEREREZEbopIkN2TQoEF8+eWX+Pj4EB0dzbBhwzhw4AAA4eHhJCYmMnfuXGJjY1m1ahWOjo64urry0UcfkZyczMsvv0xhYSHFxcWEhobStm1bevfuTUJCAnv37mXlypU0b96cn3/+mW7durFs2TIcHBz46quvWLx4MS4uLtx3332kp6ezZs0aO78aZZlMJs6ePUtgYGCZ55KTk7GYi+2Q6taSby4mOTn5iq+h1FzJycm4ubnZO4aIiMhNUUmSG+Lr68vq1av585//zNGjRxkzZoy1JNlavXo169ato2XLlly4cAG4PGMUEBDAiBEjKCgooLi4bGE4fPgw0dHR3HHHHTz66KMcOHAADw8P5s6dy4cffoi7uzsvvPBClZ+niIiIiNQ9KklyQ7p06cLJkyeJiopi0KBB5W7Xu3dvgoKC+J//+R+GDh0KQK9evXjnnXf49ddfefDBB2nbtm2Z/Xr06EGrVq2sY2VkZNC4cWPc3d1xd3cHYNiwYWzZsqXyT64SGAwGnJ2dCQkJKfNcYGAgqUmH7JDq1tLQyZE/dOx4xddQai7N/ImISG2ghRvkhnl7e7NkyRKGDRtW7jbBwcFMmzaN06dPM2bMGLKysvDz8yMsLAxnZ2cmT57Mnj17yuzXoEED6++dnJwwm81Vcg4iIiIiIr+nmSS5YWPHjqVp06Z07tyZvXv3XnGbtLQ0evbsSc+ePfn666/59ddfuXjxIu7u7gQEBHD69GmOHj1K//79rznePffcQ3p6OidPnqRNmzbExMRU9imJiIiIiKgkyY1r1aoVAQEBV91myZIlpKamYrFYeOCBB+jSpQv/+Mc/MBqN1KtXjxYtWliXDr8WZ2dn5s2bx5NPPomLiwvdu3evjNMQERERESlFJUkqLCEhocxj/fr1o1+/fgCMHj2a0aNHA7By5coy206ePJnJkyeXe1zbYwHMnTu31DhxcXFYLBYWLFiAh4fHzZ2MiIiIiMjvqCTJLeVf//oXn3zyCYWFhXTt2pVHHnnE3pFEREREpJZRSZJbysSJE5k4caK9Y4iIiIhILaaSJFIFfH19OXXqlL1jiFQ7X19fe0cQERG5aSpJIlXAx8eHpKQke8cQqXY+Pj72jiAiInLT9DlJIiIiIiIiNlSSREREREREbKgkiYiIiIiI2FBJEhERERERsaGFG0TsILugiO8ysyrlOEClHKumKTk3ERERkeqmkiRSzTp06FBpxzKZTAAYDIZKO2ZNUpmvlYiIiMj1UkkSqWaBgYH2jiAiIiIiV6F7kkRERERERGyoJImIiIiIiNhwsFgsFnuHEKlKP/zwAw0bNrR3DBERERGpYerVq0fHjh3LPK6SJCIiIiIiYkOX24mIiIiIiNhQSRIREREREbGhkiQiIiIiImJDJUlERERERMSGSpKIiIiIiIgNlSQREREREREbKkkilezrr7/moYceYujQobz77rv2jiMiIiIiFaSSJFKJzGYzwcHBrF27lujoaKKiokhJSbF3LBERERGpAJUkkUr0008/8Yc//AF3d3caNGjAsGHD+Oyzz+wdS0REREQqQCVJpBJlZmbSqlUr69ctW7YkMzPTjolEREREpKJUkkRERERERGyoJIlUopYtW/Lrr79av87MzKRly5Z2TCQiIiIiFaWSJFKJPDw8OHHiBOnp6RQUFBAdHY23t7e9Y4mIiIhIBdSzdwCR2qRevXrMnTuXJ598ErPZzJgxY+jYsaO9Y4mIiIhIBThYLBaLvUOIiIiIiIjUFLrcTkRERERExIZKkoiIiIiIiA2VJBERERERERsqSSIiIiIiIjZUkkRERERERGyoJImISJ2SlZWFv78//v7+DBgwgIEDB1q/LigoKLXt+vXrycvLu+YxJ0yYwMGDB687w//+7/9y4cKFcp+fPXs2KSkpALzzzjvXfVyA8PBwMjMzK7RPUFAQ3t7e+Pv74+Pjw8qVKyu0/8mTJxk+fPh1jRMXF1ehY1fUuHHjqvT4IlI36HOSRESkTmnevDlGoxGA0NBQXFxcmDRp0hW33bBhAyNGjKBRo0aVmuEf//jHVZ9/7bXXrL9fs2YNTz/99HUf+5NPPqFjx460bNmyQplmzpyJj48P+fn5+Pr64u/vj7u7e4WOURNs3rzZ3hFEpBbQTJKIiNR5e/bsYeTIkfj5+fHyyy9TUFDAhg0bOHPmDI8//jgTJkwAYN68eYwePZphw4YREhJy1WN+/fXXBAYGWr/eu3cvTz31FADe3t6YTCZyc3OZPHkyI0aMYPjw4cTExAD/nZlatmwZly5dwt/fn+nTp5e7fYm4uDgSExOZMWMG/v7+XLp06YrndjX5+fkAuLi4ALBy5UrGjBnD8OHDmTNnDiUfr5iYmMiIESMYMWIEH3300RWPZbFYCA4O5qGHHmLixImcO3fuqq95yWvz5ptv4u/vz+jRozl06BCTJk1iyJAhbNq0CYCcnBwef/xxRo0ahZ+fHzt37rQet3fv3tbXe8KECQQGBuLj48P06dPRR0OKyHWziIiI1FEhISGWVatWWf7f//t/ll9++cVisfz/du4epK02igP4PzfxC03BD6pIEeIQisQaLIpSigarYEzUDAYKDopdnFy0hRZsOxS/sBQXNxXEwQ5qyJA6lFIp7W21HRws9IMiRVrFWEgIWHLjeYdiuL4xmpa+y+v/N97nPCfnPNvhubkiAwMDMj09LSIiDodDgsFgPP7Hjx8iIqJpmnR2dsr79+9FRKSzs1PW19eP5I5Go1JXVyeRSERERAYHB2VpaelI3idPnsidO3fie0KhUEI+u90eX08Wr6ffu7+/n7Q3vVu3bonD4ZDW1lax2+0yPj6e0LOISH9/vzx9+lRERFwul7x580ZERIaHh6WlpSUh7/LysnR1dYmmafL9+3e5fPmyBAKBE+tyOBwyNzcnIiIPHjwQl8sl4XBYgsGg1NbWxs82HA6LiEgwGJRr167JwcHBkfNSVVUqKyvl27dvEovFxOv1yurqakKNRETH4U0SERGdaQcHB7hw4QIsFgsAwOPxYG1t7djYQCAAj8eD9vZ2fPz4EZ8/f06a12Qy4erVq3j27Bk0TcPz58/R0NBwJMZqteLly5cYGxvD2toazGbzibX+bvyXL19S7u3mzZvw+Xx48eIFXr16hXfv3gH4dSPT0dEBt9sNVVXx6dMnhEIhhMNhVFVVAQDa2tqOzbm6uoqWlhYYjUYUFhaipqYmpboOz8lqtaKiogI5OTnIy8tDeno6QqEQRAQPHz6E2+1Gd3c3tre3sbu7m/D7ly5dQlFRERRFwcWLF7G1tXXieRERHeKQRERElIKvX79iamoKMzMz8Pv9qK+vj7+alozT6UQgEICqqrDZbMjJyTmybrFYsLCwAKvVikePHp36wYTfjf8T2dnZqK6uxtu3b/Hz50/cv38fExMT8Pv98Hq9p/b8N6SlpQEAFEVBenp6/LmiKNA0DX6/H3t7e1hYWIDP50NBQcGxden3Go1GxGKx/7x2Ivp/4JBERERnmqIo2NrawubmJgDA5/PFb0iys7MRiUQA/PofTFZWFsxmM3Z3d7GysnJq7urqamxsbODx48dwOp0J69vb28jKykJb9ThMPQAAAY1JREFUWxt6enqwsbGREGMymRCNRlOO19dssViS9paMpmlYX19HSUlJfPDIzc1FJBLB8vIyAODcuXMwm83x2x+/339srqqqKgQCAcRiMezs7OD169d/XJdeOBxGfn4+0tLSoKoqb4iI6K/j1+2IiOhMy8jIwNDQEPr6+hCLxWCz2XD9+nUAgNfrxY0bN3D+/HnMzs6irKwMzc3NKCoqQmVl5am5jUYj6uvrsbi4iJGRkYT1Dx8+YHR0FIqiwGQy4d69ewkxXq8Xra2tKCsrQ3t7+6nxHo8Hd+/eRWZmJubn55P29m+jo6OYnJxENBpFbW0tmpqaYDAY0NHRAZfLhYKCApSXl8fjh4aGcPv2bRgMBly5cuXYnI2NjVBVFU6nE8XFxbDb7QBOPvNUuN1u9Pb2wu12w2azobS0NOW9RESpMIjwUy9ERERERESH+LodERERERGRDockIiIiIiIiHQ5JREREREREOhySiIiIiIiIdDgkERERERER6XBIIiIiIiIi0uGQREREREREpPMPMyWpqGgOZxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=['total_visits_tl_malware'], var_name='model', value_name='total_visits')\n",
    "g = sns.catplot(x=\"total_visits\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('Malware (Toulouse)');\n",
    "g.set_xlabels('Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_visits_tl_malware_educ.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Race\n",
    "\n",
    "Next, we track total time spent and total number of visits to malware sites by racial background. Here we see reasonably consistent results with the following broad pattern: Asians visit phishing and malware sites most frequently followed by whites, blacks, and \"others\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_time_phishing</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_visits_phishing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>racial_background</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>5769</td>\n",
       "      <td>2725</td>\n",
       "      <td>6250</td>\n",
       "      <td>0</td>\n",
       "      <td>415</td>\n",
       "      <td>1163</td>\n",
       "      <td>2931</td>\n",
       "      <td>199231</td>\n",
       "      <td>5769</td>\n",
       "      <td>222</td>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>129</td>\n",
       "      <td>290</td>\n",
       "      <td>5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>9775</td>\n",
       "      <td>1845</td>\n",
       "      <td>4962</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>699</td>\n",
       "      <td>1917</td>\n",
       "      <td>216238</td>\n",
       "      <td>9775</td>\n",
       "      <td>162</td>\n",
       "      <td>495</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>78</td>\n",
       "      <td>193</td>\n",
       "      <td>30493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>10</td>\n",
       "      <td>302</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>166</td>\n",
       "      <td>449</td>\n",
       "      <td>1120</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>18553</td>\n",
       "      <td>1163</td>\n",
       "      <td>3714</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>259</td>\n",
       "      <td>884</td>\n",
       "      <td>151887</td>\n",
       "      <td>18553</td>\n",
       "      <td>96</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>95</td>\n",
       "      <td>10813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>47300</td>\n",
       "      <td>2843</td>\n",
       "      <td>8679</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>864</td>\n",
       "      <td>2601</td>\n",
       "      <td>455084</td>\n",
       "      <td>47300</td>\n",
       "      <td>212</td>\n",
       "      <td>358</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>100</td>\n",
       "      <td>263</td>\n",
       "      <td>26028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_time_phishing                                   \\\n",
       "                                count  mean   std min  25%   50%   75%   \n",
       "racial_background                                                        \n",
       "Asian                            5769  2725  6250   0  415  1163  2931   \n",
       "Black                            9775  1845  4962   0  188   699  1917   \n",
       "Missing                            10   302   354   0   42   166   449   \n",
       "Other                           18553  1163  3714   0   72   259   884   \n",
       "White                           47300  2843  8679   0  238   864  2601   \n",
       "\n",
       "                          total_visits_phishing                              \\\n",
       "                      max                 count mean  std min 25%  50%  75%   \n",
       "racial_background                                                             \n",
       "Asian              199231                  5769  222  288   0  52  129  290   \n",
       "Black              216238                  9775  162  495   0  24   78  193   \n",
       "Missing              1120                    10   19   21   0   4   13   27   \n",
       "Other              151887                 18553   96  211   0  11   32   95   \n",
       "White              455084                 47300  212  358   0  31  100  263   \n",
       "\n",
       "                          \n",
       "                     max  \n",
       "racial_background         \n",
       "Asian               5900  \n",
       "Black              30493  \n",
       "Missing               71  \n",
       "Other              10813  \n",
       "White              26028  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['racial_background'])['total_time_phishing', 'total_visits_phishing'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_time_malware</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_visits_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>racial_background</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>5769</td>\n",
       "      <td>1810</td>\n",
       "      <td>4970</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>742</td>\n",
       "      <td>1891</td>\n",
       "      <td>175442</td>\n",
       "      <td>5769</td>\n",
       "      <td>140</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>80</td>\n",
       "      <td>181</td>\n",
       "      <td>3615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>9775</td>\n",
       "      <td>1265</td>\n",
       "      <td>3857</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>446</td>\n",
       "      <td>1251</td>\n",
       "      <td>210861</td>\n",
       "      <td>9775</td>\n",
       "      <td>103</td>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>123</td>\n",
       "      <td>18297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>10</td>\n",
       "      <td>174</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>55</td>\n",
       "      <td>233</td>\n",
       "      <td>756</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>18553</td>\n",
       "      <td>808</td>\n",
       "      <td>3012</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>167</td>\n",
       "      <td>574</td>\n",
       "      <td>150629</td>\n",
       "      <td>18553</td>\n",
       "      <td>61</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>47300</td>\n",
       "      <td>1966</td>\n",
       "      <td>7159</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>554</td>\n",
       "      <td>1707</td>\n",
       "      <td>434907</td>\n",
       "      <td>47300</td>\n",
       "      <td>135</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>63</td>\n",
       "      <td>167</td>\n",
       "      <td>16532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_time_malware                                          \\\n",
       "                               count  mean   std min  25%  50%   75%     max   \n",
       "racial_background                                                              \n",
       "Asian                           5769  1810  4970   0  271  742  1891  175442   \n",
       "Black                           9775  1265  3857   0  121  446  1251  210861   \n",
       "Missing                           10   174   235   0   28   55   233     756   \n",
       "Other                          18553   808  3012   0   47  167   574  150629   \n",
       "White                          47300  1966  7159   0  152  554  1707  434907   \n",
       "\n",
       "                  total_visits_malware                                    \n",
       "                                 count mean  std min 25% 50%  75%    max  \n",
       "racial_background                                                         \n",
       "Asian                             5769  140  184   0  33  80  181   3615  \n",
       "Black                             9775  103  303   0  16  50  123  18297  \n",
       "Missing                             10   12   13   0   3   8   17     43  \n",
       "Other                            18553   61  134   0   7  20   60   6590  \n",
       "White                            47300  135  230   0  20  63  167  16532  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['racial_background'])['total_time_malware', 'total_visits_malware'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=['total_time_phishing', 'total_time_malware'], var_name='model', value_name='total_time')\n",
    "adf['model'] = adf['model'].replace({'total_time_phishing':'Phishing (PhishTank)', 'total_time_malware':'Malware (Malware)'})\n",
    "g = sns.catplot(x=\"total_time\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}');\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total time spent on Phishing/Malware domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_time_phishing_malware_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=['total_visits_phishing', 'total_visits_malware'], var_name='model', value_name='total_visits')\n",
    "adf['model'] = adf['model'].replace({'total_visits_phishing':'Phishing (PhishTank)', 'total_visits_malware':'Malware (Malware)'})\n",
    "g = sns.catplot(x=\"total_visits\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}');\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total visits to Phishing/Malware domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_visits_phishing_malware_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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",
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       "    }\n",
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       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_time_tl_malware</th>\n",
       "      <th colspan=\"8\" halign=\"left\">total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>racial_background</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>5769</td>\n",
       "      <td>328</td>\n",
       "      <td>765</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>135</td>\n",
       "      <td>341</td>\n",
       "      <td>28892</td>\n",
       "      <td>5769</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>9775</td>\n",
       "      <td>206</td>\n",
       "      <td>880</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>75</td>\n",
       "      <td>209</td>\n",
       "      <td>77749</td>\n",
       "      <td>9775</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>18553</td>\n",
       "      <td>135</td>\n",
       "      <td>824</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>95</td>\n",
       "      <td>98970</td>\n",
       "      <td>18553</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>47300</td>\n",
       "      <td>313</td>\n",
       "      <td>764</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>101</td>\n",
       "      <td>316</td>\n",
       "      <td>53732</td>\n",
       "      <td>47300</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_time_tl_malware                                     \\\n",
       "                                  count mean  std min 25%  50%  75%    max   \n",
       "racial_background                                                            \n",
       "Asian                              5769  328  765   0  51  135  341  28892   \n",
       "Black                              9775  206  880   0  19   75  209  77749   \n",
       "Missing                              10   16   14   0   6   11   26     44   \n",
       "Other                             18553  135  824   0   8   28   95  98970   \n",
       "White                             47300  313  764   0  28  101  316  53732   \n",
       "\n",
       "                  total_visits_tl_malware                                 \n",
       "                                    count mean std min 25% 50% 75%   max  \n",
       "racial_background                                                         \n",
       "Asian                                5769   22  34   0   5  12  28   853  \n",
       "Black                                9775   14  27   0   2   7  18  1340  \n",
       "Missing                                10    1   1   0   0   1   1     4  \n",
       "Other                               18553    9  18   0   1   3   9   665  \n",
       "White                               47300   19  27   0   3   9  24   956  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['racial_background'])['total_time_tl_malware', 'total_visits_tl_malware'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=['total_time_tl_malware'], var_name='model', value_name='total_time')\n",
    "g = sns.catplot(x=\"total_time\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('');\n",
    "g.set_xlabels('Total time spent on Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_time_tl_malware_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=['total_visits_tl_malware'], var_name='model', value_name='total_visits')\n",
    "g = sns.catplot(x=\"total_visits\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('');\n",
    "g.set_xlabels('Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_visits_tl_malware_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Age\n",
    "\n",
    "Next, we track things by age. Here we have a fairly consistent pattern. People 60 and over most frequently visit (spend the most time) on phishing and malware sites. People under 30 lie on the other end of the spectrum. People in thirties, forties, and fifies generally come in between.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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      ],
      "text/plain": [
       "               total_time_phishing                                          \\\n",
       "                             count  mean   std min  25%  50%   75%     max   \n",
       "hoh_oldest_age                                                               \n",
       "18-20                         1560  1152  2815   0   95  344  1045   39345   \n",
       "21-24                         2812  1516  4563   0  105  391  1280  125988   \n",
       "25-29                         4958  1904  7047   0  130  484  1578  273833   \n",
       "30-34                         5714  2131  6045   0  166  591  1846  135709   \n",
       "35-39                         6820  2185  8303   0  188  646  1883  392928   \n",
       "40-44                         7919  2283  8781   0  184  671  1992  455084   \n",
       "45-49                        10900  2289  8598   0  143  617  1964  393303   \n",
       "50-54                        12461  2068  5912   0   98  516  1888  178752   \n",
       "55-59                         9201  2530  7315   0  200  789  2439  257024   \n",
       "60-64                         6463  2918  7157   0  274  969  2956  188606   \n",
       "65 and over                  12595  2882  7139   0  278  958  2829  338646   \n",
       "Missing                          4   624  1035   5   36  161   749    2167   \n",
       "\n",
       "               total_visits_phishing                                     \n",
       "                               count mean  std min 25%  50%  75%    max  \n",
       "hoh_oldest_age                                                           \n",
       "18-20                           1560   94  152   0  15   42  118   3062  \n",
       "21-24                           2812  113  202   0  15   47  129   4266  \n",
       "25-29                           4958  135  221   0  18   56  156   3997  \n",
       "30-34                           5714  154  242   0  22   69  181   4888  \n",
       "35-39                           6820  156  265   0  24   74  189   7858  \n",
       "40-44                           7919  166  268   0  24   76  202   6503  \n",
       "45-49                          10900  166  285   0  21   71  201   9378  \n",
       "50-54                          12461  164  419   0  14   61  195  27517  \n",
       "55-59                           9201  199  321   0  26   89  245  10813  \n",
       "60-64                           6463  245  515   0  37  115  308  30493  \n",
       "65 and over                    12595  241  429   0  37  114  304  26028  \n",
       "Missing                            4   45   57   2   7   24   62    127  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_oldest_age'])['total_time_phishing', 'total_visits_phishing'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "      <td>128</td>\n",
       "      <td>6076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-54</th>\n",
       "      <td>12461</td>\n",
       "      <td>1412</td>\n",
       "      <td>4697</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>332</td>\n",
       "      <td>1215</td>\n",
       "      <td>196356</td>\n",
       "      <td>12461</td>\n",
       "      <td>104</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>124</td>\n",
       "      <td>16693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-59</th>\n",
       "      <td>9201</td>\n",
       "      <td>1757</td>\n",
       "      <td>6086</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>503</td>\n",
       "      <td>1576</td>\n",
       "      <td>241482</td>\n",
       "      <td>9201</td>\n",
       "      <td>127</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>56</td>\n",
       "      <td>158</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-64</th>\n",
       "      <td>6463</td>\n",
       "      <td>2004</td>\n",
       "      <td>5689</td>\n",
       "      <td>0</td>\n",
       "      <td>176</td>\n",
       "      <td>625</td>\n",
       "      <td>1997</td>\n",
       "      <td>186895</td>\n",
       "      <td>6463</td>\n",
       "      <td>156</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>197</td>\n",
       "      <td>18297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 and over</th>\n",
       "      <td>12595</td>\n",
       "      <td>1989</td>\n",
       "      <td>5061</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>616</td>\n",
       "      <td>1902</td>\n",
       "      <td>189046</td>\n",
       "      <td>12595</td>\n",
       "      <td>154</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>195</td>\n",
       "      <td>16532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>4</td>\n",
       "      <td>315</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>89</td>\n",
       "      <td>381</td>\n",
       "      <td>1077</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_time_malware                                          \\\n",
       "                            count  mean   std min  25%  50%   75%     max   \n",
       "hoh_oldest_age                                                              \n",
       "18-20                        1560   759  1822   0   64  222   656   26580   \n",
       "21-24                        2812   989  3003   0   64  255   835   90187   \n",
       "25-29                        4958  1269  5746   0   85  309   996  273694   \n",
       "30-34                        5714  1471  5292   0  106  383  1180  165681   \n",
       "35-39                        6820  1489  7101   0  123  413  1237  392112   \n",
       "40-44                        7919  1598  7792   0  117  427  1323  434907   \n",
       "45-49                       10900  1612  7210   0   93  397  1279  390354   \n",
       "50-54                       12461  1412  4697   0   63  332  1215  196356   \n",
       "55-59                        9201  1757  6086   0  127  503  1576  241482   \n",
       "60-64                        6463  2004  5689   0  176  625  1997  186895   \n",
       "65 and over                 12595  1989  5061   0  180  616  1902  189046   \n",
       "Missing                         4   315   512   4   23   89   381    1077   \n",
       "\n",
       "               total_visits_malware                                    \n",
       "                              count mean  std min 25% 50%  75%    max  \n",
       "hoh_oldest_age                                                         \n",
       "18-20                          1560   60   97   0   9  26   71   1855  \n",
       "21-24                          2812   72  131   0   9  30   80   2870  \n",
       "25-29                          4958   85  139   0  11  36   98   2739  \n",
       "30-34                          5714   97  151   0  14  43  115   3016  \n",
       "35-39                          6820   98  165   0  15  47  119   4763  \n",
       "40-44                          7919  106  173   0  15  48  127   4405  \n",
       "45-49                         10900  106  185   0  13  45  128   6076  \n",
       "50-54                         12461  104  264   0   9  39  124  16693  \n",
       "55-59                          9201  127  203   0  17  56  158   6590  \n",
       "60-64                          6463  156  319   0  23  73  197  18297  \n",
       "65 and over                   12595  154  276   0  23  73  195  16532  \n",
       "Missing                           4   26   34   1   5  14   35     76  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_oldest_age'])['total_time_malware', 'total_visits_malware'].describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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REc9i6iapS5cu7N+/v9J9Vqu12vGXs4jhH//4xxrXZaTo6GiOHDlidBkiItWq7hZpERERszJ1kyQVWa1WMjIyjC5DRKRal/N/ZomIiJiRR7zdTkREREREpL5oJsmLHSooJXnH6Xq9HlCv12xIDhWU0t7oIkREREQaADVJXio8PLzer9kqPx+AxpexeKQ3LJgJ9ZujPcb8XkVEREQaGjVJXiohIcHoEi7JG9aCAe/JISIiIiL/pWeSREREREREytBMUgOQkpJCZmam0WUAkP+fW/L8/f11u51JVJchPDzc9DOTIiIiIu6kJqkByMzMZH/GLoKvMboSyP7pwp8h10BRgbG1uIs35Kgqw48/1W8dIiIiImagJqmBCL4GfnOPr9FlsOLvF96AZ4ZapHoXf18iIiIiDYmeSRIRERERESlDTZKHsdvtpKenG12GiIjUgt1ux263G12GiIhUQU2Sh7HZbKSlpRldhoiI1ILNZsNmsxldhoiIVMHUzyQdPXqUSZMmkZubi8Vi4Te/+Q2///3veemll1ixYgVBQUEAjB8/np49e1YYP2fOHP7+97/TqFEjfvGLXzBr1iwCAwMpLi7m6aefZs+ePVgsFqZOnUq3bt3qO56IiIiIiJiQqZskX19fJk+ezE033cSpU6cYMmQI3bt3B2DEiBHEx8dfcnz37t2ZMGECfn5+JCcn88orr5CYmMj7778PwLp168jNzeXRRx9l5cqV+PhoYk1EREREpKEzdZMUEhJCSEgIAM2aNSM8PJzjx49f9vg777zT9fWtt97Kpk2bAPj+++9dM0ctW7akefPm7Nmzh44dO7qx+rqRl5dHTk4OiYmJlz3G4XDgr/5PrkDhWch3OGr0eTOKN6xZBd6RwxsyQN3mcDgctGjRok7OLSIitecx/+mclZVFRkYGnTp1AuCtt94iNjaWpKQkTpw4Ue34VatWcddddwEQERHB1q1bKSkp4dChQ/zzn//k6NGjdVq/iIiIiIh4BlPPJF1UWFjIuHHjmDJlCs2aNeOBBx5gzJgxWCwWFixYwOzZs5k1a1aV41NSUvD19WXAgAEADBkyBIfDwZAhQ2jTpg2dO3fG19cz1u0JCgoiICCA5OTkyx6TmJhI3tFddViVeKumjSHsf9rV6PNmlIyMDCIjI40uo9a8IYc3ZIC6zeEJs7MiIg2Z6Zukc+fOMW7cOGJjY4mOjgagVatWrv1Dhw5l9OjRACQlJbF3715CQkJ49dVXAVi9ejUff/wxS5cuxWKxAODn58eUKVNc57j//vu57rrr6imRiIiIiIiYmambJKfTydSpUwkPD+fhhx92bc/OznY9q7Rlyxbat28PUGE2adu2bbz22mu8+eabXHXVVa7tZ86cwel00qRJE7Zv346vry833HBDPSQSERERERGzM3WTtHPnTlJTU7nxxhuJi4sDLrzu+8MPP2Tfvn0AXHvttUybNq3S8dOnT6e4uNjVYHXq1Ilp06aRm5tLfHw8Pj4+hIaGMnfu3PoJ5AbR0dEcOXLE6DJERKQWLt4ZISIi5mTqJqlLly7s37+/wvbK1kSqTFWrmbdt25bNmzfXqjajWK1WMjIyjC5DRERqwWq1Gl2CiIhcgse83U5ERERERKQ+mHomSdznx59gxd9LjS6D7J8u/GmGWqR6P/4EQf9jdBUiIiIi9UtNUgMQHh5udAkulsb5APj7+2uxSZO4VIag/zHX50dERESkPqhJagASEhKMLqECraNiHt6QQURERMSd9EySiIiIiIhIGZpJamBSUlLIzMw0uoxqb1PLz79wW16LFi3qq6Qr4u2329W38PBwU858ioiISMOiJqmByczMJCNjF1eboPc4cbLqfT9d6JE4U3SofoqphUvl8BRmyHAi3+gKRERERC5Qk9QAXd0C7jL5OobbbBf+NHud4j4Xf+ciIiIiRtMzSSIiIiIiImWoSfIwdrud9PR0o8sQERG5bHa7HbvdbnQZIiKXTU2Sh7HZbKSlpRldhoiIyGWz2WzYbLqnVkQ8h6mbpKKiIu677z4GDBhAv379ePHFFwE4dOgQQ4cOxWq18vjjj1NcXFzp+Pj4eNfYv/zlL5SWlpbbv2TJEjp06EBeXl6dZxEREREREc9g6ibJ39+fZcuW8cEHH7B27Vo+/fRTvvnmG+bNm8eIESOw2+0EBgaycuXKSscvWLCADz74gA8//JD8/Hw2bdrk2nf06FG2b99OmzZt6iuOiIiIiIh4AFO/3c5isbjWbykpKaGkpASLxcLnn3/O/PnzARg0aBALFy5k2LBhFcY3a9bMNfbcuXNYLBbXvlmzZpGYmMiYMWPqIYn75OXlkZOTQ2Ji4hWNdzgcWEzdGktDdfbMhc/nlX62zbTeU214Qw5vyADekcMsGRwOh+nXvRMRKcv0/7lcWlpKXFwcd9xxB3fccQdhYWEEBgbi53ehv2vdujXHjx+vcnx8fDx33HEHTZs2pU+fPgBs2bKFkJAQIiIi6iWDiIiIiIh4DlPPJAH4+vqSmppKQUEBY8eOJTMzs0bjFy9eTFFRERMnTuTzzz/nV7/6Fa+88gpLliypo4rrVlBQEAEBASQnJ1/R+MTERI4c2+XmqkRqr/FV0KZ1uyv+bGdkZBAZGenmquqfN+TwhgzgHTnMkuFKZ4hFRIxi+pmkiwIDA+nWrRvffPMNBQUFlJSUAHDs2DFCQ0NdM05xcXEsWLCg3NiAgAB69erFRx99xMGDB8nKyiIuLo6oqCiOHTvG4MGD+fHHH42IJSIiIiIiJmPqmaS8vDz8/PwIDAzk7NmzfPbZZzz66KN069aNzZs3069fP9asWUNUVJRrxumiwsJCCgsLCQkJoaSkhI8//pguXbrQoUMHduzY4TouKiqKlStXEhQUZEREERERERExGVM3SdnZ2UyePJnS0lKcTicxMTHcc8893HDDDTzxxBO88MILREZGMnTo0Apjz5w5Q0JCAsXFxTidTrp168b9999vQAr3io6O5siRI0aXISIictmio6ONLkFEpEZM3SRFRESwdu3aCtvDwsKqfO33Ra1atWLVqlXVXmPr1q1XXJ8RrFYrGRkZRpchIiJy2axWq9EliIjUiMc8kyQiIiIiIlIfTD2TJHXjRD5ssxldxaX9lH/hT7PXKe5zIh/atDa6ChERERE1SQ1OeHi40SUA1S9weFXAhS7J7IsPmmWhxtowS4Y2rc3z+RQREZGGTU1SA5OQkGB0CYB51u6oLW/I4Q0ZRERERNxJzySJiIiIiIiUoZmkBiolJYXMzEzDrn+lt3jl55vrNjyz3KpWG2bNEB4ebpqZTxEREWlY1CQ1UJmZmezet4tGLQ0s4nTNh5zLvfDn8ZJD7q2lNq4gh+mYLMPF37OIiIiIEdQkNWCNWkKrOIvRZdRITqoT8Ly6pWYu/p5FREREjKBnkkRERERERMpQk+Rh7HY76enpRpchIiJSa3a7HbvdbnQZIiIVqEnyMDabjbS0NKPLEBERqTWbzYbNplXDRcR8TP1MUlFREQ8++CDFxcWUlpbSp08fxo0bx+TJk0lPT6d58+YAzJ49u9J1XqZMmcKePXtwOp1cf/31zJo1i6ZNm3L48GGmTJlCXl4e11xzDcnJybRu3bq+44mIiIiIiAmZukny9/dn2bJlNG3alHPnzjFs2DDuuusuACZNmkRMTMwlx0+ZMoVmzZoBMGvWLN566y1GjhzJnDlzGDhwIIMGDWLHjh3Mnz+f5OTkOs8jIiIiIiLmZ+omyWKxuNZvKSkpoaSkBIvl8t9qdrFBcjqdnD171rXd4XCQlJQEwG233cbYsWPdWHXdysvLIycnh8TExFqdx+FwUGrq3740ZKWnL3xGa/I5N+t6TzXlDTm8IQN4Rw6zZ3A4HKZZ905EpCzTP5NUWlpKXFwcd9xxB3fccQedOnUC4Pnnnyc2NpZnn32W4uLiKscnJSXRvXt3MjMzGT58OAARERGue6DtdjuFhYWuRUpFRERERKRhM/1cgq+vL6mpqRQUFDB27Fj+9a9/MX78eIKDgzl37hxPPfUUixYt4rHHHqt0/KxZsygtLWX69Ols2LCBIUOGMGnSJKZPn86aNWvo0qULoaGh+Pr61nOyKxMUFERAQECtbw9MTExk34+73FSViHv5NoF2we1q9DnPyMio9NlET+MNObwhA3hHDrNnqO1dESIidcX0M0kXBQYG0q1bNz799FNCQkKwWCz4+/szePBgdu/eDUB8fDxxcXFMnTq13FhfX1/69evnmj0KDQ1l4cKFrF27lieeeMJ1fhEREREREVPPJOXl5eHn50dgYCBnz57ls88+49FHHyU7O5uQkBCcTidbtmyhffv2ACxevNg11ul0cvDgQf73f/8Xp9PJ1q1bCQ8Pd533mmuuwcfHh0WLFjFkyBBD8omIiIiIiPmYuknKzs5m8uTJlJaW4nQ6iYmJ4Z577uGhhx4iPz8fp9NJREQEzzzzTIWxTqeTP//5zxQWFuJ0OunQoYPruPT0dJ577jksFgtdunTh6aefru9oVyw6OpojR44YXYaIiEitRUdHG12CiEilTN0kRUREsHbt2grbly9fXu1YHx8f3n333Ur3xcTEVPv6cLOyWq1kZGQYXYaIiEitWa1Wo0sQEamUxzyTJCIiIiIiUh9MPZMkdetcLuSkOo0uo0bO5V7409Pqlpo5lwsEG12FiIiINFRqkhqoiy+xMMqVLnCY73dhPSuzLD5o9oUaL4cpMwQb/xkVERGRhktNUgOVkJBg6PXNvnbH5fKGHN6QQURERMSd9EySiIiIiIhIGZpJaqBSUlLIzMw07Pq1vcUrP98ct92Z8la1GvKGDOHh4dx9991GlyEiIiJeQk1SA5WZmcmufXuhZTPjijide+Vjc08CcKjkpJuKqYXa5DALT86QewpATZKIiIi4jZqkhqxlM/ziuhhdxRUpSf0SwGPrF/e5+FkQERERcRc9kyQiIiIiIlKGmiQPY7fbSU9PN7oMERERj2O327Hb7UaXISIeQE2Sh7HZbKSlpRldhoiIiMex2WzYbDajyxARD+ARTVJpaSkDBw5k1KhRABw6dIihQ4ditVp5/PHHKS4uvuT40aNH079//wrblyxZQocOHcjLy6uTukVERERExPN4RJO0fPly2rVr5/p+3rx5jBgxArvdTmBgICtXrqxyrM1mq/T1xkePHmX79u20adOmTmoWERERERHPZPq32x07doyPP/6Y0aNHs3TpUpxOJ59//jnz588HYNCgQSxcuJBhw4ZVGFtYWMjrr7/O9OnTefzxx8vtmzVrFomJiYwZM6ZecrhLXl4eOTk5JCYm1uo8DocD/JxuqkrEQKeLcTgcvPTSSx6/3hN4x7pV3pABvCOHN2QA9+VwOByGr68nIp7B9DNJzz77LImJifj4XCg1Pz+fwMBA/Pwu9HetW7fm+PHjlY5dsGABf/jDH2jcuHG57Vu2bCEkJISIiIi6LV5ERERERDyOqWeS/v73vxMUFMTNN99c45cVZGRkcPDgQaZMmUJWVpZr+5kzZ3jllVdYsmSJu8utF0FBQQQEBJCcnFyr8yQmJrLrx4NuqkrEQE38aRf8C/7whz8QGRlpdDW1lpGR4fE5vCEDeEcOb8gA7stR27swRKThMHWT9NVXX7F161a2bdtGUVERp06dYubMmRQUFFBSUoKfnx/Hjh0jNDSU0tJSBg8eDEBUVBTBwcHs2bOHqKgoSkpKyMvLY/jw4Tz55JNkZWURFxcHXLidb/Dgwbz//vsEBwcbGVdEREREREzA1E3ShAkTmDBhAgBpaWksWbKE+fPnM27cODZv3ky/fv1Ys2YNUVFR+Pr6kpqaWm78xeeUsrKyGD16NG+88QYAO3bscB0TFRXFypUrCQoKqqdUIiIiIiJiZqZukqqSmJjIE088wQsvvEBkZCRDhw41uqR6Ex0dzZEjR4wuQ0RExONER0cbXYKIeAiPaZK6detGt27dAAgLC7vka79/rm3btnz44YeV7tu6datb6qsvVquVjIwMo8sQERHxOFar1egSRMRDmP7tdiIiIiIiIvXJY2aSpA7knqIk9Uujq7gyuScBPLd+cZ/cU6B3roiIiIgbqUlqoMLDww29fm0XBsz3ywcwfMMBIwkAACAASURBVFFAb1io0eMzBBv/eRYRERHvoiapgUpISDD0+lq7wzy8IQOgZ/VERETEbfRMkoiIiIiISBmaSWogUlJSyMzMNLoMl5rc4pWfb45b6yrj8beq4dkZwsPDDZ8VFREREe+jJqmByMzMZNe+DCwtTbRo7ulTl3WYMzcPgKyS4rqs5spdZg5T88AMFz8XIiIiIu6mJqkBsbQMwq9/H6PLqLGSDzcDeGTtUncufi5ERERE3E3PJImIiIiIiJShJsnD2O120tPTjS5DRETE49jtdux2u9FliIgHUJPkYWw2G2lpaUaXISIi4nFsNhs2m83oMkTEA3hEk1RaWsrAgQMZNWoUAJMnTyYqKoq4uDji4uKqXR9lxowZdO7c2fX94cOH+f3vf09sbCzDhw/n2LFjdVq/iIiIiIh4Do94ccPy5ctp164dp0799w1ckyZNIiYmptqxu3fv5sSJE+W2zZkzh4EDBzJo0CB27NjB/PnzSU5OdnvdIiIiIiLieUzfJB07doyPP/6Y0aNHs3Tp0hqNLS0tZe7cucyfP58tW7a4tjscDpKSkgC47bbbGDt2rDtLrlN5eXnk5OSQmJhYo3EOhwOnn28dVSVS/5ynz+BwOFx/Fzx5vaeyvCGHN2QA78jhDRnAfTkcDocp19wTEfMx/e12zz77LImJifj4lC/1+eefJzY2lmeffZbi4srXz3nzzTfp1asXISEh5bZHRES47km22+0UFha6FiwVEREREZGGzdQzSX//+98JCgri5ptvLveygvHjxxMcHMy5c+d46qmnWLRoEY899li5scePH2fTpk288cYbFc47adIkpk+fzpo1a+jSpQuhoaH4+nrGLEtQUBABAQE1vj0wMTGR3T8er6OqROqfpclVtAsOdf1dyMjIIDIy0uCqas8bcnhDBvCOHN6QAdyXo6Z3YYhIw2XqJumrr75i69atbNu2jaKiIk6dOsXEiROZN28eAP7+/gwePJglS5YAEB8fT05ODjfffDNWq5WDBw8SHR0NwJkzZ7BardjtdkJDQ1m4cCFwYQrfZrMRGBhoTEgRERERETEVUzdJEyZMYMKECQCkpaWxZMkS5s2bR3Z2NiEhITidTrZs2UL79u0BWLx4cbnx27dvd33duXNn19oIeXl5XHPNNfj4+LBo0SKGDBlST4lERERERMTsTN0kVWXixInk5+fjdDqJiIjgmWeeqdH49PR0nnvuOSwWC126dOHpp5+uo0rdLzo6miNHjhhdhoiIiMe5eHeJiEh1PKZJ6tatG926dQMuvBK8pr7++mvX1zExMZf1+nAzslqt1a4LJSIiIhVZrVajSxARD2H6t9uJiIiIiIjUJ4+ZSZLac+bmUfLhZqPLqDFnbh6AR9YudceZmwfBoUaXISIiIl5ITVIDER4ebnQJ5dRkYcB8P38AUy4A6A0LNXpshuBQ032uRURExDuoSWogEhISjC6hHK3dYR7ekEFERETEnfRMkoiIiIiISBmaSfJSKSkpZGZmGl1GlQoLCykuLgbMeRvd5fLYW9XKMHOG8PBw082CioiIiPdTk+SlMjMz2bVvPz4tQ4wupUrnc7MBOFxicCG1dbrY6Apqz4QZLn4+REREROqbmiQv5tMyhID+DxhdRpWKPnwHwNQ1inEufj5ERERE6pueSRIRERERESlDTZKHsdvtpKenG12GiIiI4ex2O3a73egyRMQLqUnyMDabjbS0NKPLEBERMZzNZsNmsxldhoh4IdM/kxQVFUXTpk3x8fHB19eX1atX89NPP/HEE09w+PBhrr32Wl544QWuvvrqCmMnT55Meno6zZs3B2D27Nnl1oPZtWsX999/P8899xwxMTH1lklERERERMzL9E0SwLJlywgKCnJ9v2jRIm6//XZGjhzJokWLWLRoEYmJiZWOnTRpUqUNUGlpKfPmzaN79+51VreIiIiIiHgej2iSfu6jjz7ijTfeAGDgwIEMHz68yiapKm+88QZ9+vRh9+7ddVFincnLyyMnJ6favA6HA6dfQD1VJeJ+ztOFOBx5l/1328zrPdWEN+TwhgzgHTm8IQNUncPhcHj0WnsiYl4e8UxSfHw8gwcP5r333gMgNzeXkJAL6/8EBweTm5tb5djnn3+e2NhYnn32WdfipcePH2fLli088IBePS0iIiIiIuWZfibpnXfeITQ0lNzcXB5++GHCw8PL7bdYLFgslkrHjh8/nuDgYM6dO8dTTz3FokWLeOyxx5g5cyYTJ07Ex8cjesRygoKCCAgIIDk5+ZLHJSYmsufH/HqqSsT9LE2a0i64bbWf9YsyMjLKPXPoqbwhhzdkAO/I4Q0ZoOocNb2LRETkcpm+SQoNDQWgZcuWWK1Wdu3aRcuWLcnOziYkJITs7GzX80rx8fHk5ORw8803M3PmTNdsk7+/P4MHD2bJkiUA7Nmzh/HjxwOQn5/PJ598gp+fH7179zYgoYiIiIiImImpm6TTp09z/vx5mjVrxunTp9m+fTtjxowhKiqKtWvXMnLkSNauXUuvXr0AWLx4cbnxFxspp9PJli1baN++PQBbt251HTN58mTuvvtuNUgiIiIiIgKYvEnKzc1l7NixwIW30fXv35+77rqLW265hccff5yVK1fSpk0bXnjhhUrHT5w4kfz8fJxOJxERETzzzDP1WX6diI6O5siRI0aXISIiYrjo6GijSxARL2XqJiksLIwPPvigwvYWLVqwbNmyascvX7682mNmz559RbUZxWq1kpGRYXQZIiIihrNarUaXICJeyvPeXCAiIiIiIlKH1CSJiIiIiIiUYerb7aR2zudmU/ThO0aXUaXzudkApq5RjHM+NxuCtUikiIiI1D81SV7q5+tJmU1hYSHFfhde0e7Jq6V7w2r2ps0Q3ML0n2MRERHxTmqSvFRCQoLRJVySty9w6Em8IYOIiIiIO+mZJBERERERkTI0k9TApaSkkJmZWe/Xre9bvPLz8wH339pn2lvVasAbMoDn5QgPDzf9jK+IiEhDpSapgcvMzGTPvu8IaBlWz1f2hdNn6+1qRbk5AOSUXOXmM9dvjrrhDRnAk3IU5R4yugQRERG5BDVJQkDLMH4RN8noMurUwdS5AF6fUzzDxc+jiIiImJOeSRIRERERESlDTZKHsdvtpKenG12GiIiISJ2y2+3Y7Xajy5AGSk2Sh7HZbKSlpRldhoiIiEidstls2Gw2o8uQBsr0zyRFRUXRtGlTfHx88PX1ZfXq1bz00kusWLGCoKAgAMaPH0/Pnj0rjK3quOLiYp5++mn27NmDxWJh6tSpdOvWrV5ziYiIiIiIOZm+SQJYtmyZq9G5aMSIEcTHx1c7trLj3n//fQDWrVtHbm4ujz76KCtXrsTHRxNrIiIiIiINnUc0Se72/fffu2aOWrZsSfPmzdmzZw8dO3Y0uLLq5eXlkZOTQ2JiolvO53A4KPFr7pZzicjlKTldgMNxuNK/x5623lNlvCEDeEcOb8gA3pHDGzJA/eZwOBxuX99Q5HJ5xNRJfHw8gwcP5r333nNte+utt4iNjSUpKYkTJ05UObay4yIiIti6dSslJSUcOnSIf/7znxw9erTOc4iIiIiIiPmZfibpnXfeITQ0lNzcXB5++GHCw8N54IEHGDNmDBaLhQULFjB79mxmzZpVYWxVxw0ZMgSHw8GQIUNo06YNnTt3xtfX14B0NRcUFERAQADJycluOV9iYiLf/egZC3CKeAu/JoG0Cw6p9O9xRkYGkZGRBlTlPt6QAbwjhzdkAO/I4Q0ZoH5zuOuuGZErYfqZpNDQUODCbXFWq5Vdu3bRqlUrfH198fHxYejQoezevRuApKQk4uLiePTRRwGqPM7Pz48pU6aQmppKSkoKJ0+e5LrrrjMkn4iIiIiImIupZ5JOnz7N+fPnadasGadPn2b79u2MGTOG7OxsQkJCANiyZQvt27cHqDCbVNVxZ86cwel00qRJE7Zv346vry833HBDPSYTERERERGzMnWTlJuby9ixYwEoLS2lf//+3HXXXSQmJrJv3z4Arr32WqZNm1bp+OTk5EqPy83NJT4+Hh8fH0JDQ5k7d249pHGP6Ohojhw5YnQZIiIiInUqOjra6BKkATN1kxQWFsYHH3xQYfvlPo9T1XFt27Zl8+bNtarNKFarlYyMDKPLEBEREalTVqvV6BKkATP9M0kiIiIiIiL1ydQzSVI/inIPcTDVc245vBJFuYcAvD6neIai3EMQ3N7oMkRERKQKapIauPDwcEOuW9+L6uX7tQKgRYvGbj2vNywO6A0ZwMNyBLc37O+eiIiIVE9NUgOXkJBgyHW1XoR5eEMG8J4cIiIiYjw9kyQiIiIiIlKGZpKkSikpKWRmZtbJuT3q1qgy8vPzAWjRogXguTnK8oYMoBxGCg8PN2xWWkREpC6oSZIqZWZmsm/f9wQH/W8dnN2fs4Xn6uC8devH3FwAzp9r9p8tnpmjPG/IAMphjB/zDhhdgoiIiNupSZJLCg76X4b2fdLoMkzj/Q0zAPQzEfmPi38nREREvImeSRIRERERESlDTZKHsdvtpKenG12GiIiIiEiN2e127Ha70WVUS02Sh7HZbKSlpRldhoiIiIhIjdlsNmw2m9FlVMv0TVJBQQHjxo0jJiaGe++9l6+//pqffvqJhx9+mOjoaB5++GFOnDhR6Vin08nzzz9Pnz59uPfee1m+fHm5/bt27eKXv/wlmzZtqo8oIiIiIiLiAUzfJM2cOZMePXqwadMmUlNTadeuHYsWLeL222/HZrNx++23s2jRokrHrl69mqNHj7Jx40Y2btxIv379XPtKS0uZN28e3bt3r68oIiIiIiLiAUz9druTJ0/yxRdfMHv2bAD8/f3x9/fno48+4o033gBg4MCBDB8+nMTExArj33nnHebPn4+Pz4VesGXLlq59b7zxBn369GH37t31kMR98vLyyMnJqTSvuzkcDvz9Auv8OiLiuU6f+YmfHAXl/k3yxLWeKuMNObwhA3hHDm/IAMphJp6aweFwuNabNDNTzyRlZWURFBREUlISAwcOZOrUqZw+fZrc3FxCQkIACA4OJvc/a9f83KFDh9iwYQODBw/mkUce4YcffgDg+PHjbNmyhQceeKC+ooiIiIiIiIcw9UxSSUkJe/fu5amnnqJTp07MmDGjwq11FosFi8VS6fji4mICAgJYvXo1NpuNKVOm8PbbbzNz5kwmTpzommHyJEFBQQQEBJCcnFzn10pMTCQ323MWtRSR+tfkqmsICwku929SRkYGkZGRBlblHt6QwxsygHfk8IYMoBxm4qkZ6uNuKHcwdZPUunVrWrduTadOnQCIiYlh0aJFtGzZkuzsbEJCQsjOziYoKAiA+Ph4cnJyuPnmm5k5cyahoaFYrVYArFYrSUlJAOzZs4fx48cDkJ+fzyeffIKfnx+9e/c2IKWIiIiIiJiJqZuk4OBgWrduTWZmJuHh4ezYsYN27drRrl071q5dy8iRI1m7di29evUCYPHixeXG9+7dm7S0NMLCwkhPT+e6664DYOvWra5jJk+ezN13360GSUREREREAJM3SQBPPfUUEydO5Ny5c4SFhTFr1izOnz/P448/zsqVK2nTpg0vvPBCpWNHjhzJxIkTWbZsGU2aNGHmzJn1XL37RUdHc+TIEaPLEBERERGpsejoaKNLuCymb5IiIyNZvXp1he3Lli2rdmxgYGCVrwe/6OKb8zyF1WolIyPD6DJERERERGrs4qMwZud5by4QERERERGpQ6afSRJj/Zh3gPc3zDC6DNP4MfcAgH4mIv/xY94BWobcYHQZIiIibqUmSaoUHh5eZ+f21AXQfBpdWJC4RYtGgOfmKMsbMoByGKVlyA11+m+FiIiIEdQkSZUSEhLq7Nye+m7/n/OGHN6QAZRDRERE3EfPJImIiIiIiJShmSSpUkpKCpmZmXVybk+7pQguLDwM0KJFC9c2T8zxc96QAZTDXcLDw+t0FllERMQTqEmSKmVmZvLd3u8JC/yF28/tSyPO5he7/bx1KacgB4CrCv/7H7CemOPnvCEDKIc7HCo4aMh1RUREzEZNklxSWOAvmNAtyegyTGF+2iwA/TzEa138jIuIiDR0eiZJRERERESkDDVJHsZut5Oenm50GSIiIiLioex2O3a73egyTE1Nkoex2WykpaUZXYaIiIiIeCibzYbNZjO6DFOrtkkqKChg3LhxxMTEcO+99/L1118D8NJLL9GjRw/i4uKIi4vjk08+cXtxWVlZ9O/f3+3nFRERERERqUq1L26YOXMmPXr04MUXX6S4uJizZ8+69o0YMYL4+Pg6LbC+lJSU4Oen91iIiIiIiDR0l+wKTp48yRdffMHs2bMB8Pf3x9/f/7JPXlhYyJgxYygoKKCkpIQ//elP9O7dm6ysLB599FF+/etf8/XXXxMaGsrLL79M48aN2bNnD1OmTAGge/fulZ7X6XQyd+5cPv30UywWCwkJCfTt25cnnniCuLg47r77bgAmT57M3XffjdVqZd68eaSnp1NcXMyDDz7I/fffT1paGgsWLCAwMJB///vfbN68+bKzGSUvL4+cnBwSExPr/FoOh4PmBNb5dUTEHAqKTnDYUVDrf1+MXuvJXbwhhzdkAO/I4Q0ZQDnMpDYZHA5HuXUfpaJL3m6XlZVFUFAQSUlJDBw4kKlTp3L69GnX/rfeeovY2FiSkpI4ceJEhfEBAQH89a9/Zc2aNSxbtow5c+bgdDoBOHDgAA8++CDr16+nefPmrgYlKSmJp556ig8++KDKumw2G/v27SM1NZXXX3+duXPnkp2dTd++fdm4cSMAxcXF7Nixg7vvvpuVK1fSvHlzVq1axapVq1ixYgWHDh0CYO/evUydOtUjGiQREREREal7l5xJKikpYe/evTz11FN06tSJGTNmsGjRIh5//HEeeOABxowZg8ViYcGCBcyePZtZs8qvseF0Onnuuef44osv8PHx4fjx4+TkXFiQs23btkRGRgJw0003cfjwYQoKCjh58iRdu3YFIC4ujk8//bRCXTt37qRfv374+vrSqlUrunbtyu7du7nrrruYOXMmxcXFbNu2jS5dutC4cWO2b9/O/v37XY3QyZMnOXDgAI0aNeKWW24hLCys9j/JehIUFERAQADJycl1fq3ExETOZnn+4pwicnkCA64mpG1wrf99ycjIcP377sm8IYc3ZADvyOENGUA5zKQ2GerjjiRPd8kmqXXr1rRu3ZpOnToBEBMTw6JFiwBo1aqV67ihQ4cyevToCuPXrVtHXl4eq1evplGjRkRFRVFUVARQ7rY9X19f1/baCAgI4P/9v//Hp59+ysaNG+nbty9woVl78skn6dGjR7nj09LSaNKkSa2vKyIiIiIi3uOSt9sFBwfTunVrMjMzAdixYwft2rUDIDs723Xcli1baN++fYXxJ0+epGXLljRq1IjPP/+cw4cPX7KYwMBAmjdvzpdffglcaLIq06VLFzZu3EhpaSl5eXl8+eWXdOzYEYC+ffuyevVqvvzyS1dTdOedd/LOO+9w7tw5AP7973+Xu21QRERERETkompf5/bUU08xceJEzp07R1hYmOuWuuTkZPbt2wfAtddey7Rp0yqMjY2NJSEhgdjYWG6++WbCw8OrLWjWrFlMmTIFi8VS5YsbrFYrX3/9NXFxcVgsFhITEwkODgYuvOxh0qRJ9OrVyzVbNXToUA4fPszgwYNxOp20aNGCl19+udpazCg6OpojR44YXYaIiIiIeKjo6GijSzA9i/PimxTkksx072p91XLxmaQJ3ZLq/FqeYH7ahf+DQD8P8Vbz02bRuK2/nkn6D2/I4Q0ZwDtyeEMGUA4z8YYMYN4c1S4mKyIiIiIi0pBo9VS5pEMFB10zKA3doYIDAPp5iNc6VHCQ9txgdBkiIiKGU5MkVbqcZ8iulCcu4tYq/8IbHRu3+O+bGT0xx895QwZQDndozw11+vdeRETEU6hJkiolJCTU2bnNev9pTXlDDm/IAMohIiIi7qNnkkRERERERMrQTJIHS0lJca1hdbny8/MBaNGiRV2UdNnq45ai8PDwOp0NExERERHvpCbJg2VmZvL93v38IrD1ZY/JKbiwCHDTQmMnERsBxfkn6uz8BwuO1dm5RURERMS7qUnycL8IbM2U2x6+7OOf/fx1gBqN8UQXc4qIiIiI1JSeSRIRERERESlDTZKHsdvtpKenG12G6Z0oOuV6/kpEREREpCbUJHkYm81GWlqa0WWY3k9Fp8jLyzO6DBERERHxQIY2SR06dGDixImu70tKSrjtttsYNWoUAB999BGLFi2q8Xnvv/9+t9UoIiIiIiINi6EvbmjSpAnfffcdZ8+epXHjxmzfvp3Q0FDX/l69etGrV68an/fdd991Z5kiIiIiItKAGH67Xc+ePfn4448BWL9+Pf369XPtW716NdOmTQNg48aN9O/fnwEDBvDggw8C8N1333HfffcRFxdHbGwsP/zwAwCdO3cGIC0tjeHDhzNu3DhiYmKYMGECTqcTgE8++YSYmBgGDx7MjBkzXLNXZpeXl8fhw4dJTEzE4XBwouiU0SWJiIiIiHgVw18B3rdvX15++WXuuece9u/fz5AhQ9i5c2eF415++WUWL15MaGgoBQUFwIUZo4ceeogBAwZQXFzM+fPnK4zbu3cv69evJyQkhAceeICdO3dyyy238Je//IU333yTsLAwxo8fX+c5RURERETEMxg+kxQREUFWVhYffvghPXv2rPK4zp07M3nyZFasWEFpaSkAt956K6+88gqLFi3iyJEjNG7cuMK4jh070rp1a3x8fIiIiODw4cNkZmYSFhZGWFgYQLnZK7MLCgri2muvJTk5mXbt2nF1QDOjSxIRERER8SqGN0kAUVFRzJ0795LNyrRp03j88cc5evQoQ4YMIT8/n9jYWFJSUmjcuDEjR45kx44dFcb5+/u7vvb19XU1WCIiIiIiIpUx/HY7gPvuu4/AwEA6dOhQ5eutDx48SKdOnejUqRPbtm3j2LFjnDp1irCwMB566CGOHj3K/v37uf3226u93vXXX8+hQ4fIysqibdu2bNiwwd2RRERERETEQ5miSWrdujUPPfTQJY+ZO3cuBw4cwOl0cttttxEREcGrr75Kamoqfn5+tGrV6rJfvtC4cWOefvppHnnkEZo0acLNN9/sjhj1Ijo6miNHjhhdhuldE9AMv6AmRpchIiIiIh7I0Cbp66+/rrCtW7dudOvWDYDBgwczePBgABYuXFjh2JEjRzJy5Mgqz1v2XAB/+ctfyl1n06ZNOJ1OnnnmGW655ZbahaknVquVjIwMo8swvasDmuHf4mqjyxARERERD2SKmSQjvP/++6xZs4Zz584RGRnJb3/7W6NLEhERERERE2iwTdKIESMYMWKE0WXU2sGCYzz7+euXffyBgmMANRrjiQ4WHOMGNJMkIiIiIjXXYJskbxAeHl7jMa3yL6wlZfStaIWFhTRt2rTOzn8DV1/Rz0dERERERE2SB0tISDC6hCuWkZFBZGSk0WWIiIiIiFRgcTqdTqOL8ATffPMNAQEBRpchIiIiIiJu4ufnR/v27StsV5MkIiIiIiJSho/RBYiIiIiIiJiJmiQREREREZEy1CSJiIiIiIiUoSZJRERERESkDDVJIiIiIiIiZahJEhERERERKUNNkoiIiIiISBlqkkRERERERMpQkyQiIiIiIlKGmiQREREREZEy1CSJiEid6NChAxMnTnR9X1JSwm233caoUaMuOS4tLa3aY+rK2bNn+d3vfkdpaSlZWVl06NCB559/3rU/Ly+Pm266iWnTpl3yPKtXr672GHfZv38/kydPrpdriYg0FGqSRESkTjRp0oTvvvuOs2fPArB9+3ZCQ0PrvY6SkpLLPnbVqlVYrVZ8fX0BaNu2LZ988olr/6ZNm7jhhhvcXmN1LpWhQ4cOHDt2jCNHjtRjRSIi3k1NkoiI1JmePXvy8ccfA7B+/Xr69evn2rdr1y5++9vfMnDgQO6//34yMzMrjI+NjaWgoACn00m3bt1Yu3YtAJMmTWL79u1kZWUxbNgwBg0axKBBg/jqq6+AC7NRw4YNY/To0fTr14/S0lLmzJnDkCFDiI2N5d1336203nXr1tGrVy/X91dddRXt2rVj9+7dAGzcuJF7773XtX/r1q0MHTqUgQMHMmLECHJycsqdr7S0lKioKJxOJwUFBURGRvLFF18A8OCDD/LDDz9U+XNYvXo1o0eP5qGHHmLEiBGcPn2apKQk7rvvPgYOHMiWLVtc17nnnntYv3795f1SRESkWmqSRESkzvTt25cNGzZQVFTE/v376dSpk2tfeHg4b731FmvXrmXcuHHlbmu7qHPnznz11Vd89913tG3bli+//BKAb775hs6dO9OyZUtef/111qxZw/PPP8+MGTNcY/fu3cvUqVPZvHkzK1eupHnz5qxatYpVq1axYsUKDh06VO5axcXFHDp0iLZt21aa4ejRo/j4+BASEuLa9+tf/5oVK1awdu1a+vXrx2uvvVZurK+vL9dffz3ff/89O3fu5Je//CVffvklxcXFHD16lOuuu+6SP4e9e/fy4osv8uabb/K3v/2N2267jZUrV7J8+XKSk5M5ffo0ADfffDM7d+6s6a9HRESq4Gd0ASIi4r0iIiLIysriww8/pGfPnuX2nTx5kj//+c8cOHAAi8XCuXPnKozv0qULX3zxBW3atOGBBx5gxYoVHD9+nMDAQJo0acLJkyeZNm0a+/btw8fHhx9++ME19pZbbiEsLAy4cKvf/v372bx5s+vaBw4ccO0HyM/Pp3nz5hVq6NGjBwsWLKBly5b07du33L5jx47xxBNP8OOPP1JcXFyhwSqbISsri1GjRrFixQq6du3KLbfcUu3PoXv37lxzzTUA/OMf/2Dr1q0sWbIEgKKiIo4ePUq7du1onBQRMwAAIABJREFU2bIl2dnZVf8iRESkRtQkiYhInYqKimLu3LksX76cn376ybV9wYIFdOvWjb/+9a9kZWXx0EMPVRjbtWtX3n77bY4ePcoTTzzBli1b2LRpE126dAFg6dKltGrVitTUVM6fP0/Hjh1dY5s0aeL62ul08uSTT9KjR48q62zcuDHFxcUVtvv7+3PTTTfx+uuvs379erZu3eraN2PGjP/P3r3HR1Hf+x9/bRITuQUI5FIwrQ2i4YiiPfiLiqKGboiGEAzQiq1WmooiFhFYSqBo5Y4R76c8xMIRL6UqAlFU2EVELMWktVhAgtXkKIRAYpKFcJHcmN8fNNukSbgl2ZnZvJ//kMzMd+bz2e+Q8OE73/lyzz33MGTIEHJycnj++eebzGHlypWUlJTw0EMPsWzZMnJzc305nO5z6NChQ4NzPfvss8TFxTW6RmVlJWFhYc3mJiIi50aP24mISJsaNWoUEyZM4LLLLmuw/ciRI74XOaxZs6bJtt/73vfwer18/fXXxMbG8qMf/Yjly5f7CowjR44QGRlJUFAQ2dnZ1NbWNnmeG264gZUrV/pGaf7v//7P96hana5du1JbW0tlZWWj9r/85S+ZOnWqb1SnqRzq5kv9pyuvvJLt27fjcDgICwsjPj6e119/nWuuueasP4e6HF599VUMwwBOPYpX5+uvv6Zv377NthURkXOjIklERNpUTExMk6NEv/rVr3jyyScZMWLEad/eduWVV/LDH/4QOPXoWnFxMf/93/8NwJ133smaNWsYPnw4BQUFDUaP6hs9ejSXXHIJ6enpDBs2jEceeaTJgmrQoEFNzu3p27cvt99+e6PtDz74IA899BDp6emNCqg6oaGhxMTEcNVVV/lyOHbsGJdeeuk5fQ4PPPAANTU1DB8+nJSUFJ555hnfvpycHG6++eZm24qIyLlxGHX/JSUiItLOff7557z00ktkZWWZHcpZq6qq4uc//zl//OMfCQnRU/QiIq1BI0kiIiL/cvnll5OQkNDsY3tWVFRUxJQpU1QgiYi0Io0kiYiIiIiI1KORJBERERERkXpUJImIiIiIiNSjIuksffnll2aH4FN/sUS7CoQcIDDyCIQcQHlYSSDkAIGRRyDkAIGRRyDkAMrDSgIhB7BuHprleZZO91pWf/vuu+8abVuyZAkFBQXnfU6v1wtA9+7dz/sc5+LYsWN06tTpvNvHxcUxfvz4Vozo/DTVF3YTCDmA8rCSQMgBAiOPQMgBAiOPQMgBlIeVBEIOYN08VCQFiIKCAr7ancf3u0acV/vSw+UAdDreeLX5tnABUHXo6Hm13fuvWEVERERE2oKKpADy/a4R/PbGpPNqO/djN8B5t/enulhFRERERNqC5iSJiIiIiIjUoyLJZjweD7m5uWaHIQHI4/Hg8XjMDkNERETEdCqSbMbtdpOTk2N2GBKA3G43brceZRQRERGxdJGUmZnJddddx7Bhw3zb8vLy+MlPfkJaWhrp6ens2LGjybZTpkxh6NChDBs2jMzMTKqrqwEwDIO5c+fidDpJTU3l888/90suIiIiIiJiD5YuktLT0/nDH/7QYFtWVhYTJkwgOzubhx56iKysrCbbDh8+nPXr1/POO+9QWVnJm2++CcCWLVv4+uuvcbvdzJkzh9/97ndtnYaIiIiIiNiIpd9ud80111BYWNhgm8Ph4NixYwAcOXKEqKioJtvedNNNvq+vvPJKiouLAfjggw8YMWIEDoeDq666ioqKCkpKSpo9j9WUl5dTWlqKy+VqsD0/P59wR7BJUfnX4crvqMjPb/QZmKGl6z1ZQV0O+fn5flsnS0RERMTKLF0kNWXGjBlkZGSwaNEiTp48yZ/+9KfTHl9dXU12djYzZ84EoLi4mJiYGN/+mJgYiouLbVMkiYiIiIhI27JdkbRy5UoyMzMZOnQo7733HjNnzuSll15q9vjHHnuMgQMHMnDgQP8F2YYiIiIICwtr9Jihy+Wian+xSVH5V9ewDkT2jm72UUt/ysvLo1+/fmaH0SJ1OVhhZE5ERETECiw9J6kpa9asISnp1IKnt956q+/FDRkZGaSlpflGjACef/55ysvLyczM9G2Ljo7m4MGDvu8PHjxIdHS0n6IXERERERGrs91IUlRUFLm5uSQkJPDJJ59w8cUXA7Bs2bIGx7355pv8+c9/5qWXXiIo6N+1YGJiIq+++iopKSn84x//oEuXLnrUTkREREREfCxdJE2ePJnc3Fy8Xi+DBw/m17/+NXPmzGH+/PnU1NQQFhbG7Nmzm2z76KOP0qtXL376058C4HQ6efDBB7npppv46KOPcDqddOjQgfnz5/szpRZLSkqiqKjI7DAkANWN0IqIiIi0d5Yukp588skmt69evfqMbXfv3t3kdofDwaOPPtqiuMzkdDrJy8szOwwJQE6n0+wQRERERCzBdnOSRERERERE2pKlR5Lk3Ow9XM7cj93n1fabw+UA593en/YeLueS3nrZhoiIiIi0DRVJASIuLq5F7Xt2DAUg1E+LibZkEdZLeke3OF8RERERkeaoSAoQ48ePNzuEcxII6wuJiIiISGDSnCQREREREZF6NJIUIJYsWUJBQUGrnMvr9QLQvQ0fvWvJ43atKS4uznajcCIiIiLStlQkBYiCggK+3L2D2PDgFp+rtKIWgA7H2m49pmDghLfNTn9W9v0rTxERERGR+lQkBZDY8GBc13Vs8Xmyth0HaJVzWVldniIiIiIi9WlOkoiIiIiISD0aSbIZj8dDUVGR3gwn8i8ejweAiy66yORIREREJFBoJMlm3G43OTk5ZochYhlutxu32/qLIIuIiIh9WLpIOnDgAHfddRe33XYbKSkprFixAoD333+flJQU4uPj2blzZ7PtFy1aRHJyMqmpqUyYMIGKiooG+4uKirj66qtZtmxZm+YhIiIiIiL2YekiKTg4mOnTp/Pee+/x+uuv88c//pGvvvqKSy+9lOeee45rrrnmtO0HDRrEunXreOedd7j44ot54YUXGuxfuHAhN954Y1umICIiIiIiNmPpOUlRUVFERUUB0LlzZ+Li4iguLmbQoEFn1f6GG27wfX3VVVexfv163/cbN26kd+/edOxorze4lZeXU1paisvlarA9Pz+fLpw0KSp7qqg8yf78/Eaf5bmwynpPLWH3HPLz89t0TS8RERFpfyw9klRfYWEheXl5DBgw4Lzav/XWWwwePBg49Y/CF198kQcffLA1QxQRERERkQBg6ZGkOseOHWPixInMmDGDzp07n3P7JUuWEBwczPDhwwF4/vnn+cUvfmHL/z2PiIggLCyMrKysBttdLhcnCj83KSp7Cg8LIuqiPo0+y3ORl5dn+zcN2j2HlowEioiIiDTF8kVSdXU1EydOJDU1laSkpNMem5mZye7du4mKiuLFF18EYPXq1WzevJmXXnoJh8MBwD/+8Q82bNjAE088QUVFBUFBQYSFhfHzn/+8zfMRERERERFrs3SRZBgGM2fOJC4ujrFjx57x+AULFjT4fsuWLfzhD3/g1VdfpUOHDr7tf/zjH31fP/fcc3Ts2FEFkoiIiIiIABYvkj799FOys7O59NJLSUtLA2Dy5MlUVVUxZ84cysvLue++++jXr1+Tr/GeM2cOVVVVvgJrwIABzJ492685tLakpCSKiorMDkPEMs40wiwiIiJyrixdJA0cOJAvvviiyX1Op/OM7T0ezxmP+fWvf33OcZnJ6XSSl5dndhgillH3s0B/L0RERKS12ObtdiIiIiIiIv5g6ZEkOTf7KmrJ2na8Vc4DtMq5rGxfRS19zQ5CRERERCxHRVKAiIuLa7Vz9fR6AbiwDRfotMICpn1p3c9NRERERAKDiqQAMX78eLNDOCd2X5tHRERERAKX5iSJiIiIiIjUo5GkALNkyRIKCgpa/bzefz2C172VHsGzwuN2raFbt27MnDnT7DBEREREpBWpSAowBQUFfJG3g8hurXvekkOn/jRO7Gu1c1ZWtNqpTPHtIbjo+33MDkNEREREWpmKpAAU2Q1+cktwq57zjQ9PvfGutc9rZ3WfiYiIiIgEFs1JEhERERERqUdFks14PB5yc3PNDkNEmuHxePB4PGaHISIiIi2gIslm3G43OTk5ZochIs1wu9243W6zwxAREZEWsPScpAMHDjBt2jTKyspwOBz85Cc/4Re/+AXPPfccb7zxBhEREQBMnjyZm266qVH7RYsW8eGHH3LBBRfw/e9/nwULFhAeHk5VVRWPPvoou3btwuFwMHPmTBISEvydnoiIiIiIWJCli6Tg4GCmT5/O5ZdfztGjRxk5ciSDBg0C4J577iEjI+O07QcNGsSUKVMICQkhKyuLF154AZfLxZtvvgnAO++8Q1lZGffeey+rVq0iKEgDayIiIiIi7Z2li6SoqCiioqIA6Ny5M3FxcRQXF591+xtuuMH39VVXXcX69esB+Oqrr3wjRz169KBLly7s2rWLK6+8shWjbxvl5eWUlpbicrma3J+fn0+oaj2/OHYCSo/sb7Yv7CJQ1qyySh75+fmttp6YiIiImMM2/5wuLCwkLy+PAQMGAPDaa6+RmppKZmYmhw8fPmP7t956i8GDBwMQHx/Ppk2bqKmpYd++fXz++eccOHCgTeMXERERERF7sPRIUp1jx44xceJEZsyYQefOnRkzZgwPPPAADoeDZ555hoULF7JgwYJm2y9ZsoTg4GCGDx8OwMiRI8nPz2fkyJH06tWLq6++muBge6z/ExERQVhYGFlZWU3ud7lclB/Y4eeo2qdOF0JEVO9m+8Iu8vLy6Nevn9lhtJhV8rD7yKKIiIjYoEiqrq5m4sSJpKamkpSUBEDPnj19+0ePHs39998PQGZmJrt37yYqKooXX3wRgNWrV7N582ZeeuklHA4HACEhIcyYMcN3jjvuuIOLL77YTxmJiIiIiIiVWbpIMgyDmTNnEhcXx9ixY33bS0pKfHOVNm7cSN++fQEajSZt2bKFP/zhD7z66qt06NDBt/27777DMAw6duzI1q1bCQ4O5pJLLvFDRiIiIiIiYnWWLpI+/fRTsrOzufTSS0lLSwNOve573bp17NmzB4DevXsze/bsJtvPmTOHqqoqX4E1YMAAZs+eTVlZGRkZGQQFBREdHc3jjz/un4RaQVJSEkVFRWaHISLNqBvxFhEREfuydJE0cOBAvvjii0bbm1oTqSnNrXp/0UUXsWHDhhbFZhan00leXp7ZYYhIM5xOp9khiIiISAvZ5u12IiIiIiIi/mDpkSQ5P98egjc+rG3Vc5YcOvVna5/Xzr49BBeFmx2FiIiIiLQ2FUkBJi4urk3O67jQC9Bqi2RaZeHPloj4HnTr1s3sMERERESklalICjDjx483O4SzYpU1bVpK88NEREREAo/mJImIiIiIiNSjkaQAtWTJEgoKCvx2Pa/33B7HC4TH7aD18oiLi7PNKKCIiIhIoFORFKAKCgrIy9tB19aZQnRGh07VSHxXue+s2xw+0kbB+FlL8zjsbZ04RERERKR1qEgKYF27w2A/rWu5xX3qT39dL5DUfXYiIiIiYg2akyQiIiIiIlKPiiSb8Xg85Obmmh2GiAQAj8eDx+MxOwwRERHLUZFkM263m5ycHLPDEJEA4Ha7cbv1vKeIiMh/snSRVFlZyahRoxg+fDgpKSk8++yzAOzbt4/Ro0fjdDqZNGkSVVVVTbbPyMjwtX3kkUeora1tsH/58uVcdtlllJeXt3kuIiIiIiJiD5YukkJDQ1mxYgVvv/02a9eu5eOPP+azzz7jiSee4J577sHj8RAeHs6qVauabP/MM8/w9ttvs27dOrxeL+vXr/ftO3DgAFu3bqVXr17+SkdERERERGzA0m+3czgcvjVoampqqKmpweFw8Mknn7B48WIAbr/9dp5//nnuvPPORu07d+7sa1tdXY3D4fDtW7BgAS6XiwceeMAPmbSe8vJySktLcblcpz0uPz8fh6VLYKlz4rtT/XWmPm0rWrPKWvyZR35+/lmvbSYiItKeWP6f0bW1taSlpXH99ddz/fXXExsbS3h4OCEhp+q7mJgYiouLm22fkZHB9ddfT6dOnRg6dCgAGzduJCoqivj4eL/kICIiIiIi9mHpkSSA4OBgsrOzqaioYMKECRQUFJxT+2XLllFZWcnUqVP55JNP+NGPfsQLL7zA8uXL2yjithUREUFYWBhZWVmnPc7lclF0cIefopKWuLAD9Irpc8Y+bSt5eXn069fPlGu3JuVx7swavRQREbE6y48k1QkPDychIYHPPvuMiooKampqADh48CDR0dG+Eae0tDSeeeaZBm3DwsIYMmQIH3zwAXv37qWwsJC0tDQSExM5ePAg6enpfPvtt2akJSIiIiIiFmPpkaTy8nJCQkIIDw/nxIkT/OUvf+Hee+8lISGBDRs2kJKSwpo1a0hMTPSNONU5duwYx44dIyoqipqaGjZv3szAgQO57LLL2LZtm++4xMREVq1aRUREhBkpioiIiIiIxVi6SCopKWH69OnU1tZiGAbJycnccsstXHLJJTz88MM8/fTT9OvXj9GjRzdq+9133zF+/HiqqqowDIOEhATuuOMOE7IQERERERE7sXSRFB8fz9q1axttj42Nbfa133V69uzJW2+9dcZrbNq06bzjM0NSUhJFRUVmhyEiASApKcnsEERERCzJ0kWSNOZ0OsnLyzM7DBEJAE6n0+wQRERELMk2L24QERERERHxB40kBbDDXtji9s+1DnlP/emv6wWSw17oFWN2FCIiIiJSR0VSgIqLi/Pr9TqEnaqSunfvflbHHzt2jE6dOrVlSH7RGnn0ivF/f4mIiIhI81QkBajx48ebHcJpaeFPEREREbEqzUkSERERERGpRyNJAW7JkiUUFBT49Zpe75kfvdPjdmcWFxdn+RFBERERkUCkIinAFRQUsHPPDi7o4b9rVped+rO4Zt/pDzze9rH4RRvkUfcZioiIiIj/qUhqBy7oAT3THH67Xmm2Afj3moGm7jMUEREREf/TnCQREREREZF6VCTZjMfjITc31+wwRKQd8ng8eDwes8MQERFpcyqSbMbtdpOTk2N2GCLSDrndbtxurRgtIiKBz9JzkiorK/nZz35GVVUVtbW1DB06lIkTJzJ9+nRyc3Pp0qULAAsXLmxyrZoZM2awa9cuDMPghz/8IQsWLKBTp07s37+fGTNmUF5eTrdu3cjKyiImJsbf6YmIiIiIiAVZukgKDQ1lxYoVdOrUierqau68804GDx4MwLRp00hOTj5t+xkzZtC5c2cAFixYwGuvvca4ceNYtGgRI0aM4Pbbb2fbtm0sXryYrKysNs9HRERERESsz9JFksPh8K1BU1NTQ01NDQ7H2b8xra5AMgyDEydO+Lbn5+eTmZkJwLXXXsuECRNaMeq2VV5eTmlpKS6X66yOz8/Pp9bSvSxNqT1+qu/Otp9bQmtWWYuV88jPzz/t+mciIiKBwvJzkmpra0lLS+P666/n+uuvZ8CAAQA89dRTpKamMn/+fKqqqpptn5mZyaBBgygoKOCuu+4CID4+3vdcvcfj4dixY74FUEVEREREpH2z/BhDcHAw2dnZVFRUMGHCBP75z38yefJkIiMjqa6uZtasWSxdupQHH3ywyfYLFiygtraWOXPm8N577zFy5EimTZvGnDlzWLNmDQMHDiQ6Oprg4GA/Z3Z+IiIiCAsLO+vHA10uF3u+3dHGUUlrC+4IfSL7+OUx0Ly8vCbn9NmN8mh7/hjZFBERsQLLjyTVCQ8PJyEhgY8//pioqCgcDgehoaGkp6ezc+dOADIyMkhLS2PmzJkN2gYHB5OSkuIbPYqOjub5559n7dq1PPzww77zi4iIiIiIWHokqby8nJCQEMLDwzlx4gR/+ctfuPfeeykpKSEqKgrDMNi4cSN9+/YFYNmyZb62hmGwd+9efvCDH2AYBps2bSIuLs533m7duhEUFMTSpUsZOXKkKfmJiIiIiIj1WLpIKikpYfr06dTW1mIYBsnJydxyyy3cfffdeL1eDMMgPj6exx57rFFbwzD4zW9+w7FjxzAMg8suu8x3XG5uLk8++SQOh4OBAwfy6KOP+ju185aUlERRUZHZYYhIO5SUlGR2CCIiIn5h6SIpPj6etWvXNtr+8ssvn7FtUFAQf/rTn5rcl5ycfMbXh1uV0+kkLy/P7DBEpB1yOp1mhyAiIuIXtpmTJCIiIiIi4g+WHkmS1lFdBqXZhl+vB/69ZqCpLgMizY5CREREpH1SkRTg6l5W4U/ekFNrTp1u0UkrL5h5Ltosj0hz+k5EREREVCQFvPHjx5sdQpOsvBbMuQiUPERERETk3zQnSUREREREpB6NJAWwJUuWUFBQYMq1vd7TP3Knx+1aR1xcnGVHC0VERETsSkVSACsoKGDHnt3Qo7P/L152BIB9NUeaP+Z4mZ+CaWNm5VF21JzrioiIiAQ4FUmBrkdnQtIG+v2yNdl/AzDl2u1F3WcsIiIiIq1Lc5JERERERETqUZFkMx6Ph9zcXLPDEBFpNR6PB4/HY3YYIiIiPiqSbMbtdpOTk2N2GCIircbtduN2u80OQ0RExMcWRVJtbS0jRozgvvvuA2Dfvn2MHj0ap9PJpEmTqKqqOm37+++/n2HDhjXavnz5ci677DLKy8vbJG4REREREbEfWxRJL7/8Mn369PF9/8QTT3DPPffg8XgIDw9n1apVzbZ1u91NvqL5wIEDbN26lV69erVJzCIiIiIiYk+Wf7vdwYMH2bx5M/fffz8vvfQShmHwySefsHjxYgBuv/12nn/+ee68885GbY8dO8b//u//MmfOHCZNmtRg34IFC3C5XDzwwAN+yaO1lJeXU1paisvlOuOx+fn5EGL4ISoxxfEq8vPzz+peOB2z13pqLcrDOs41h/z8/GbXVBMRETGD5UeS5s+fj8vlIijoVKher5fw8HBCQk7VdzExMRQXFzfZ9plnnuGXv/wlF154YYPtGzduJCoqivj4+LYNXkREREREbMfSI0kffvghERER9O/f/5xfVpCXl8fevXuZMWMGhYWFvu3fffcdL7zwAsuXL2/tcP0iIiKCsLAwsrKyznisy+Vix7d7/RCVmKJjKH0iv39W98Lp5OXl0a9fv1YKyjzKwzrONYeWjoaKiIi0NksXSX//+9/ZtGkTW7ZsobKykqNHjzJv3jwqKiqoqakhJCSEgwcPEh0dTW1tLenp6QAkJiYSGRnJrl27SExMpKamhvLycu666y5++9vfUlhYSFpaGnDqcb709HTefPNNIiMjzUxXREREREQswNJF0pQpU5gyZQoAOTk5LF++nMWLFzNx4kQ2bNhASkoKa9asITExkeDgYLKzsxu0r5unVFhYyP33388rr7wCwLZt23zHJCYmsmrVKiIiIvyUlYiIiIiIWJmli6TmuFwuHn74YZ5++mn69evH6NGjzQ7Jb5KSkigqKjI7DBGRVpOUlGR2CCIiIg3YpkhKSEggISEBgNjY2NO+9vs/XXTRRaxbt67JfZs2bWqV+PzF6XSSl5dndhgiIq3G6XSaHYKIiEgDln+7nYiIiIiIiD/ZZiRJzlPZUWqy/2bCdY8AmHPt9qLsKOhdIyIiIiKtTkVSAIuLizPt2t4QL0CzC0QGwoKZYHIekeb2sYiIiEigUpEUwMaPH292CM0KhLVgIHDyEBEREZF/05wkERERERGRejSSFICWLFlCQUGB36/r9Z7+Ebv69Ljd+YmLi7P0CKGIiIhIIFCRFIAKCgrYsScPRw//LpBrlJUDUFhTdXYNjh9tw2j8yE951H2+IiIiItK2VCQFKEePCEKGDfXrNWvWbQDw+3Xbi7rPV0RERETaluYkiYiIiIiI1KMiyWY8Hg+5ublmhyEick48Hg8ej8fsMERERM6KiiSbcbvd5OTkmB2GiMg5cbvduN1us8MQERE5K7YokmpraxkxYgT33XcfANOnTycxMZG0tDTS0tLIy8s7bfu5c+dy9dVX+77fv38/v/jFL0hNTeWuu+7i4MGDbRq/iIiIiIjYhy1e3PDyyy/Tp08fjh7991vEpk2bRnJy8hnb7ty5k8OHDzfYtmjRIkaMGMHtt9/Otm3bWLx4MVlZWa0et4iIiIiI2I/li6SDBw+yefNm7r//fl566aVzaltbW8vjjz/O4sWL2bhxo297fn4+mZmZAFx77bVMmDChNUNuU+Xl5ZSWluJyuZo9Jj8/HyMk2I9RiT8Yx78jPz//tH1/PrRmlbUEQh5N5ZCfn39Wa6iJiIhYgeUft5s/fz4ul4ugoIahPvXUU6SmpjJ//nyqqppel+fVV19lyJAhREVFNdgeHx/vezbe4/Fw7Ngx30KoIiIiIiLSvll6JOnDDz8kIiKC/v37N3hZweTJk4mMjKS6uppZs2axdOlSHnzwwQZti4uLWb9+Pa+88kqj806bNo05c+awZs0aBg4cSHR0NMHB9hh5iYiIICws7LSPB7pcLnZ+W+zHqMQfHB070CcyutUfDc3Ly6Nfv36tek4zKA/raCqH1h4BFRERaUuWLpL+/ve/s2nTJrZs2UJlZSVHjx5l6tSpPPHEEwCEhoaSnp7O8uXLAcjIyKC0tJT+/fvjdDrZu3cvSUlJAHz33Xc4nU48Hg/R0dE8//zzwKnHQtxuN+Hh4eYkKSIiIiIilmLpImnKlClMmTIFgJycHJYvX84TTzxBSUkJUVFRGIbBxo0b6du3LwDLli1r0H7r1q2+r6+++mrfGh3l5eV069aNoKAgli5dysiRI/2UkYiIiIiIWJ2li6TmTJ06Fa/Xi2EYxMfH89hjj51T+9zcXJ588kkcDgcDBw7k0UcfbaNIW19SUhJFRUVmhyEick7qRvVFRETswDZFUkJCAgkJCcCpV4Kfq+3bt/u+Tk5OPqvXh1uR0+k847pQIiJW43Q6zQ5BRETkrFn+7XYiIiI2EiKcAAAgAElEQVQiIiL+ZJuRJDk3Rlk5Nes2+P2agN+v214YZeUQGW12GCIiIiIBT0VSAIqLizPlut6QUICzWjAyEBbMBD/nERltWt+KiIiItCcqkgLQ+PHjzQ7hjAJhLRgInDxERERE5N80J0lERERERKQejSQFiCVLllBQUOCXa3m9XuDsHqtrjh63a15cXJwtRgNFREREApWKpABRUFDAjj1fENQjqs2vdbKsBID9NS080fGqlgdjBa2YR91nKyIiIiLmUZEUQIJ6RBE2bEybX6dy3UoAv1yrvan7bEVERETEPJqTJCIiIiIiUo+KJJvxeDzk5uaaHYaItHMejwePx2N2GCIiIm1CRZLNuN1ucnJyzA5DRNo5t9uN2+02OwwREZE2Yfk5SYmJiXTq1ImgoCCCg4NZvXo1hw4d4uGHH2b//v307t2bp59+mq5duzZqO336dHJzc+nSpQsACxcubLCmzY4dO7jjjjt48sknSU5O9ltOIiIiIiJiXZYvkgBWrFhBRESE7/ulS5dy3XXXMW7cOJYuXcrSpUtxuVxNtp02bVqTBVBtbS1PPPEEgwYNarO4RURERETEfmxRJP2nDz74gFdeeQWAESNGcNdddzVbJDXnlVdeYejQoezcubMtQmwz5eXllJaWNso3Pz8fIyTMpKiktRjHj5GfX37O93NLaM0qa7FLHvn5+S1aK01ERMTKbDEnKSMjg/T0dF5//XUAysrKiIo6tR5QZGQkZWVlzbZ96qmnSE1NZf78+VRVnVrPpri4mI0bNzJmjF5hLSIiIiIiDVl+JGnlypVER0dTVlbG2LFjiYuLa7Df4XDgcDiabDt58mQiIyOprq5m1qxZLF26lAcffJB58+YxdepUgoJsUSM2EBERQVhYGFlZWQ22u1wudn3rNSkqaS2Ojp3oE3lRo/5tS3l5eQ3m6tmV8vAvf452ioiI+Jvli6To6GgAevTogdPpZMeOHfTo0YOSkhKioqIoKSnxzVfKyMigtLSU/v37M2/ePN9oU2hoKOnp6SxfvhyAXbt2MXnyZAC8Xi8fffQRISEh/PjHPzYhQxERERERsRJLF0nHjx/n5MmTdO7cmePHj7N161YeeOABEhMTWbt2LePGjWPt2rUMGTIEgGXLljVoX1dIGYbBxo0b6du3LwCbNm3yHTN9+nRuvvlmFUgiIiIiIgJYvEgqKytjwoQJwKm30Q0bNozBgwdzxRVXMGnSJFatWkWvXr14+umnm2w/depUvF4vhmEQHx/PY4895s/w20RSUhJFRUVmhyEi7VxSUpLZIYiIiLQZSxdJsbGxvP322422d+/enRUrVpyx/csvv3zGYxYuXHhesZnF6XSSl5dndhgi0s45nU6zQxAREWkz9ntzgYiIiIiISBuy9EiSnJuTZSVUrlvpl+sAfrlWe3OyrAQitfaMiIiIiJlUJAWI/3w1elvy/uuuaclCknZZMPNMWj2PyO5+7UsRERERaUxFUoAYP3682SGcE7usBXMmgZKHiIiIiPyb5iSJiIiIiIjUo5GkALdkyRIKCgrMDqORgwcPEhYW1qJH9qwgEB4bPFMOcXFxthupFBEREWkJFUkBrqCggF17viSsR6zZoTRQeegIcITSmg5mh9JCwXD8hNlBtFDzOVSW7fNzLCIiIiLmU5HUDoT1iOX7adPMDqOBvdmPA1guLmmorp9ERERE2hPNSRIREREREalHRZLNeDwecnNzzQ5DRERawOPx4PF4zA5DRESaoSLJZtxuNzk5OWaHISIiLeB2u3G73WaHISIizbD8nKTExEQ6depEUFAQwcHBrF69mueee4433niDiIgIACZPnsxNN93UqG1zx1VVVfHoo4+ya9cuHA4HM2fOJCEhwa95iYiIiIiINVm+SAJYsWKFr9Cpc88995CRkXHGtk0d9+abbwLwzjvvUFZWxr333suqVasICtLAmoiIiIhIe2eLIqm1ffXVV76Rox49etClSxd27drFlVdeaXJkZ1ZeXk5paSkul+usjs/Pz6cmpEsbRyWBquZ4Bfn5+8/6fjNTIKxZBYGRRyDkAG2bR35+vu3XiRMRCWS2GDrJyMggPT2d119/3bfttddeIzU1lczMTA4fPtxs26aOi4+PZ9OmTdTU1LBv3z4+//xzDhw40OZ5iIiIiIiI9Vl+JGnlypVER0dTVlbG2LFjiYuLY8yYMTzwwAM4HA6eeeYZFi5cyIIFCxq1be64kSNHkp+fz8iRI+nVqxdXX301wcHBJmR37iIiIggLCyMrK+usjne5XHz5rd0XOxWzhHQMp09k1Fnfb2bKy8ujX79+ZofRYoGQRyDkAG2bhx1GZ0VE2jPLjyRFR0cDpx6Lczqd7Nixg549exIcHExQUBCjR49m586dAGRmZpKWlsa9994L0OxxISEhzJgxg+zsbJYsWcKRI0e4+OKLTclPRERERESsxdIjScePH+fkyZN07tyZ48ePs3XrVh544AFKSkqIiooCYOPGjfTt2xeg0WhSc8d99913GIZBx44d2bp1K8HBwVxyySV+zExERERERKzK0kVSWVkZEyZMAKC2tpZhw4YxePBgXC4Xe/bsAaB3797Mnj27yfZZWVlNHldWVkZGRgZBQUFER0fz+OOP+yGb1pGUlERRUZHZYYiISAskJSWZHYKIiJyGpYuk2NhY3n777Ubbz3Z+RHPHXXTRRWzYsKFFsZnF6XSSl5dndhgiItICTqfT7BBEROQ0LD8nSURERERExJ8sPZIkraOybB97s631SGFl2T4Ay8UlDVWW7YPIvmaHISIiIuJXKpICXFxcnNkhNOlgbRfCwsLo3v1Cs0NpkUBYNPO0OUT2tew9JCIiItJWVCQFuPHjx5sdQpO0jop1BEIOIiIiIq1Jc5JERERERETq0UhSO7RkyRIKCgpMjeFcH1Pzer0AdO/eva1COi8B/7idjXTr1o2ZM2eaHYaIiIgEABVJ7VBBQQF79nxFZMQPTIwilBPHqs/66G/LygA4Wd25rQI6T+eWhzXZP4dvy78hNvZ7ZochIiIiAUJFUjsVGfEDRt/2W7PDOGtvvjcXwFYxi/+cuj+qzA5DREREAoTmJImIiIiIiNSjIslmPB4Pubm5ZochIiJiax6PB4/HY3YYImJRKpJsxu12k5OTY3YYIiIituZ2u3G73WaHISIWZfkiqaKigokTJ5KcnMytt97K9u3bOXToEGPHjiUpKYmxY8dy+PDhJtsahsFTTz3F0KFDufXWW3n55Zcb7N+xYwf/9V//xfr16/2RioiIiIiI2IDli6R58+Zx4403sn79erKzs+nTpw9Lly7luuuuw+12c91117F06dIm265evZoDBw7w/vvv8/7775OSkuLbV1tbyxNPPMGgQYP8lYqIiIiIiNiApd9ud+TIEf7617+ycOFCAEJDQwkNDeWDDz7glVdeAWDEiBHcdddduFyuRu1XrlzJ4sWLCQo6VQv26NHDt++VV15h6NCh7Ny50w+ZtJ7y8nJKS0ubzPds5efnExoS3opRiZjr+HeHKDvkbdHfC6sIhHWrAiEHCIw8AiEHaJs88vPzLbf2nohYh6VHkgoLC4mIiCAzM5MRI0Ywc+ZMjh8/TllZGVFRUQBERkZS9q81dP7Tvn37eO+990hPT+dXv/oVX3/9NQDFxcVs3LiRMWPG+CsVERERERGxCUuPJNXU1LB7925mzZrFgAEDmDt3bqNH6xwOBw6Ho8n2VVVVhIWFsXr1atxuNzNmzOCPf/wj8+bNY+rUqb4RJjuJiIggLCyMrKys8z6Hy+WirMTei4eK1NexQzcienZs0d8Lq8jLy6Nfv35mh9EigZADBEYegZADtE0egTDyLCJtx9JFUkxMDDExMQwYMACA5ORkli5dSo8ePSgpKSEqKoqSkhIiIiIAyMjIoLS0lP79+zNv3jyio6NxOp0AOJ1OMjMzAdi1axeTJ08GwOv18tFHHxESEsKPf/xjE7IUERERERErsXSRFBkZSUxMDAUFBcTFxbFt2zb69OlDnz59WLt2LePGjWPt2rUMGTIEgGXLljVo/+Mf/5icnBxiY2PJzc3l4osvBmDTpk2+Y6ZPn87NN9+sAklERERERACLF0kAs2bNYurUqVRXVxMbG8uCBQs4efIkkyZNYtWqVfTq1Yunn366ybbjxo1j6tSprFixgo4dOzJv3jw/R9/6kpKSKCoqMjsMERERW0tKSjI7BBGxMMsXSf369WP16tWNtq9YseKMbcPDw5t9PXidujfn2YXT6SQvL8/sMERERGyt7nF8EZGm2O/NBSIiIiIiIm3I8iNJ0ja+Lf+GN9+ba3YYZ+3bsm8AbBWz+M+35d8Q2+l7ZochIiIiAUJFUjsUFxdndgjnvDBg0AWnFgLu3v2CtgrpvATCQo2BkEOPqEvo1q2b2WGIiIhIgFCR1A6NHz/e7BC0doeFBEIOgObqiYiISKvRnCQREREREZF6VCSJiIiIiIjUo8ft2qklS5ZQUFBg2vXPZR6M1+sFoHv37m0Z0nkJhPk8Vs8hLi7OEo+IioiISPuhIqmdKigo4MvdXxEb/n1Trh/MBZzwVp3VsaUVpQB0OGa9f8ifSx5WZeUc9lXsNTsEERERaYdUJLVjseHfZ0pCptlhnNHinAUAtohVWldd34uIiIj4k+YkiYiIiIiI1KMiyWY8Hg+5ublmhyEiItKmPB4PHo/H7DBEpJ06Y5FUUVHBxIkTSU5O5tZbb2X79u0APPfcc9x4442kpaWRlpbGRx991OrBFRYWMmzYsFY/r5253W5ycnLMDkNERKRNud1u3G632WGISDt1xjlJ8+bN48Ybb+TZZ5+lqqqKEydO+Pbdc889ZGRktGmA/lJTU0NIiKZoiYiIiIi0d6cdSTpy5Ah//etfGTVqFAChoaGEh4ef9cmPHTvGL37xC26//XZSU1PZuHEjcGqE6NZbb+W3v/0tKSkp/PKXv/QVX7t27WL48OEMHz6c1157rcnzGobBokWLGDZsGKmpqbz33nsAPPzww2zevNl33PTp01m/fj21tbUsWrSIkSNHkpqayp/+9CcAcnJyuPPOO7n//vtJSUk567xERERERCRwnXbopLCwkIiICDIzM9mzZw+XX345M2fOpGPHjgC89tprrF27lv79+zN9+nS6du3aoH1YWBj/8z//Q+fOnSkvL+enP/0pQ4YMAeCbb77hySefZO7cuTz00ENs2LCBtLQ0MjMzeeSRR7jmmmtYtGhRk3G53W727NlDdnY2Xq+XUaNGMXDgQG677Tbef/99br75Zqqqqti2bRu/+93vWLVqFV26dOGtt96iqqqKO+64g0GDBgGwe/du3nnnHWJjY1v8YfpDeXk5paWluFyuFp0nPz+fLpx9wStihorKw+zPrzir+93q6z2drUDIIxBygMDIw8455OfnW3J9PBFpH047klRTU8Pu3bsZM2YMa9eupUOHDixduhSAMWPG4PF4yM7OJioqioULFzZqbxgGTz75JKmpqYwdO5bi4mJKS0+teXPRRRfRr18/AC6//HL2799PRUUFR44c4ZprrgEgLS2tybg+/fRTUlJSCA4OpmfPnlxzzTXs3LmTwYMHk5OTQ1VVFVu2bGHgwIFceOGFbN26lezsbNLS0hg9ejSHDh3im2++AeCKK66wTYEkIiIiIiJt77QjSTExMcTExDBgwAAAkpOTfUVSz549fceNHj2a+++/v1H7d955h/LyclavXs0FF1xAYmIilZWVwKlH9+oEBwf7trdEWFgY/+///T8+/vhj3n//fW677TbgVLH229/+lhtvvLHB8Tk5Ob5RMbuIiIggLCyMrKysFp3H5XJxotCaC4iK1AkP60rURZFndb/n5eX5/uPFzgIhj0DIAQIjDzvn0NInJkREWuK0I0mRkZHExMRQUFAAwLZt2+jTpw8AJSUlvuM2btxI3759G7U/cuQIPXr04IILLuCTTz5h//79pw0mPDycLl268Le//Q04VWQ1ZeDAgbz//vvU1tZSXl7O3/72N6688koAbrvtNlavXs3f/vY3X1F0ww03sHLlSqqrqwH4v//7P44fP37aWEREREREpH064+vcZs2axdSpU6muriY2NpYFCxYAkJWVxZ49ewDo3bs3s2fPbtQ2NTWV8ePHk5qaSv/+/YmLiztjQAsWLGDGjBk4HA7fvKH/5HQ62b59O2lpaTgcDlwuF5GRkQAMGjSIadOmMWTIEN9o1ejRo9m/fz/p6ekYhkH37t35/e9/f8ZYRERERESk/TljkdSvXz9Wr17daPvZPP4SERHB66+/3uS+devW+b6u/xrx/v378/bbb/u+nzZtWqO2DoeD3/zmN/zmN79ptO+CCy5otNhqUFAQkydPZvLkyQ22JyQkkJCQcMY8rCQpKYmioiKzwxAREWlTSUlJZocgIu2YFgayGafTSV5entlhiIiItCmn02l2CCLSjp12TpKIiIiIiEh7o5GkdmxfxV4W5ywwO4wz2ldx6nXtdohVWte+ir305RKzwxAREZF2RkVSO3U2L9FoS+eywGFP76nXzV/YPfQMR/qfnRdqrGPlHPpyien3qoiIiLQ/KpLaqfHjx5t6fTuv3VFfIOQRCDmIiIiItCbNSRIREREREalHI0k2tWTJEt8iv//J6/UC0L17d3+GRFxcnOkjVCIiIiIiLaUiyaYKCgr4avcXfD88ptG+0ooSADod899A4d6Kg367loiIiIhIW1KRZGPfD49hxrVjG22f/8n/AjS5r63UXVNERERExO40J0lERERERKQejSTZjMfjoaioyOwwGjlceZQQ70mzwxARERERaTEVSTbjdrstua7NocqjBJWfMDsMEREREZEWM/Vxu8suu4ypU6f6vq+pqeHaa6/lvvvuA+CDDz5g6dKl53zeO+64o9ViFBERERGR9sXUkaSOHTvy5ZdfcuLECS688EK2bt1KdHS0b/+QIUMYMmTIOZ/3T3/6U2uGKSIiIiIi7YjpL2646aab2Lx5MwDvvvsuKSkpvn2rV69m9uzZALz//vsMGzaM4cOH87Of/QyAL7/8klGjRpGWlkZqaipff/01AFdffTUAOTk53HXXXUycOJHk5GSmTJmCYRgAfPTRRyQnJ5Oens7cuXN9o1dWV15ezv79+8nPz+dw5VGzwxERERERCTimz0m67bbb+P3vf88tt9zCF198wciRI/n0008bHff73/+eZcuWER0dTUVFBXBqxOjuu+9m+PDhVFVVcfJk4xcH7N69m3fffZeoqCjGjBnDp59+yhVXXMEjjzzCq6++SmxsLJMnT27zPEVERERExB5MH0mKj4+nsLCQdevWcdNNNzV73NVXX8306dN54403qK2tBeCqq67ihRdeYOnSpRQVFXHhhRc2anfllVcSExNDUFAQ8fHx7N+/n4KCAmJjY4mNjQVoMHpldREREfTu3Zs+ffrQNayz2eGIiIiIiAQc04skgMTERB5//PHTFiuzZ89m0qRJHDhwgJEjR+L1eklNTWXJkiVceOGFjBs3jm3btjVqFxoa6vs6ODjYV2CJiIiIiIg0xfTH7QBGjRpFeHg4l112GTk5OU0es3fvXgYMGMCAAQPYsmULBw8e5OjRo8TGxnL33Xdz4MABvvjiC6677rozXu+HP/wh+/bto7CwkIsuuoj33nuvtVMSERERERGbskSRFBMTw913333aYx5//HG++eYbDMPg2muvJT4+nhdffJHs7GxCQkLo2bPnWb984cILL+TRRx/lV7/6FR07dqR///6tkYZfJCUlUVRUxK5du8wOpYFuYZ0JiehodhgiIiIiIi1mapG0ffv2RtsSEhJISEgAID09nfT0dACef/75RseOGzeOcePGNXve+ucCeOSRRxpcZ/369RiGwWOPPcYVV1zRsmT8xOl0kpeXZ7kiqWtYZ0K7dzU7DBERERGRFrPESJIZ3nzzTdasWUN1dTX9+vXjpz/9qdkhiYiIiIiIBbTbIumee+7hnnvuMTuMFtlbcZD5n/xvo+3fVBwEaHJfW8ZyCRpJEhERERH7a7dFkt3FxcU1u6+n99R6Uf58/O0Sup42JhERERERu1CRZFPjx483OwQRERERkYDkMAzDMDsIO/jss88ICwszOwwREREREWklISEh9O3bt9F2FUkiIiIiIiL1BJkdgIiIiIiIiJWoSBIREREREalHRZKIiIiIiEg9KpJERERERETqUZEkIiIiIiJSj4okERERERGRelQk2ciWLVsYOnQoTqeTpUuXmh3OGSUmJpKamkpaWhrp6ekAHDp0iLFjx5KUlMTYsWM5fPgwAIZhMHfuXJxOJ6mpqXz++eemxJyZmcl1113HsGHDfNvOJ+Y1a9aQlJREUlISa9assUQezz33HDfeeCNpaWmkpaXx0Ucf+fa98MILOJ1Ohg4dyscff+zbbuY9d+DAAe666y5uu+02UlJSWLFiBWC//mguDzv1R2VlJaNGjWL48OGkpKTw7LPPArBv3z5Gjx6N0+lk0qRJVFVVAVBVVcWkSZNwOp2MHj2awsLCM+ZmZh7Tp08nMTHR1xd5eXmAde8pgNraWkaMGMF9990H2K8vmsvDjn3RWr/rzMyjqRzs9DOqTkVFBRMnTiQ5OZlbb72V7du3264vmsrBbn1RUFDgizUtLY0f/ehHvPTSS7brCwyxhZqaGmPIkCHG3r17jcrKSiM1NdX48ssvzQ7rtG655RajrKyswbZFixYZL7zwgmEYhvHCCy8Yjz/+uGEYhrF582YjIyPDOHnypLF9+3Zj1KhRfo/XMAwjNzfX2LVrl5GSknLeMXu9XiMxMdHwer3GoUOHjMTEROPQoUOm5/Hss88af/jDHxod++WXXxqpqalGZWWlsXfvXmPIkCFGTU2N6fdccXGxsWvXLsMwDOPIkSNGUlKS8eWXX9quP5rLw079cfLkSePo0aOGYRhGVVWVMWrUKGP79u3GxIkTjXXr1hmGYRizZs0yXnvtNcMwDOPVV181Zs2aZRiGYaxbt8546KGHTpubvzSXx29+8xvj/fffb3S8Ve8pwzCM5cuXG5MnTzbGjRtnGIZhu75oLg879kVr/K4zO4+mcrDTz6g606ZNM9544w3DMAyjsrLSOHz4sO36oqkc7NgXdWpqaozrr7/eKCwstF1faCTJJnbs2MEPfvADYmNjCQ0NJSUlhQ8++MDssM7ZBx98wIgRIwAYMWIEGzdubLDd4XBw1VVXUVFRQUlJid/ju+aaa+jatWuLYv7zn//MoEGD6NatG127dmXQoEF+/1/apvJozgcffEBKSgqhoaHExsbygx/8gB07dph+z0VFRXH55ZcD0LlzZ+Li4iguLrZdfzSXR3Os2B8Oh4NOnToBUFNTQ01NDQ6Hg08++YShQ4cCcPvtt/vi2bRpE7fffjsAQ4cOZdu2bRiG0Wxu/tJcHs2x6j118OBBNm/ezKhRo4BT/wtrt75oKo/TsWpfnClesMfPqXNhxZ9RAEeOHOGvf/2r734KDQ0lPDzcVn3RXA7NsWpf1Ldt2zZiY2Pp3bu3rfoC9LidbRQXFxMTE+P7Pjo6+rT/0LKKjIwM0tPTef311wEoKysjKioKgMjISMrKyoDG+cXExFgmv3ON2cp99dprr5GamkpmZqZvmLu5eK2UR2FhIXl5eQwYMMDW/VE/D7BXf9TW1pKWlsb111/P9ddfT2xsLOHh4YSEhAAN/84WFxfzve99D4CQkBC6dOmC1+s1PYem8qjri6eeeorU1FTmz5/ve1TNqvfU/PnzcblcBAWd+hXu9Xpt2Rf/mUcdO/VFnZb+rrNCHv+ZA9jrZ1RhYSERERFkZmYyYsQIZs6cyfHjx23VF83lAPbqi/reffdd36P/duoLUJEkbWjlypWsWbOGF198kddee42//vWvDfY7HI7T/i+uFdkx5jpjxozB4/GQnZ1NVFQUCxcuNDuks3Ls2DEmTpzIjBkz6Ny5c4N9duqP/8zDbv0RHBxMdnY2H330ETt27KCgoMDskM7Lf+bxz3/+k8mTJ7N+/XreeustDh8+bOk5nx9++CERERH079/f7FBapLk87NQXdQLhd11TOdjtZ1RNTQ27d+9mzJgxrF27lg4dOjS6f6zeF83lYLe+qFNVVcWmTZtITk5utM/qfQEqkmwjOjqagwcP+r4vLi4mOjraxIjOrC6+Hj164HQ62bFjBz169PA9RldSUkJERITv2Pr5HTx40DL5nWvMVu2rnj17EhwcTFBQEKNHj2bnzp1A8/eWFfKorq5m4sSJpKamkpSUBNizP5rKw479ARAeHk5CQgKfffYZFRUV1NTUAA3/zkZHR3PgwAHg1C/9I0eO0L17d8vkAP/O4+OPPyYqKgqHw0FoaCjp6enN9oUV7qm///3vbNq0icTERCZPnswnn3zCvHnzbNcXTeUxdepUW/VFndb4XWd2Hk3lYLefUTExMcTExPhGh5OTk9m9e7et+qK5HOzWF3W2bNnC5ZdfTs+ePQH7/f5WkWQTV1xxBV9//TX79u2jqqqKd999l8TERLPDatbx48c5evSo7+utW7fSt29fEhMTWbt2LQBr165lyJAhAL7thmHw2Wef0aVLF9+QrNnONeYbbriBP//5zxw+fJjDhw/z5z//mRtuuMHMFAAazPHauHEjffv2BU7l8e6771JVVcW+ffv4+uuvufLKK02/5wzDYObMmcTFxTF27Fjfdrv1R3N52Kk/ysvLqaioAODEiRP85S9/oU+fPiQkJLBhwwbg1BuI6uJJTEz0vYVow4YNXHvttTgcjmZz85em8oiLi/P1hWEYjfrCavfUlClT2LJlC5s2beLJJ5/k2muvZfHixbbri6byeOKJJ2zVF9B6v+vMzKO5HOz0MwpOPb4VExPjG+Xetm0bffr0sVVfNJeD3fqizrvvvktKSorvezv1BUCI364kLRISEsIjj9YLoOcAAA2/SURBVDzCr371K2praxk5cqTvL4kVlZWVMWHCBODUHIBhw4YxePBgrrjiCiZNmsSqVavo1asXTz/9NAA33XQTH330EU6nkw4dOjB//nxT4p48eTK5ubl4vV4GDx7Mr3/9a8aNG3dOMXfr1o0HHnjAN/FywoQJdOvWzfQ8cnNz2bNnDwC9e/dm9uzZAPTt25dbb72V2267jeDgYB555BGCg4MBTL3nPv30U7Kzs7n00ktJS0vz5WW3/mguj3Xr1tmmP0pKSpg+fTq1tbUYhkFycjK33HILl1xyCQ8//DBPP/00/fr1Y/To0QCMGjUKl8uF0+mka9euPPXUU2fMzcw87r77brxeL4ZhEB8fz2OPPQZY955qisvlslVfNGfq1Km26ovW+l1nZh7N5eByuWzzM6rOrFmzmDp1KtXV1cTGxrJgwQJOnjxpm75oLoe5c+fari+OHz/OX/7yF1+sgO1+fzsMwzD8djURERERERGL0+N2IiIiIiIi9ahIEhERERERqUdFkoiIiIiISD0qkkREREREROpRkSQiIiIiIlKPiiQRkRbyer2kpaWRlpbGoEGD/n979x8UVdXGAfy7iystoQ5JRcVMaQRWQPxYmmEm1iAEdLm7LMIOmhsStowMSTNELFQYZAg2ZrIZ05imyDTZGEWAIOSMIoNgEAxlNokw8WNIFAKRQX4sz/sH422RHyrq9L5vz+cv7+45zzn3nIvD4bn3Hvj5+YnHo6OjU8oeOHAAw8PDN42p1+vFDQPnqv/aa6+Je//8r+js7ERxcfE9iW00GhEQEACNRgOtVovGxkYAs4/nzz//jG3bts0ar66uDnFxcTN+9/bbb6OlpeWO+rtp0yb8+eef0Ov1ePHFF2H5wtn4+Hh4enreNMatlLnX5hqnu2X37t2oqam5p20wxth1vE8SY4zdITs7OxQVFQEATCYTbGxsEBsbO2PZ/Px8qNVqyOXyebV1Y/29e/fOr9P/oK6uLpSUlEAQhHsS/6233kJISAiqq6uRnp4+54LMzc0Nbm5u82rngw8+mG8XAUxuaNvf3w8HBwcAwKJFi9DQ0ACFQoErV67g0qVLdxR/vsxm83/Fnkk3SkxM/Ke7wBj7F+FMEmOM3QOnT59GWFgYBEFAamoqRkdHkZ+fj56eHkRHR0Ov1wMAtm7divDwcKhUKuTm5s4Zc6b6AQEB6OvrQ2dnJ0JCQmA0GhEcHIykpCTU1NQgKioKQUFBaG5uBjC5wV9qaioiIiIQFhaGH374YVo7PT09ePnll6HRaBAaGor6+noAkxmLrKwsqFQqREdHo6+vDwDQ3t6O2NhYhIeHY/369bhw4QKAyazOtm3bEBUVhZdeegnl5eUAgJ07d6K+vh4ajQYHDhyY0jYRIScnB6GhoRAEAUePHgUwmanQ6/XYsmULQkJCkJSUhJtt8+fj44P29nbxuLy8HBEREQgODhbPyTIDcubMGTEDGBYWhqtXr4pjNlO7ltkpT09P7Nq1C2q1GjqdDpcvXxbHRqfTQRAE7Nq1a0rWp66uDs8//7x4rFKpxPOtqKjAqlWrxO+GhoYQHR0NrVYLQRBmnLeMjAwcP34cwOSmi6mpqQCAI0eOiJvIxsfHi9fb4cOHxbqenp7Izs6GWq1GY2MjioqKEBERAY1Gg/T0dJjN5mntVVVVISQkBFqtFpWVleLn/f39iI+PhyAI0Ol04iaYJpMJKSkpWL9+Pfz9/VFRUYEdO3ZAEATExsZibGwMAPDJJ59g7dq1CA0NxbvvviuOt9FoFK+hgIAA5ObmiuNx/ZqbbQ4ZY+y2EWOMsbsmNzeX9uzZQ0qlklpbW4mIKDk5mb744gsiIvL396fe3l6x/F9//UVEROPj47RhwwY6d+4cERFt2LCBmpubp8W/sf71446ODnr66afpt99+I7PZTFqtloxGI01MTFBlZSVt3ryZiIh27txJ3333HRERDQwMUFBQEA0NDU1pY9++ffTpp5+K/RocHCQiImdnZyoqKiIiIpPJRBkZGURE9Morr1BbWxsRETU1NZFeryciopSUFHr99dfJbDbT+fPnKTAwkIiIamtryWAwzDh+5eXltHHjRhofH6dLly7RypUr6eLFi1RbW0teXl7U3d1NZrOZdDod/fjjj9Pqp6SkUFlZGRERHT16lCIiIsTx3L59OxERnThxgqKjo6f1JS4ujurr64mI6OrVqzQ2NjZnu5Zz5OzsTMePHyciopycHNqzZw8RERkMBiouLiYioi+//JI8PDzEvr7//vtUU1MjxmpqaqLQ0FAaHx+nmJgY6ujoEMuPjY2J89Db20uBgYE0MTFBRCSWKSkpoezsbCIiWrt2LUVGRhIRkdFopKqqKiL6+3obHh4mlUpFfX19Yv9LS0uJiKilpYXi4uJodHSUiIi2bt1K33777ZRxvnbtGimVSmpra6OJiQnasmWLOI6ZmZlkMpmIiKimpobUajURTf5sREVF0ejoKJ07d47c3d3pxIkTREQUHx9PlZWVU/pIRPTmm2+K42o5t/7+/pSfn09ERAUFBZSWljbrHDLG2HxwJokxxu6yiYkJODo6YtmyZQAArVYrZi5uVFZWBq1Wi7CwMJw/f178i/h8ODo6wsXFBVKpFE5OTvD19YVEIoGLiwu6uroAANXV1di7dy80Gg30ej1GRkbQ3d09JY6bmxsKCwthMpnw+++/w9bWFgAglUqxZs0aAIBGo0FDQwOGhobQ2NiIxMREMetgeZtYYGCg2J/r2ZW5NDQ0QKVSwcrKCvb29vDx8RGzNe7u7nBwcIBUKsWKFSvEc7rRjh07oNFo8PXXX0+5Je56ZubZZ5+dsa6Xlxeys7ORn5+PwcFBLFiw4Jbblclk8Pf3BwC4urqKZZqamhASEgIA024v/Omnn+Dt7S0eS6VSeHt7o7S0FNeuXYOjo6P4HRHho48+giAIiImJwcWLF6eNp0KhQENDA1paWuDk5ISlS5eip6cHjY2NYgbr0KFDYraru7sbf/zxBwDAysoKwcHBACazoL/88ouYSTp9+jQ6OjqmtNXa2gpHR0c88cQTkEgkUKvV4ncNDQ3QaDQAAF9fX/T394sZHaVSCZlMBmdnZ5jNZiiVSgCAs7MzOjs7AUxm2CIjIyEIAmpra2d97isoKGjaeM82h4wxdrv4fw/GGPuHdHR0YP/+/Thy5AiWLFkCo9GIkZGRecdbuHCh+G+pVCoeSySSKbdL5ebmYvny5bPG8fHxQUFBAU6ePAmj0YiYmBiEhYVNKyeRSEBEWLx4sfhM1lx9ulOWsaysrGa8BQz4+5mk2epLpdIZ6xoMBqxcuRInT57EunXr8Pnnn99yuzKZDBKJZM74ljo6OuDg4DBtfFQqFRISEpCQkDDl8+LiYvT19aGwsBAymQwBAQHTrpWHH34YV65cwalTp6BQKDAwMICysjLY2NjA1tYWdXV1qKmpweHDhyGXy8VFMgBYW1uLzyEREbRaLZKSkuY8h/mwnIOZxmxkZAQZGRn45ptv8Mgjj8BkMs36MyGTyabUBWaewyeffPKunwdj7P8fZ5IYY+wuk0ql6OrqEv9KX1RUBB8fHwDA/fffj6GhIQCTz5nI5XIsWrQIly9fRlVV1U1jW9afjxdeeAEFBQXicx6//vrrtDJdXV2wt7eHTqdDZGQkzp49C2AyQ3bs2DEAk7+0e3t7w9bWFo6OjigrKwMw+Qv29WdQ5nMOCoUCZWVlMJvN6OvrQ319Pdzd3ed9vrejvb0dLi4uMBgMcHNzQ1tb2x3HfO6551BRUQEAKC0tFT+vqqqCn5/ftPIKhQIGgwEqlWrK54ODg1i6dClkMhlqa2tnzaJ5eHjg4MGD8PHxgUKhwP79+6FQKMQYS5YsgVwux4ULF9DU1DRjDF9fXxw7dgy9vb0AJp8xurG95cuXo6urS3zmy/LcFAoFvv/+ewCTWSE7OzsxG3kz1xdEdnZ2GBoaEq+3W3Uv5pAx9u/EmSTGGLvLrK2tsX37diQmJsJsNsPV1RXr1q0DAOh0OmzatAkPPfQQDh06hGeeeQarV6+Gg4MDvLy8bhr7xvq3Kz4+HllZWVCr1eJtgZ999tmUMmfOnMG+ffuwYMEC2NjYICcnBwBgY2OD5uZm5OXl4YEHHsDHH38MAPjwww/x3nvvIS8vD+Pj41izZg1WrFgxax+u3xKoVqsRHh6OjRs3it+tWrUKjY2N0Gg0kEgkSE5OxoMPPojW1tbbPtfbdfDgQdTV1UEikeCpp56CUqkUXyE+X2lpaUhOTkZeXh78/PzExcKpU6fwzjvvTCsvkUhmfDOiIAjYvHkzBEGAq6vrrJlAb29vVFdX4/HHH8ejjz6KgYEBcZGkVCrx1VdfYfXq1Vi2bBk8PDxmjOHk5IQ33ngDr776KiYmJiCTyZCeno7HHntMLGNtbY3MzEwYDAbI5XJ4e3uLC9+EhASkpaVBEATI5XJkZ2ff8ngtXrwYkZGRCA0Nhb29/W2/eXCmOWSMsfmQEN3k9UCMMcYYJt+AdqeLhn+b4eFh3HfffZBIJCgtLUVJSQl2796NqKgoFBYW/tPdY4wxNgvOJDHGGGP3yNmzZ5GZmSk+u5WVlYWFCxfyAokxxv7LcSaJMcYYY4wxxizwixsYY4wxxhhjzAIvkhhjjDHGGGPMAi+SGGOMMcYYY8wCL5IYY4wxxhhjzAIvkhhjjDHGGGPMwn8AdnzTJHmT4UYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=['total_time_phishing', 'total_time_malware'], var_name='model', value_name='total_time')\n",
    "adf['model'] = adf['model'].replace({'total_time_phishing':'Phishing (PhishTank)', 'total_time_malware':'Malware (Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"total_time\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total time spent on Phishing/Malware domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_time_phishing_malware_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=['total_visits_phishing', 'total_visits_malware'], var_name='model', value_name='total_visits')\n",
    "adf['model'] = adf['model'].replace({'total_visits_phishing':'Phishing (PhishTank)', 'total_visits_malware':'Malware (Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"total_visits\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Total visits to Phishing/Malware domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(500))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(100))\n",
    "\n",
    "g.savefig('./total_visits_phishing_malware_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">total_time_tl_malware</th>\n",
       "      <th colspan=\"9\" halign=\"left\">total_visits_tl_malware</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>85%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>85%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hoh_oldest_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18-20</th>\n",
       "      <td>1560</td>\n",
       "      <td>130</td>\n",
       "      <td>310</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>38</td>\n",
       "      <td>115</td>\n",
       "      <td>207</td>\n",
       "      <td>6742</td>\n",
       "      <td>1560</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-24</th>\n",
       "      <td>2812</td>\n",
       "      <td>166</td>\n",
       "      <td>654</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>144</td>\n",
       "      <td>254</td>\n",
       "      <td>28892</td>\n",
       "      <td>2812</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>4958</td>\n",
       "      <td>193</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>170</td>\n",
       "      <td>308</td>\n",
       "      <td>12048</td>\n",
       "      <td>4958</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>5714</td>\n",
       "      <td>240</td>\n",
       "      <td>1154</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>66</td>\n",
       "      <td>205</td>\n",
       "      <td>382</td>\n",
       "      <td>77749</td>\n",
       "      <td>5714</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>6820</td>\n",
       "      <td>236</td>\n",
       "      <td>1298</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>72</td>\n",
       "      <td>219</td>\n",
       "      <td>381</td>\n",
       "      <td>98970</td>\n",
       "      <td>6820</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-44</th>\n",
       "      <td>7919</td>\n",
       "      <td>240</td>\n",
       "      <td>588</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>229</td>\n",
       "      <td>398</td>\n",
       "      <td>16357</td>\n",
       "      <td>7919</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49</th>\n",
       "      <td>10900</td>\n",
       "      <td>241</td>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>69</td>\n",
       "      <td>227</td>\n",
       "      <td>410</td>\n",
       "      <td>22722</td>\n",
       "      <td>10900</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-54</th>\n",
       "      <td>12461</td>\n",
       "      <td>238</td>\n",
       "      <td>867</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "      <td>216</td>\n",
       "      <td>402</td>\n",
       "      <td>53732</td>\n",
       "      <td>12461</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>1202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-59</th>\n",
       "      <td>9201</td>\n",
       "      <td>286</td>\n",
       "      <td>710</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>89</td>\n",
       "      <td>290</td>\n",
       "      <td>511</td>\n",
       "      <td>37851</td>\n",
       "      <td>9201</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-64</th>\n",
       "      <td>6463</td>\n",
       "      <td>341</td>\n",
       "      <td>714</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>116</td>\n",
       "      <td>371</td>\n",
       "      <td>628</td>\n",
       "      <td>19090</td>\n",
       "      <td>6463</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>42</td>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 and over</th>\n",
       "      <td>12595</td>\n",
       "      <td>339</td>\n",
       "      <td>670</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>358</td>\n",
       "      <td>630</td>\n",
       "      <td>15011</td>\n",
       "      <td>12595</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>4</td>\n",
       "      <td>69</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>93</td>\n",
       "      <td>146</td>\n",
       "      <td>226</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_time_tl_malware                                           \\\n",
       "                               count mean   std min 25%  50%  75%  85%    max   \n",
       "hoh_oldest_age                                                                  \n",
       "18-20                           1560  130   310   0  12   38  115  207   6742   \n",
       "21-24                           2812  166   654   0  12   45  144  254  28892   \n",
       "25-29                           4958  193   491   0  15   55  170  308  12048   \n",
       "30-34                           5714  240  1154   0  19   66  205  382  77749   \n",
       "35-39                           6820  236  1298   0  22   72  219  381  98970   \n",
       "40-44                           7919  240   588   0  20   75  229  398  16357   \n",
       "45-49                          10900  241   613   0  16   69  227  410  22722   \n",
       "50-54                          12461  238   867   0  11   59  216  402  53732   \n",
       "55-59                           9201  286   710   0  22   89  290  511  37851   \n",
       "60-64                           6463  341   714   0  32  116  371  628  19090   \n",
       "65 and over                    12595  339   670   0  33  114  358  630  15011   \n",
       "Missing                            4   69   106   1   1   25   93  146    226   \n",
       "\n",
       "               total_visits_tl_malware                                     \n",
       "                                 count mean std min 25% 50% 75% 85%   max  \n",
       "hoh_oldest_age                                                             \n",
       "18-20                             1560    8  12   0   1   4  10  16   135  \n",
       "21-24                             2812   10  17   0   1   5  12  20   283  \n",
       "25-29                             4958   12  20   0   2   5  14  23   349  \n",
       "30-34                             5714   14  21   0   2   6  17  27   371  \n",
       "35-39                             6820   14  20   0   2   7  18  27   347  \n",
       "40-44                             7919   15  22   0   2   7  18  28   459  \n",
       "45-49                            10900   15  24   0   2   7  19  29   679  \n",
       "50-54                            12461   14  27   0   1   6  18  29  1202  \n",
       "55-59                             9201   18  26   0   2   8  23  35   461  \n",
       "60-64                             6463   22  35   0   3  11  29  42  1340  \n",
       "65 and over                      12595   21  32   0   3  11  28  42   956  \n",
       "Missing                              4    5   6   0   0   2   7  10    14  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_oldest_age'])['total_time_tl_malware', 'total_visits_tl_malware'].describe(percentiles = [.25, .50, .75, .85]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=['total_time_tl_malware'], var_name='model', value_name='total_time')\n",
    "g = sns.catplot(x=\"total_time\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('Malware (Toulouse)');\n",
    "g.set_xlabels('Total time spent on Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_time_tl_malware_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3331: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resultiing Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=['total_visits_tl_malware'], var_name='model', value_name='total_visits')\n",
    "g = sns.catplot(x=\"total_visits\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_ylabels('');\n",
    "g.set_titles('Malware (Toulouse)');\n",
    "g.set_xlabels('Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(100))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(50))\n",
    "    \n",
    "g.savefig('./total_visits_tl_malware_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of time, visits\n",
    "\n",
    "We think some of the patterns we see are a reflection of the total time people spend online. And exposure to problems may be greater just as a result of that. And that is an important insight. We also believe though that the more educated, more instance, are less likely---adjusted for frequency---to go to phishing and malware sites because they have greater skills. So we now look at proportions. The data are roughly in line with expectations for education with the least educated (remember this is household level) spending the largest share of their time (visits) on phishing and malware sites and the most educated spending the least. For race as well the pattern is flipped. For age, we don't expect such a pattern and that is indeed what we find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf['prop_phishing_visits'] = mdf['total_visits_phishing']/mdf['total_visits']\n",
    "mdf['prop_phishing_time'] = mdf['total_time_phishing']/mdf['total_time']\n",
    "\n",
    "mdf['prop_malware_visits'] = mdf['total_visits_malware']/mdf['total_visits']\n",
    "mdf['prop_malware_time']   = mdf['total_time_malware']/mdf['total_time']\n",
    "\n",
    "mdf['prop_tl_malware_visits'] = mdf['total_visits_tl_malware']/mdf['total_visits']\n",
    "mdf['prop_tl_malware_time']   = mdf['total_time_tl_malware']/mdf['total_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp_prop_visits = ['prop_phishing_visits', 'prop_malware_visits', 'prop_tl_malware_visits']\n",
    "grp_prop_time = ['prop_phishing_time', 'prop_malware_time', 'prop_tl_malware_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>prop_phishing_visits</th>\n",
       "      <th>prop_phishing_time</th>\n",
       "      <th>prop_malware_visits</th>\n",
       "      <th>prop_malware_time</th>\n",
       "      <th>prop_tl_malware_visits</th>\n",
       "      <th>prop_tl_malware_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hoh_most_education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Associate degree</th>\n",
       "      <td>0.177</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor’s degree</th>\n",
       "      <td>0.176</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate degree</th>\n",
       "      <td>0.176</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school diploma or equivalent</th>\n",
       "      <td>0.174</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>0.177</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college but no degree</th>\n",
       "      <td>0.178</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   prop_phishing_visits  prop_phishing_time  \\\n",
       "hoh_most_education                                                            \n",
       "Associate degree                                  0.177               0.152   \n",
       "Bachelor’s degree                                 0.176               0.149   \n",
       "Graduate degree                                   0.176               0.140   \n",
       "High school diploma or equivalent                 0.174               0.153   \n",
       "Missing                                           0.177               0.159   \n",
       "Some college but no degree                        0.178               0.154   \n",
       "\n",
       "                                   prop_malware_visits  prop_malware_time  \\\n",
       "hoh_most_education                                                          \n",
       "Associate degree                                 0.113              0.101   \n",
       "Bachelor’s degree                                0.112              0.098   \n",
       "Graduate degree                                  0.111              0.092   \n",
       "High school diploma or equivalent                0.113              0.105   \n",
       "Missing                                          0.117              0.110   \n",
       "Some college but no degree                       0.115              0.105   \n",
       "\n",
       "                                   prop_tl_malware_visits  \\\n",
       "hoh_most_education                                          \n",
       "Associate degree                                    0.018   \n",
       "Bachelor’s degree                                   0.018   \n",
       "Graduate degree                                     0.018   \n",
       "High school diploma or equivalent                   0.018   \n",
       "Missing                                             0.019   \n",
       "Some college but no degree                          0.018   \n",
       "\n",
       "                                   prop_tl_malware_time  \n",
       "hoh_most_education                                       \n",
       "Associate degree                                  0.018  \n",
       "Bachelor’s degree                                 0.018  \n",
       "Graduate degree                                   0.018  \n",
       "High school diploma or equivalent                 0.018  \n",
       "Missing                                           0.019  \n",
       "Some college but no degree                        0.018  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_most_education'])['prop_phishing_visits', 'prop_phishing_time',\n",
    "                                    'prop_malware_visits', 'prop_malware_time',\n",
    "                                    'prop_tl_malware_visits', 'prop_tl_malware_time'].mean().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=grp_prop_visits, var_name='model', value_name='prop_total_visits')\n",
    "\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_visits':'Phishing (PhishTank)', 'prop_malware_visits':'Malware (Malware)', 'prop_tl_malware_visits':'Malware (Toulouse Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"prop_total_visits\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.01))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.05))\n",
    "\n",
    "g.savefig('./prop_total_visits_phishing_malware_tl_educ.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_most_education'], value_vars=grp_prop_time, var_name='model', value_name='prop_total_time')\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_time':'Phishing (PhishTank)', 'prop_malware_time':'Malware (Malware)', 'prop_tl_malware_time':'Malware (Toulouse Malware)'})\n",
    "\n",
    "g = sns.catplot(x=\"prop_total_time\", y=\"hoh_most_education\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=edu.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of time spent on Malware/Phishing domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.05))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.01))\n",
    "\n",
    "g.savefig('./prop_total_time_phishing_malware_tl_educ.eps', format='eps', dpi=300)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>prop_phishing_visits</th>\n",
       "      <th>prop_phishing_time</th>\n",
       "      <th>prop_malware_visits</th>\n",
       "      <th>prop_malware_time</th>\n",
       "      <th>prop_tl_malware_visits</th>\n",
       "      <th>prop_tl_malware_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>racial_background</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>0.170</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0.182</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>0.188</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.174</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>0.178</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   prop_phishing_visits  prop_phishing_time  \\\n",
       "racial_background                                             \n",
       "Asian                             0.170               0.144   \n",
       "Black                             0.182               0.163   \n",
       "Missing                           0.188               0.199   \n",
       "Other                             0.174               0.155   \n",
       "White                             0.178               0.154   \n",
       "\n",
       "                   prop_malware_visits  prop_malware_time  \\\n",
       "racial_background                                           \n",
       "Asian                            0.109              0.094   \n",
       "Black                            0.119              0.111   \n",
       "Missing                          0.111              0.114   \n",
       "Other                            0.114              0.106   \n",
       "White                            0.115              0.104   \n",
       "\n",
       "                   prop_tl_malware_visits  prop_tl_malware_time  \n",
       "racial_background                                                \n",
       "Asian                               0.018                 0.017  \n",
       "Black                               0.019                 0.019  \n",
       "Missing                             0.017                 0.014  \n",
       "Other                               0.018                 0.018  \n",
       "White                               0.018                 0.019  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['racial_background'])['prop_phishing_visits', 'prop_phishing_time',\n",
    "                                   'prop_malware_visits', 'prop_malware_time',\n",
    "                                   'prop_tl_malware_visits', 'prop_tl_malware_time'].mean().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=grp_prop_visits, var_name='model', value_name='prop_total_visits')\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_visits':'Phishing (PhishTank)', 'prop_malware_visits':'Malware (Malware)', 'prop_tl_malware_visits':'Malware (Toulouse Malware)'})\n",
    "g = sns.catplot(x=\"prop_total_visits\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.01))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.001))\n",
    "\n",
    "g.savefig('./prop_total_visits_phishing_malware_tl_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['racial_background'], value_vars=grp_prop_time, var_name='model', value_name='prop_total_time')\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_time':'Phishing (PhishTank)', 'prop_malware_time':'Malware (Malware)', 'prop_tl_malware_time':'Malware (Toulouse Malware)'})\n",
    "g = sns.catplot(x=\"prop_total_time\", y=\"racial_background\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=race.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of time spent on Malware/Phishing domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.05))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.01))\n",
    "\n",
    "g.savefig('./prop_total_time_phishing_malware_tl_race.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given plausible skew in total visits and time spent, we also check how medians look across income, education, and race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/venv37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>prop_phishing_visits</th>\n",
       "      <th>prop_phishing_time</th>\n",
       "      <th>prop_malware_visits</th>\n",
       "      <th>prop_malware_time</th>\n",
       "      <th>prop_tl_malware_visits</th>\n",
       "      <th>prop_tl_malware_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hoh_oldest_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18-20</th>\n",
       "      <td>0.166</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21-24</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29</th>\n",
       "      <td>0.171</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39</th>\n",
       "      <td>0.174</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-44</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49</th>\n",
       "      <td>0.178</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-54</th>\n",
       "      <td>0.179</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-59</th>\n",
       "      <td>0.179</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-64</th>\n",
       "      <td>0.180</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 and over</th>\n",
       "      <td>0.182</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>0.161</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                prop_phishing_visits  prop_phishing_time  prop_malware_visits  \\\n",
       "hoh_oldest_age                                                                  \n",
       "18-20                          0.166               0.140                0.109   \n",
       "21-24                          0.168               0.145                0.108   \n",
       "25-29                          0.171               0.149                0.111   \n",
       "30-34                          0.175               0.155                0.113   \n",
       "35-39                          0.174               0.151                0.112   \n",
       "40-44                          0.175               0.154                0.113   \n",
       "45-49                          0.178               0.155                0.115   \n",
       "50-54                          0.179               0.159                0.117   \n",
       "55-59                          0.179               0.158                0.116   \n",
       "60-64                          0.180               0.153                0.116   \n",
       "65 and over                    0.182               0.157                0.118   \n",
       "Missing                        0.161               0.132                0.101   \n",
       "\n",
       "                prop_malware_time  prop_tl_malware_visits  \\\n",
       "hoh_oldest_age                                              \n",
       "18-20                       0.095                   0.017   \n",
       "21-24                       0.097                   0.018   \n",
       "25-29                       0.100                   0.018   \n",
       "30-34                       0.104                   0.018   \n",
       "35-39                       0.102                   0.018   \n",
       "40-44                       0.104                   0.018   \n",
       "45-49                       0.105                   0.018   \n",
       "50-54                       0.109                   0.019   \n",
       "55-59                       0.107                   0.018   \n",
       "60-64                       0.104                   0.018   \n",
       "65 and over                 0.108                   0.019   \n",
       "Missing                     0.076                   0.017   \n",
       "\n",
       "                prop_tl_malware_time  \n",
       "hoh_oldest_age                        \n",
       "18-20                          0.017  \n",
       "21-24                          0.017  \n",
       "25-29                          0.017  \n",
       "30-34                          0.018  \n",
       "35-39                          0.018  \n",
       "40-44                          0.018  \n",
       "45-49                          0.018  \n",
       "50-54                          0.020  \n",
       "55-59                          0.019  \n",
       "60-64                          0.019  \n",
       "65 and over                    0.020  \n",
       "Missing                        0.016  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.groupby(['hoh_oldest_age'])['prop_phishing_visits', 'prop_phishing_time',\n",
    "                                'prop_malware_visits', 'prop_malware_time',\n",
    "                                'prop_tl_malware_visits', 'prop_tl_malware_time'].mean().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=grp_prop_visits, var_name='model', value_name='prop_total_visits')\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_visits':'Phishing (PhishTank)', 'prop_malware_visits':'Malware (Malware)', 'prop_tl_malware_visits':'Malware (Toulouse Malware)'})\n",
    "g = sns.catplot(x=\"prop_total_visits\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of Total visits to Bad domain')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.01))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.001))\n",
    "\n",
    "g.savefig('./prop_total_visits_phishing_malware_tl_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adf = pd.melt(mdf, id_vars=['hoh_oldest_age'], value_vars=grp_prop_time, var_name='model', value_name='prop_total_time')\n",
    "adf['model'] = adf['model'].replace({'prop_phishing_time':'Phishing (PhishTank)', 'prop_malware_time':'Malware (Malware)', 'prop_tl_malware_time':'Malware (Toulouse Malware)'})\n",
    "g = sns.catplot(x=\"prop_total_time\", y=\"hoh_oldest_age\",\n",
    "                col='model', col_wrap=1, height=4, aspect=3,\n",
    "                kind=\"box\", data=adf, showfliers=False, order=age.values());\n",
    "g.set_titles(row_template = '{row_name}', col_template = '{col_name}')\n",
    "g.set_ylabels('');\n",
    "g.set_xlabels('Proportion of time spent on Malware/Phishing domains')\n",
    "\n",
    "for ax in g.axes.flatten():\n",
    "    ax.xaxis.set_major_locator(tkr.MultipleLocator(0.05))\n",
    "    ax.xaxis.set_minor_locator(tkr.MultipleLocator(0.01))\n",
    "\n",
    "g.savefig('./prop_total_time_phishing_malware_tl_age.eps', format='eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}