'English word'ed domain resolver' Notebook for Jupyter

README.md

'English word'ed domain resolver' Notebook for Jupyter

The goal of this notebook is to easily find an available domain based on the English dictionary.

This Notebook can be launched directly from mybinder.org

domain-resolver.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "pygments_lexer": "ipython3",
      "version": "3.7.8"
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  "nbformat_minor": 5,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "# English word'ed domain resolver\n\n*The goal of this notebook is to easily find an available domain based on the English dictionary.*\n\n---\n\n## Prerequisite (prepare environment)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from __future__ import print_function\nimport sys",
      "metadata": {
        "trusted": true
      },
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "from ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nimport pandas\nimport whois",
      "metadata": {
        "trusted": true
      },
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Setup dictionnary and domain list",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "english_dict_url = 'https://github.com/dwyl/english-words/raw/master/words_alpha.txt'\n#domain_list_url = 'https://pkgstore.datahub.io/core/top-level-domain-names/top-level-domain-names.csv_csv/data/667f4464088f3ca10522e0e2e39c8ae4/top-level-domain-names.csv_csv.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "en_dict = pandas.read_csv(english_dict_url,names=[\"Word\"]).dropna(subset=[\"Word\"])\n\ndomains = pandas.DataFrame({'Domain': ['.{}'.format(d.replace('_', '.').rstrip('.')) for d in whois.TLD_RE.keys()]})\n#domains = pandas.read_csv(domain_list_url).drop(labels=[\"Type\",\"Sponsoring Organisation\"], axis=1)\n#domains = domains[domains.apply(lambda d: all(ord(c) < 128 for c in d.Domain), axis=1)]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "---\n## Run UI",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def selector(x):\n    words = en_dict[en_dict.Word.str.contains(x)]\n    if words.size > 10000:\n        return 'Too much words found ({})'.format(words.size)\n    results = {word: [(word[::-1].replace(d.replace('.','')[::-1],d[::-1], 1))[::-1] for d in domains.Domain if word.endswith(d.replace('.',''))] for word in words.Word}\n    results = {k: results[k] for k in results.keys() if results[k]}\n    return results\n\ninteract(selector, x=widgets.Text(description=' '));",
      "metadata": {
        "trusted": true
      },
      "execution_count": 0,
      "outputs": []
    }
  ]
}

requirements.txt

pandas
whois