Jupyterthon-2022-Analysis of Billion Passwords.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Analyzing-billions-of-passwords-from-Breach-compilation-dataset\" data-toc-modified-id=\"Analyzing-billions-of-passwords-from-Breach-compilation-dataset-1\">Analyzing billions of passwords from Breach compilation dataset</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-Acquisition\" data-toc-modified-id=\"Data-Acquisition-1.1\">Data Acquisition</a></span></li><li><span><a href=\"#Data-Preparation/Cleaning\" data-toc-modified-id=\"Data-Preparation/Cleaning-1.2\">Data Preparation/Cleaning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-files-from-the-dir-recursively\" data-toc-modified-id=\"Load-files-from-the-dir-recursively-1.2.1\">Load files from the dir recursively</a></span></li><li><span><a href=\"#Dask-Dataframe-demo\" data-toc-modified-id=\"Dask-Dataframe-demo-1.2.2\">Dask Dataframe demo</a></span></li><li><span><a href=\"#Data-manipulation-via-Dask\" data-toc-modified-id=\"Data-manipulation-via-Dask-1.2.3\">Data manipulation via Dask</a></span></li></ul></li><li><span><a href=\"#Data-Analysis\" data-toc-modified-id=\"Data-Analysis-1.3\">Data Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Password-length-summary-dataset\" data-toc-modified-id=\"Password-length-summary-dataset-1.3.1\">Password length summary dataset</a></span></li><li><span><a href=\"#Domain-summary-statistics-dataset\" data-toc-modified-id=\"Domain-summary-statistics-dataset-1.3.2\">Domain summary statistics dataset</a></span></li></ul></li><li><span><a href=\"#Data-Enrichment\" data-toc-modified-id=\"Data-Enrichment-1.4\">Data Enrichment</a></span><ul class=\"toc-item\"><li><span><a href=\"#Fortune-500-dataset-enrichment\" data-toc-modified-id=\"Fortune-500-dataset-enrichment-1.4.1\">Fortune 500 dataset enrichment</a></span></li><li><span><a href=\"#Merging-with-Domain-summary-dataset\" data-toc-modified-id=\"Merging-with-Domain-summary-dataset-1.4.2\">Merging with Domain summary dataset</a></span></li></ul></li><li><span><a href=\"#Data-Visualization\" data-toc-modified-id=\"Data-Visualization-1.5\">Data Visualization</a></span><ul class=\"toc-item\"><li><span><a href=\"#Histogram-for-Password-Length\" data-toc-modified-id=\"Histogram-for-Password-Length-1.5.1\">Histogram for Password Length</a></span></li><li><span><a href=\"#Bar-chart-of-No-of-companies-per-Sector\" data-toc-modified-id=\"Bar-chart-of-No-of-companies-per-Sector-1.5.2\">Bar chart of No of companies per Sector</a></span></li><li><span><a href=\"#Password-Length-trends-in-Fortune-500-companies\" data-toc-modified-id=\"Password-Length-trends-in-Fortune-500-companies-1.5.3\">Password Length trends in Fortune 500 companies</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-1.6\">Conclusion</a></span></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-1.7\">References</a></span></li></ul></li></ul></div>"
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    "# Analyzing billions of passwords from Breach compilation dataset\n",
    "\n",
    "In this notebook, we will take a look at old database dump of 1.4 billion clear text credentials which was compilation of several data breaches including sites such as yahoo.com, LinkedIn etc. The complete database is an archive of 41 GB with around 1980 files. We will take a look at how this dataset can be loaded for scalable data analysis using python and perform common data cleaning and preparation steps. \n",
    "\n",
    "The goal of this presentation is to demonstrate how one can take real world large dataset, clean, enrich and perform data analysis at scale to extract interesting insights using Jupyter notebook."
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"
    }
   },
   "cell_type": "markdown",
   "id": "3c7ecd91",
   "metadata": {},
   "source": [
    "## Data Acquisition\n",
    "\n",
    "The links to original archive of 41 GB having 1981 files were found on various social media blogs \n",
    "with common [Pastebin](https://pastebin.com/R8Aj8Ncq) link containing both compressed \n",
    "and uncompressed version of the archive. The data is structured in an alphabetic directory \n",
    "tree fragmented in 1,981 pieces to allow fast searches. \n",
    "The dump includes search tools and insert scripts explained in a README file\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37a3cb5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:21:22.065146Z",
     "start_time": "2022-12-02T17:21:21.370592Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "!cat imported.log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ac8591",
   "metadata": {},
   "source": [
    "## Data Preparation/Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b910cb4",
   "metadata": {},
   "source": [
    "- Files were with no extension. Use simple powershell to rename files and added .txt extension\n",
    "```powershell\n",
    "#To add extension to files\n",
    "Get-ChildItem -Path \"C:\\BreachCompilation/\" -File | Rename-Item -NewName { $PSItem.Name + \".txt\" }\n",
    "```\n",
    "\n",
    "- Read each file and generate metata - No of lines and identify duplicates\n",
    "\n",
    "    - Total files : 1986\n",
    "    - Total Records: 140,055,3869 (1.4 Billion), \n",
    "    - Same Email Id and Password : 647701 - 0.04 %\n",
    "\n",
    "***Out of Scope for Analysis***\n",
    "\n",
    "    - No of duplicates : 202,281 (202K) - 0.014 %\n",
    "    - Files with invalid data (all Null chars) :  105 Files - 1.93 GB - 4.8 %\n",
    "    - Entries with Email but No Password : 2546790 - 0.18%\n",
    "    - Entries with Password less than 6 chars : 40140152 - 2.86 % \n",
    "\n",
    "- Parse UserName:Password pair into 4 columns - EMail, Password, EmailID, EmailDomain.\n",
    "\n",
    "- Output the final processed dataset for further analysis\n",
    "\n",
    "***R Code References:***\n",
    "- [Data Preprations-FileStats](https://github.com/ashwin-patil/springboard-intro-to-datascience/blob/master/capstone_project/00_Data_Preparation/DataPreparation-FileStats.R)\n",
    "- [DataCleaning-Dups_NA_lessthan6](https://github.com/ashwin-patil/springboard-intro-to-datascience/blob/master/capstone_project/01_Data_Cleaning/DataCleaning-Dups_NA_lessthan6.R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea137644",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:21:31.322724Z",
     "start_time": "2022-12-02T17:21:28.835733Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import dask.dataframe as dd\n",
    "import pandas as pd\n",
    "from urllib.parse import urlparse\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set(font_scale=1.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42072c9f",
   "metadata": {},
   "source": [
    "### Load files from the dir recursively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91d8df42",
   "metadata": {
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16a9d9ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:21:38.657854Z",
     "start_time": "2022-12-02T17:21:38.413817Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('/home/jovyan/work/Jupyterthon/BreachCompilation/Part01/*.txt', recursive = True)\n",
    "files = files + glob.glob('/home/jovyan/work/Jupyterthon/BreachCompilation/Part01/*/*/*.txt', recursive = True)\n",
    "files = files + glob.glob('/home/jovyan/work/Jupyterthon/BreachCompilation/Part01/*/*/*/*.txt', recursive = True)\n",
    "files = files + glob.glob('/home/jovyan/work/Jupyterthon/BreachCompilation/Part01/*/*/*/*/*.txt', recursive = True)\n",
    "\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8784111f",
   "metadata": {},
   "source": [
    "### Dask Dataframe demo\n",
    "\n",
    "Typical Use cases\n",
    "    - Manipulating large datasets, even when those datasets don’t fit in memory\n",
    "    - Accelerating long computations by using many cores\n",
    "    - Distributed computing on large datasets with standard pandas operations like groupby, join, and time series computations\n",
    "    \n",
    "**Docs::**\n",
    "- [10 mins to Dask](https://docs.dask.org/en/stable/10-minutes-to-dask.html)\n",
    "- [Best Practices](https://docs.dask.org/en/stable/best-practices.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3000b839",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:21:53.384724Z",
     "start_time": "2022-12-02T17:21:52.864823Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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 style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMail</th>\n",
       "      <th>Password</th>\n",
       "      <th>EMailID</th>\n",
       "      <th>EMailDomain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=146</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: read-csv, 146 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                  EMail Password EMailID EMailDomain\n",
       "npartitions=146                                     \n",
       "                 object   object  object      object\n",
       "                    ...      ...     ...         ...\n",
       "...                 ...      ...     ...         ...\n",
       "                    ...      ...     ...         ...\n",
       "                    ...      ...     ...         ...\n",
       "Dask Name: read-csv, 146 tasks"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_files = dd.read_csv(files, on_bad_lines='skip', encoding='latin1', sep=\"\\t\", blocksize=\"25MB\")\n",
    "all_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f8d2f26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:21:56.677673Z",
     "start_time": "2022-12-02T17:21:56.617290Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>EMail</th>\n",
       "      <th>Password</th>\n",
       "      <th>EMailID</th>\n",
       "      <th>EMailDomain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL00000@aol.com</td>\n",
       "      <td>bazookaz9</td>\n",
       "      <td>AL00000</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL00001-06.12.1992@mail.ru</td>\n",
       "      <td>A00001</td>\n",
       "      <td>AL00001-06.12.1992</td>\n",
       "      <td>mail.ru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL00001.92@mail.ru</td>\n",
       "      <td>Gonchik</td>\n",
       "      <td>AL00001.92</td>\n",
       "      <td>mail.ru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL00013@yandex.ru</td>\n",
       "      <td>ibkjdf1330</td>\n",
       "      <td>AL00013</td>\n",
       "      <td>yandex.ru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL0001NG@e.ntu.edu.sg</td>\n",
       "      <td>myanmarpyi</td>\n",
       "      <td>AL0001NG</td>\n",
       "      <td>e.ntu.edu.sg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        EMail    Password             EMailID   EMailDomain\n",
       "0             AL00000@aol.com   bazookaz9             AL00000       aol.com\n",
       "1  AL00001-06.12.1992@mail.ru      A00001  AL00001-06.12.1992       mail.ru\n",
       "2          AL00001.92@mail.ru     Gonchik          AL00001.92       mail.ru\n",
       "3           AL00013@yandex.ru  ibkjdf1330             AL00013     yandex.ru\n",
       "4       AL0001NG@e.ntu.edu.sg  myanmarpyi            AL0001NG  e.ntu.edu.sg"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_files.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e549102e",
   "metadata": {},
   "source": [
    "### Data manipulation via Dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "225a3233",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:22:02.125704Z",
     "start_time": "2022-12-02T17:22:02.109621Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EMail</th>\n",
       "      <th>Password</th>\n",
       "      <th>EMailID</th>\n",
       "      <th>EMailDomain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=146</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: getitem, 584 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                  EMail Password EMailID EMailDomain\n",
       "npartitions=146                                     \n",
       "                 object   object  object      object\n",
       "                    ...      ...     ...         ...\n",
       "...                 ...      ...     ...         ...\n",
       "                    ...      ...     ...         ...\n",
       "                    ...      ...     ...         ...\n",
       "Dask Name: getitem, 584 tasks"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering column by value\n",
    "aol = all_files[all_files.EMailDomain == 'aol.com']\n",
    "\n",
    "aol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1344324",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:23:09.973784Z",
     "start_time": "2022-12-02T17:22:07.276507Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1088899 entries, 0 to 108894\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count    Dtype \n",
      "---  ------       --------------    ----- \n",
      " 0   EMail        1088899 non-null  object\n",
      " 1   Password     1087951 non-null  object\n",
      " 2   EMailID      1088899 non-null  object\n",
      " 3   EMailDomain  1088899 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 41.5+ MB\n"
     ]
    }
   ],
   "source": [
    "aol_df = aol.compute()\n",
    "aol_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33ddfd81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:23:33.237752Z",
     "start_time": "2022-12-02T17:23:33.232624Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(aol_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8189650",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:23:36.388918Z",
     "start_time": "2022-12-02T17:23:36.379551Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>EMail</th>\n",
       "      <th>Password</th>\n",
       "      <th>EMailID</th>\n",
       "      <th>EMailDomain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL00000@aol.com</td>\n",
       "      <td>bazookaz9</td>\n",
       "      <td>AL00000</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AL002WRIT@aol.com</td>\n",
       "      <td>cjohn1</td>\n",
       "      <td>AL002WRIT</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AL007LTK@aol.com</td>\n",
       "      <td>winkle12</td>\n",
       "      <td>AL007LTK</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AL008@aol.com</td>\n",
       "      <td>oreos8</td>\n",
       "      <td>AL008</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>AL0201@aol.com</td>\n",
       "      <td>swimteam</td>\n",
       "      <td>AL0201</td>\n",
       "      <td>aol.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                EMail   Password    EMailID EMailDomain\n",
       "0     AL00000@aol.com  bazookaz9    AL00000     aol.com\n",
       "15  AL002WRIT@aol.com     cjohn1  AL002WRIT     aol.com\n",
       "25   AL007LTK@aol.com   winkle12   AL007LTK     aol.com\n",
       "26      AL008@aol.com     oreos8      AL008     aol.com\n",
       "51     AL0201@aol.com   swimteam     AL0201     aol.com"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aol_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b9bd293",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:23:39.875372Z",
     "start_time": "2022-12-02T17:23:39.872674Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [],
   "source": [
    "# Group by count\n",
    "# %%time\n",
    "\n",
    "# unique_domain_stats = all_files.groupby('EMailDomain')['EMailID'].nunique().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20546bd7",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85a105e",
   "metadata": {},
   "source": [
    "###  Password length summary dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "612f82c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T17:24:23.421467Z",
     "start_time": "2022-12-02T17:24:23.405808Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>PasswordLength</th>\n",
       "      <th>DistinctPasswords</th>\n",
       "      <th>DistinctEmailDomain</th>\n",
       "      <th>DistinctEmails</th>\n",
       "      <th>TotalCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>88573019</td>\n",
       "      <td>145292205</td>\n",
       "      <td>197625448</td>\n",
       "      <td>197625448</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>98472357</td>\n",
       "      <td>140926082</td>\n",
       "      <td>173206656</td>\n",
       "      <td>173206656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>225238280</td>\n",
       "      <td>293375531</td>\n",
       "      <td>331599690</td>\n",
       "      <td>331599690</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>101889914</td>\n",
       "      <td>135110968</td>\n",
       "      <td>155991336</td>\n",
       "      <td>155991336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>122724252</td>\n",
       "      <td>149603233</td>\n",
       "      <td>164837299</td>\n",
       "      <td>164837299</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>363</th>\n",
       "      <td>8199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>15273</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>15288</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>15291</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>15305</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PasswordLength  DistinctPasswords  DistinctEmailDomain  DistinctEmails  \\\n",
       "0                 6           88573019            145292205       197625448   \n",
       "1                 7           98472357            140926082       173206656   \n",
       "2                 8          225238280            293375531       331599690   \n",
       "3                 9          101889914            135110968       155991336   \n",
       "4                10          122724252            149603233       164837299   \n",
       "..              ...                ...                  ...             ...   \n",
       "363            8199                  1                    1               1   \n",
       "364           15273                  1                    1               1   \n",
       "365           15288                  2                    2               2   \n",
       "366           15291                  1                    1               1   \n",
       "367           15305                  2                    2               2   \n",
       "\n",
       "     TotalCount  \n",
       "0     197625448  \n",
       "1     173206656  \n",
       "2     331599690  \n",
       "3     155991336  \n",
       "4     164837299  \n",
       "..          ...  \n",
       "363           1  \n",
       "364           1  \n",
       "365           2  \n",
       "366           1  \n",
       "367           2  \n",
       "\n",
       "[368 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd_length = pd.read_csv('/home/jovyan/work/Jupyterthon/Summary-PasswordLength.txt', sep=\"\\t\")\n",
    "\n",
    "pwd_length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f80077",
   "metadata": {},
   "source": [
    "###  Domain summary statistics dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48b1af6f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:06:20.417402Z",
     "start_time": "2022-12-02T18:06:11.866200Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>EMailDomain</th>\n",
       "      <th>PasswordLength</th>\n",
       "      <th>DistinctEmails</th>\n",
       "      <th>DistinctPasswords</th>\n",
       "      <th>TotalCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-----------------------mail.ru</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>---------------------mail.ru</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>--------------------mail.ru</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-------------------yahoo.com</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>--------------mail.ru</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      EMailDomain  PasswordLength  DistinctEmails  \\\n",
       "0  -----------------------mail.ru               8               1   \n",
       "1    ---------------------mail.ru               8               2   \n",
       "2     --------------------mail.ru               8               1   \n",
       "3    -------------------yahoo.com               8               1   \n",
       "4           --------------mail.ru               8               3   \n",
       "\n",
       "   DistinctPasswords  TotalCount  \n",
       "0                  1           1  \n",
       "1                  2           2  \n",
       "2                  1           1  \n",
       "3                  1           1  \n",
       "4                  3           3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domain_summary_parquet = pd.read_parquet('/home/jovyan/work/Jupyterthon/summary_by_domain.parquet.gzip')\n",
    "domain_summary_parquet.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f757182",
   "metadata": {},
   "source": [
    "## Data Enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1af195",
   "metadata": {},
   "source": [
    "### Fortune 500 dataset enrichment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b07d19a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:06:21.248079Z",
     "start_time": "2022-12-02T18:06:21.216692Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>Title</th>\n",
       "      <th>Sector</th>\n",
       "      <th>EMailDomain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Walmart</td>\n",
       "      <td>Retailing</td>\n",
       "      <td>walmart.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berkshire Hathaway</td>\n",
       "      <td>Financials</td>\n",
       "      <td>berkshirehathaway.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Technology</td>\n",
       "      <td>apple.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>Energy</td>\n",
       "      <td>exxonmobil.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>McKesson</td>\n",
       "      <td>Wholesalers</td>\n",
       "      <td>mckesson.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Title       Sector            EMailDomain\n",
       "0             Walmart    Retailing            walmart.com\n",
       "1  Berkshire Hathaway   Financials  berkshirehathaway.com\n",
       "2               Apple   Technology              apple.com\n",
       "3         Exxon Mobil       Energy         exxonmobil.com\n",
       "4            McKesson  Wholesalers           mckesson.com"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_domain_from_url(string):\n",
    "    \"\"\" function to parse URL and extract domain\"\"\"\n",
    "    parsed_url = urlparse(string).netloc\n",
    "    domain = '.'.join(parsed_url.split('.')[-2:])\n",
    "    return domain\n",
    "\n",
    "fortune500 = pd.read_csv('/home/jovyan/work/Jupyterthon/Fortune_500_2017_from_data.world.csv')\n",
    "fortune500['EMailDomain'] = fortune500['Website'].apply(extract_domain_from_url)\n",
    "\n",
    "fortune500_domains = fortune500[['Title','Sector','EMailDomain']]\n",
    "fortune500_domains.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7b868f",
   "metadata": {},
   "source": [
    "### Merging with Domain summary dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "349cb179",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:06:53.182928Z",
     "start_time": "2022-12-02T18:06:21.965972Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>EMailDomain</th>\n",
       "      <th>PasswordLength</th>\n",
       "      <th>DistinctEmails</th>\n",
       "      <th>DistinctPasswords</th>\n",
       "      <th>TotalCount</th>\n",
       "      <th>Title</th>\n",
       "      <th>Sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aa.com</td>\n",
       "      <td>6</td>\n",
       "      <td>1545</td>\n",
       "      <td>1735</td>\n",
       "      <td>1916</td>\n",
       "      <td>American Airlines Group</td>\n",
       "      <td>Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abbott.com</td>\n",
       "      <td>6</td>\n",
       "      <td>1985</td>\n",
       "      <td>1979</td>\n",
       "      <td>1999</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abbott.com</td>\n",
       "      <td>6</td>\n",
       "      <td>1985</td>\n",
       "      <td>1979</td>\n",
       "      <td>1999</td>\n",
       "      <td>St. Jude Medical</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abbvie.com</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abc.xyz</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alphabet</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EMailDomain  PasswordLength  DistinctEmails  DistinctPasswords  TotalCount  \\\n",
       "0      aa.com               6            1545               1735        1916   \n",
       "1  abbott.com               6            1985               1979        1999   \n",
       "2  abbott.com               6            1985               1979        1999   \n",
       "3  abbvie.com               6              13                 14          14   \n",
       "4     abc.xyz               6               1                  1           1   \n",
       "\n",
       "                     Title          Sector  \n",
       "0  American Airlines Group  Transportation  \n",
       "1      Abbott Laboratories     Health Care  \n",
       "2         St. Jude Medical     Health Care  \n",
       "3                   AbbVie     Health Care  \n",
       "4                 Alphabet      Technology  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domain_by_sector = domain_summary_parquet.join(fortune500_domains.set_index('EMailDomain'), on='EMailDomain')\n",
    "domain_by_sector = domain_by_sector.dropna().reset_index()\n",
    "\n",
    "domain_by_sector.drop('index', axis=1, inplace=True)\n",
    "\n",
    "domain_by_sector.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "09ac096c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:06:53.882639Z",
     "start_time": "2022-12-02T18:06:53.867527Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 432 entries, 0 to 431\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   EMailDomain        432 non-null    object\n",
      " 1   PasswordLength     432 non-null    int64 \n",
      " 2   DistinctEmails     432 non-null    int64 \n",
      " 3   DistinctPasswords  432 non-null    int64 \n",
      " 4   TotalCount         432 non-null    int64 \n",
      " 5   Title              432 non-null    object\n",
      " 6   Sector             432 non-null    object\n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 23.8+ KB\n"
     ]
    }
   ],
   "source": [
    "domain_by_sector.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d69679",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fa58a3",
   "metadata": {},
   "source": [
    "### Histogram for Password Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1d06e67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T15:33:07.802979Z",
     "start_time": "2022-12-02T15:33:07.788506Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "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>PasswordLength</th>\n",
       "      <th>DistinctPasswords</th>\n",
       "      <th>DistinctEmailDomain</th>\n",
       "      <th>DistinctEmails</th>\n",
       "      <th>TotalCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>88573019</td>\n",
       "      <td>145292205</td>\n",
       "      <td>197625448</td>\n",
       "      <td>197625448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>98472357</td>\n",
       "      <td>140926082</td>\n",
       "      <td>173206656</td>\n",
       "      <td>173206656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>225238280</td>\n",
       "      <td>293375531</td>\n",
       "      <td>331599690</td>\n",
       "      <td>331599690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>101889914</td>\n",
       "      <td>135110968</td>\n",
       "      <td>155991336</td>\n",
       "      <td>155991336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>122724252</td>\n",
       "      <td>149603233</td>\n",
       "      <td>164837299</td>\n",
       "      <td>164837299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>30137025</td>\n",
       "      <td>39982292</td>\n",
       "      <td>42419776</td>\n",
       "      <td>42419776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>21360053</td>\n",
       "      <td>28423887</td>\n",
       "      <td>30109078</td>\n",
       "      <td>30109078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>11706077</td>\n",
       "      <td>15397072</td>\n",
       "      <td>16063998</td>\n",
       "      <td>16063998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>8375177</td>\n",
       "      <td>11104867</td>\n",
       "      <td>11590256</td>\n",
       "      <td>11590256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>12638936</td>\n",
       "      <td>14533050</td>\n",
       "      <td>14851668</td>\n",
       "      <td>14851668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>5137608</td>\n",
       "      <td>6531340</td>\n",
       "      <td>6771217</td>\n",
       "      <td>6771217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>1582420</td>\n",
       "      <td>2199646</td>\n",
       "      <td>2282325</td>\n",
       "      <td>2282325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>1429982</td>\n",
       "      <td>1982278</td>\n",
       "      <td>2120406</td>\n",
       "      <td>2120406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19</td>\n",
       "      <td>878806</td>\n",
       "      <td>1201651</td>\n",
       "      <td>1247370</td>\n",
       "      <td>1247370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>1063841</td>\n",
       "      <td>1406483</td>\n",
       "      <td>1473481</td>\n",
       "      <td>1473481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21</td>\n",
       "      <td>513143</td>\n",
       "      <td>679831</td>\n",
       "      <td>738393</td>\n",
       "      <td>738393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22</td>\n",
       "      <td>496100</td>\n",
       "      <td>633062</td>\n",
       "      <td>656718</td>\n",
       "      <td>656718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23</td>\n",
       "      <td>340259</td>\n",
       "      <td>439558</td>\n",
       "      <td>447269</td>\n",
       "      <td>447269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24</td>\n",
       "      <td>380465</td>\n",
       "      <td>471509</td>\n",
       "      <td>503721</td>\n",
       "      <td>503721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25</td>\n",
       "      <td>256995</td>\n",
       "      <td>339699</td>\n",
       "      <td>345770</td>\n",
       "      <td>345770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26</td>\n",
       "      <td>267832</td>\n",
       "      <td>305427</td>\n",
       "      <td>328856</td>\n",
       "      <td>328856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27</td>\n",
       "      <td>174004</td>\n",
       "      <td>201964</td>\n",
       "      <td>220382</td>\n",
       "      <td>220382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28</td>\n",
       "      <td>185307</td>\n",
       "      <td>212016</td>\n",
       "      <td>218868</td>\n",
       "      <td>218868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>29</td>\n",
       "      <td>153418</td>\n",
       "      <td>179965</td>\n",
       "      <td>183515</td>\n",
       "      <td>183515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30</td>\n",
       "      <td>164180</td>\n",
       "      <td>192876</td>\n",
       "      <td>224087</td>\n",
       "      <td>224087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31</td>\n",
       "      <td>114549</td>\n",
       "      <td>130568</td>\n",
       "      <td>131473</td>\n",
       "      <td>131473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32</td>\n",
       "      <td>1580572</td>\n",
       "      <td>1979928</td>\n",
       "      <td>2062520</td>\n",
       "      <td>2062520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33</td>\n",
       "      <td>69615</td>\n",
       "      <td>81134</td>\n",
       "      <td>86775</td>\n",
       "      <td>86775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>34</td>\n",
       "      <td>131524</td>\n",
       "      <td>140544</td>\n",
       "      <td>140931</td>\n",
       "      <td>140931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>35</td>\n",
       "      <td>58981</td>\n",
       "      <td>66021</td>\n",
       "      <td>66531</td>\n",
       "      <td>66531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>36</td>\n",
       "      <td>54649</td>\n",
       "      <td>60872</td>\n",
       "      <td>64716</td>\n",
       "      <td>64716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>37</td>\n",
       "      <td>36626</td>\n",
       "      <td>40722</td>\n",
       "      <td>40987</td>\n",
       "      <td>40987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>38</td>\n",
       "      <td>34530</td>\n",
       "      <td>37482</td>\n",
       "      <td>38286</td>\n",
       "      <td>38286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39</td>\n",
       "      <td>25931</td>\n",
       "      <td>29076</td>\n",
       "      <td>33600</td>\n",
       "      <td>33600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PasswordLength  DistinctPasswords  DistinctEmailDomain  DistinctEmails  \\\n",
       "0                6           88573019            145292205       197625448   \n",
       "1                7           98472357            140926082       173206656   \n",
       "2                8          225238280            293375531       331599690   \n",
       "3                9          101889914            135110968       155991336   \n",
       "4               10          122724252            149603233       164837299   \n",
       "5               11           30137025             39982292        42419776   \n",
       "6               12           21360053             28423887        30109078   \n",
       "7               13           11706077             15397072        16063998   \n",
       "8               14            8375177             11104867        11590256   \n",
       "9               15           12638936             14533050        14851668   \n",
       "10              16            5137608              6531340         6771217   \n",
       "11              17            1582420              2199646         2282325   \n",
       "12              18            1429982              1982278         2120406   \n",
       "13              19             878806              1201651         1247370   \n",
       "14              20            1063841              1406483         1473481   \n",
       "15              21             513143               679831          738393   \n",
       "16              22             496100               633062          656718   \n",
       "17              23             340259               439558          447269   \n",
       "18              24             380465               471509          503721   \n",
       "19              25             256995               339699          345770   \n",
       "20              26             267832               305427          328856   \n",
       "21              27             174004               201964          220382   \n",
       "22              28             185307               212016          218868   \n",
       "23              29             153418               179965          183515   \n",
       "24              30             164180               192876          224087   \n",
       "25              31             114549               130568          131473   \n",
       "26              32            1580572              1979928         2062520   \n",
       "27              33              69615                81134           86775   \n",
       "28              34             131524               140544          140931   \n",
       "29              35              58981                66021           66531   \n",
       "30              36              54649                60872           64716   \n",
       "31              37              36626                40722           40987   \n",
       "32              38              34530                37482           38286   \n",
       "33              39              25931                29076           33600   \n",
       "\n",
       "    TotalCount  \n",
       "0    197625448  \n",
       "1    173206656  \n",
       "2    331599690  \n",
       "3    155991336  \n",
       "4    164837299  \n",
       "5     42419776  \n",
       "6     30109078  \n",
       "7     16063998  \n",
       "8     11590256  \n",
       "9     14851668  \n",
       "10     6771217  \n",
       "11     2282325  \n",
       "12     2120406  \n",
       "13     1247370  \n",
       "14     1473481  \n",
       "15      738393  \n",
       "16      656718  \n",
       "17      447269  \n",
       "18      503721  \n",
       "19      345770  \n",
       "20      328856  \n",
       "21      220382  \n",
       "22      218868  \n",
       "23      183515  \n",
       "24      224087  \n",
       "25      131473  \n",
       "26     2062520  \n",
       "27       86775  \n",
       "28      140931  \n",
       "29       66531  \n",
       "30       64716  \n",
       "31       40987  \n",
       "32       38286  \n",
       "33       33600  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter dataset to better visualize with the right bins and length\n",
    "pwdlength_under40 = pwd_length[pwd_length['PasswordLength'] < 40]\n",
    "\n",
    "pwdlength_under40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a75b9d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T16:16:16.107067Z",
     "start_time": "2022-12-02T16:16:15.130828Z"
    },
    "scrolled": true,
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "plt.figure(figsize=(14, 10))\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "# plot a bar chart\n",
    "chart = sns.barplot(\n",
    "    x=\"PasswordLength\", \n",
    "    y=\"DistinctPasswords\", \n",
    "    data=pwdlength_under40, \n",
    "    ci=None,\n",
    "    color='#69b3a2');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00818ee6",
   "metadata": {},
   "source": [
    "### Bar chart of No of companies per Sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c0530792",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:08:59.739630Z",
     "start_time": "2022-12-02T18:08:59.423962Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sector = domain_by_sector['Sector'].value_counts().to_frame(name='count').reset_index()\n",
    "df_sector = df_sector.rename(index=str, columns={\"index\": \"Sector\", \"count\": \"Count\"}).sort_values(by='Count',ascending=True)\n",
    "\n",
    "df_sector.plot.barh(x='Sector',y='Count',figsize=(16,12))\n",
    "plt.title('No of companies per sector');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbf22694",
   "metadata": {},
   "source": [
    "### Password Length trends in Fortune 500 companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "422250cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T18:26:10.386766Z",
     "start_time": "2022-12-02T18:26:10.205452Z"
    },
    "vscode": {
     "languageId": "python"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sector_pwdlength = domain_by_sector['PasswordLength'].value_counts().to_frame(name='count').reset_index()\n",
    "df_sector_pwdlength = df_sector_pwdlength.rename(index=str, columns={\"index\": \"PasswordLength\", \"count\": \"Count\"}).sort_values(by='Count',ascending=True)\n",
    "\n",
    "df_sector_pwdlength.plot.bar(x='PasswordLength',y='Count',figsize=(16,12))\n",
    "plt.title('Password Length distribution in Fortune 500');"
   ]
  },
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   "source": [
    "## Conclusion\n",
    "In this presentation, we take a look at real world database dump and how to load it in Jupyter notebook and derive interesting insights. The dataset had lot of quality issues and requires many preparation and clean-up steps. The database also just had email domain and password but we are able to enrich that dataset to categorize password patterns per sectors. We were able to navigate the various challenges during this process and learned generic workflow than can be repeated on new datasets. Often analysts will have data already ingested in powerful analytical tools, but in some rare occurences when you encounter large set of data that need to be quickly analyzed , these methodologies can be useful. \n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "- https://medium.com/4iqdelvedeep/1-4-billion-clear-text-credentials-discovered-in-a-single-database-3131d0a1ae14\n",
    "- https://www.reddit.com/r/pwned/comments/7hhqfo/combination_of_many_breaches/\n",
    "- https://data.world/aurielle/fortune-500-2017\n",
    "- https://github.com/ashwin-patil/springboard-intro-to-datascience/tree/master/capstone_project\n",
    "- https://github.com/ashwin-patil/springboard-intro-to-datascience/blob/master/capstone_project/Analysis%20of%20Passwords%20from%20BreachCompilation%20Dataset-ver2.pdf"
   ]
  }
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