xHEADmaster

1 files changed, 325 insertions, 0 deletions

diff --git a/data_retrieval_web.ipynb b/data_retrieval_web.ipynb
new file mode 100644
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+++ b/data_retrieval_web.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sortedcontainers import SortedDict\n",
+    "from urllib.request import urlopen\n",
+    "from bs4 import BeautifulSoup\n",
+    "from pprint import pprint\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "\n",
+    "import json\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "d = SortedDict()\n",
+    "\n",
+    "with open('data/root.zone') as f:\n",
+    "    for i in range(21):\n",
+    "        next(f)\n",
+    "        \n",
+    "    for line in f:\n",
+    "        values = line.split('\\t')\n",
+    "        if 'NS' in values:\n",
+    "            tld = values[0][:-1]\n",
+    "            ns = values[-1][:-2]\n",
+    "            if tld in d:\n",
+    "                d[tld].append(ns)\n",
+    "            else:\n",
+    "                d[tld] = [ns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "f = open('test.csv', 'w')\n",
+    "f.write('tld,ns\\n')\n",
+    "for i in d:\n",
+    "    for j in d[i]:\n",
+    "        f.write(i + ',' + j + '\\n')\n",
+    "        \n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"test.csv\")\n",
+    "df2 = df.groupby('tld')['ns'].nunique()\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "url = \"https://www.iana.org/domains/root/db/\"\n",
+    "html = urlopen(url)\n",
+    "soup = BeautifulSoup(html, 'html5lib')\n",
+    "\n",
+    "for item in soup.find_all(attrs={'class': 'iana-table'}):\n",
+    "    for table in soup.find_all(attrs={'class': 'iana-table'}):\n",
+    "        values = [td.get_text(strip=True) for td in table.find_all('td')]\n",
+    "\n",
+    "        for i in range(0, len(values), 3):\n",
+    "            data.append({'tld': values[i].strip('.'), 'type': values[i + 1], 'organisation': values[i + 2]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "with open('data/tld_type', 'w') as f:\n",
+    "    f.write(json.dumps(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for tld in data:\n",
+    "    data[tld]['nameservers'] = SortedDict()\n",
+    "    html = urlopen(url + tld)\n",
+    "    soup = BeautifulSoup(html, 'html5lib')\n",
+    "\n",
+    "    for br in soup.find_all('br'):\n",
+    "        br.replace_with('\\t')\n",
+    "\n",
+    "    for item in soup.find_all(attrs={'class': 'iana-table'}):\n",
+    "        for table in soup.find_all(attrs={'class': 'iana-table'}):\n",
+    "            values = [td.get_text(strip=False) for td in table.find_all('td')]\n",
+    "            \n",
+    "            print(values[1].split('\\t')[:-1])\n",
+    "\n",
+    "            for i in range(0, len(values), 2):\n",
+    "                ips = values[i + 1].split('\\t')\n",
+    "                \n",
+    "                data[tld]['nameservers'][values[i]] = SortedDict()\n",
+    "                \n",
+    "                if '.' in ips[0]:\n",
+    "                    data[tld]['nameservers'][values[i]]['ipv4'] = ips[0]\n",
+    "                elif '.' in ips[1]:\n",
+    "                    data[tld]['nameservers'][values[i]]['ipv4'] = ips[1]\n",
+    "                    \n",
+    "                if 'nameservers' in data[tld]:\n",
+    "                    data[tld]['nameservers'].append((values[i], values[i + 1].split('\\t')))\n",
+    "                else:\n",
+    "                    data[tld]['nameservers'] = [(values[i], values[i + 1].split('\\t'))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>organisation</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Amazon Registry Services, Inc.</th>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Charleston Road Registry Inc.</th>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Uniregistry, Corp.</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United TLD Holdco Ltd.</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Top Level Domain Holdings Limited</th>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Afilias plc</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Not assigned</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Internet Assigned Numbers Authority</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lifestyle Domain Holdings, Inc.</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dish DBS Corporation</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     count\n",
+       "organisation                              \n",
+       "Amazon Registry Services, Inc.          51\n",
+       "Charleston Road Registry Inc.           43\n",
+       "Uniregistry, Corp.                      22\n",
+       "United TLD Holdco Ltd.                  22\n",
+       "Top Level Domain Holdings Limited       18\n",
+       "Afilias plc                             14\n",
+       "Not assigned                            14\n",
+       "Internet Assigned Numbers Authority     12\n",
+       "Lifestyle Domain Holdings, Inc.         11\n",
+       "Dish DBS Corporation                    11"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('organisation')['tld'].agg(['count']).sort_values('count', ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f58a26e2be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = counts.head(15).plot.barh(figsize=(25,15))\n",
+    "fig = plot.get_figure()\n",
+    "fig.savefig(\"imgs/tld_orgs.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df.to_csv('temp.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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
+}