Scraping for profs.ipynb

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   "source": [
    "## Universities:"
   ]
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    "hk_universities = []"
   ]
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   "source": [
    "## Faculties:"
   ]
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    "\"\"\" Differenet school have different faculties \n",
    "\n",
    "Input: hk_universities[i]\n",
    "Output: A dict [hk_universities[i], [Faculties, ...], [link of faculties, ...]}\n",
    "Example [HKU, [Science Faculty, Bussiness Faculty,...], [\"https://www.scifac.hku.hk/people\", \"https://www.hkubs.hku.hk/research/faculty-members/\", ...] }\n",
    "\n",
    "Insight1: webscraping get to browser, search [school + faculties + \"profs\"], get first link\n",
    "Insight2: for now we dont have too many uni, can first use mannual to get some first\n",
    "\"\"\"\n",
    "\n",
    "def get_faculties(uni):\n",
    "    \n",
    "    return faculties_dict"
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   "source": [
    "## Profs"
   ]
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   "source": [
    "\"\"\" Differenet faculties have different profs\n",
    "\n",
    "Input: faculties, faculties link\n",
    "Output: [faculties, [profs]]\n",
    "Example [\"FBE\", [\"A\",\"B\",\"C\"]]\n",
    "\n",
    "Insight1: Use lib(request) to scrape\n",
    "\"\"\"\n",
    "\n",
    "def get_profs(faculty, faculty_link):\n",
    "    return profs"
   ]
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   },
   "source": [
    "## Data Aggreation"
   ]
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   "outputs": [
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     "data": {
      "text/plain": [
       "',Uni,Faculty,Profs\\n'"
      ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame(columns=[\"Uni\",\"Faculty\",\"Profs\"])\n",
    "\n",
    "\"\"\"Operation\"\"\"\n",
    "df.to_csv()"
   ]
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   "source": [
    "## "
   ]
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   "source": [
    "## "
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   "source": [
    "## "
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