project3/URL_Scraping.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "import requests\n",
    "from time import sleep\n",
    "from bs4 import BeautifulSoup\n",
    "import csv\n",
    "from urllib.parse import urlparse\n",
    "from random import shuffle, sample"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 14,
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   "source": [
    "bing_df = pd.read_csv('data/bing_search_results.csv')\n",
    "domains = set()\n",
    "with open('data/scraped_urls.txt', 'r') as file:\n",
    "    urls = set([url.strip() for url in file])\n",
    "len(urls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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     "end_time": "2018-05-08T02:41:29.371718Z",
     "start_time": "2018-05-08T02:41:29.368896Z"
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   },
   "outputs": [],
   "source": [
    "def random_search_result():\n",
    "    return bing_df.sample(1).iloc[0].url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth: 1, urls: 9971, search: 99, requests: 130, iters: 118 \r"
     ]
    }
   ],
   "source": [
    "url = None\n",
    "depth = 0\n",
    "search_count = 0\n",
    "request_count = 0\n",
    "i = 0\n",
    "while len(urls) < 1e4:\n",
    "    print('depth: {}, urls: {}, search: {}, requests: {}, iters: {} '.format(depth, len(urls), search_count, request_count, i), end='\\r')\n",
    "    sleep(0.05)\n",
    "    if not url:\n",
    "        url = random_search_result()\n",
    "        search_count += 1\n",
    "        while urlparse(url).netloc in domains:\n",
    "            search_count += 1\n",
    "            url = random_search_result()\n",
    "        depth = 0\n",
    "    try:\n",
    "        request_count += 1\n",
    "        response = requests.get(url, timeout=2)\n",
    "    except:\n",
    "        url = None\n",
    "        continue\n",
    "    soup = BeautifulSoup(response.text,\"lxml\")\n",
    "    a_tags = soup.find_all('a')\n",
    "    shuffle(a_tags)\n",
    "    url = None\n",
    "    for a_tag in a_tags:\n",
    "        href = a_tag.get('href')\n",
    "        if href and href[0:4] == 'http':\n",
    "            domain = urlparse(href).netloc\n",
    "            if not domain in domains:\n",
    "                domains.add(domain)\n",
    "                urls.add(href)\n",
    "                url = href\n",
    "    depth += 1\n",
    "    i += 1\n",
    "    if depth > 100: url = None\n",
    "    with open('data/scraped_urls.txt', 'w') as file:\n",
    "        file.write(\"\\n\".join(urls))\n",
    "    "
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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   },
   "outputs": [],
   "source": [
    "with open('data/scraped_urls.txt', 'w') as file:\n",
    "    file.write(\"\\n\".join(urls))"
   ]
  },
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   "cell_type": "code",
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    "len(urls)"
   ]
  },
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