naughty-passwords.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naugty Passwords\n",
    "\n",
    "What are the naughty words which are used most frequently in passwords?\n",
    "\n",
    "We need two things\n",
    "\n",
    "- A list of naughty words.\n",
    "- An API to provide how frequently they are used.\n",
    "\n",
    "The first is provided by [https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) and The Second by Have I been pwned API.\n",
    "\n",
    "Lets get started. Scroll to the bottom to see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some imports\n",
    "\n",
    "import requests\n",
    "import pandas\n",
    "from pwnedapi import Password\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the list of naugthy words\n",
    "naughty_words = requests.get(\"https://raw.githubusercontent.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words/master/en\").text.splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use pwnedapi to get the number of time the word has been used as a pasword.\n",
    "\n",
    "password = Password(naughty_words[1])\n",
    "naughty_word_with_password = [(word, Password(word)) for  word in naughty_words]\n",
    "def check_is_pwned(word, pasword):\n",
    "    is_pwned = pasword.is_pwned()\n",
    "    # print(word)\n",
    "    # print(pasword.pwned_count)\n",
    "    return is_pwned\n",
    "naughty_word_with_password= [el for el in naughty_word_with_password if check_is_pwned(*el)]\n",
    "naughty_word_with_pwned_count = {word: password.pwned_count for word, password in naughty_word_with_password}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the top 40 words\n",
    "naughty_password = sorted(naughty_word_with_pwned_count.items(), key=lambda x: x[1], reverse=True)[:40]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('asshole', 171492),\n",
       " ('playboy', 113212),\n",
       " ('sexy', 104937),\n",
       " ('pussy', 98400),\n",
       " ('fuck', 95003),\n",
       " ('bitch', 86676),\n",
       " ('hardcore', 80829),\n",
       " ('lolita', 77944),\n",
       " ('bullshit', 69194),\n",
       " ('nigger', 62543),\n",
       " ('sex', 60045),\n",
       " ('nigga', 43158),\n",
       " ('penis', 43089),\n",
       " ('bitches', 41022),\n",
       " ('xxx', 35696),\n",
       " ('fucking', 34299),\n",
       " ('bollocks', 34028),\n",
       " ('shit', 33606),\n",
       " ('vagina', 31940),\n",
       " ('dick', 31770),\n",
       " ('bastard', 27544),\n",
       " ('motherfucker', 25342),\n",
       " ('boobs', 24626),\n",
       " ('blowjob', 22438),\n",
       " ('ass', 21206),\n",
       " ('hentai', 17317),\n",
       " ('escort', 16406),\n",
       " ('faggot', 15262),\n",
       " ('beaner', 14724),\n",
       " ('porn', 13993),\n",
       " ('panties', 13707),\n",
       " ('porno', 13686),\n",
       " ('cock', 13593),\n",
       " ('butthole', 13392),\n",
       " ('butt', 13155),\n",
       " ('titties', 12850),\n",
       " ('hooker', 12317),\n",
       " ('tits', 11645),\n",
       " ('slut', 11506),\n",
       " ('cunt', 11377)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the naughty passwords?\n",
    "naughty_password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the data in a dataframe\n",
    "df = pandas.DataFrame(naughty_password, columns=[\"word\", \"pwned_count\"])\n",
    "df = df.set_index(\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115b119e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [15, 10]\n",
    "\n",
    "plt = df.plot.barh()\n",
    "plt"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}