ycombinator_scraper.ipynb

{
 "cells": [
  {
   "attachments": {
    "Untitled.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "5421fb56",
   "metadata": {
    "id": "5421fb56"
   },
   "source": [
    "# March, 2022 - Scraping Companies Information for listed  companies  on Ycombinator \n",
    "\n",
    "\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "# Project Outline\n",
    "1. To start with, I'm going to scrape about 1000 entries from https://ycombinator.com/companies, which are:\n",
    "- The listed company names\n",
    "- The company's ycombinator page url \n",
    "- The company location (will prefer to get it here since it is written with the country, unlike how it appeared when the company name has been clicked\n",
    "- The company description head/slogan. Then,\n",
    "\n",
    "\n",
    "![Untitled.png](attachment:Untitled.png)\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "2. I'll go through the scraped company's ycombinator page url  and grab many other informations (company's description, year founded, team size, company page url, social media urls, management details) as they appear on the page.\n",
    "\n",
    "3. At the end, I will create for each company, a CSV file in the following format:\n",
    "\n",
    "```\n",
    "'Company_Name'| 'Company_Page_URL'| 'Company_Location'| 'Description_Head'| 'Website'| 'Description'| 'Founded'| 'Team_Size'| 'Linkedin_Profile'| 'Twitter_Profile'| 'Facebook_Profile'| 'Crunchbase_Profile'| 'Active_Founder1'| 'Active_Founder2'| 'Active_Founder3'\n",
    "Airbnb|\thttps://www.ycombinator.com/companies/airbnb|\tSan Francisco, CA, US,|\tBook accommodations around the world.|  http://airbnb.com | Founded in August of 2008 and based in San Fra... | 2008 | 5000 | https://www.linkedin.com/company/airbnb/ | https://twitter.com/Airbnb | https://www.facebook.com/airbnb/ | https://www.crunchbase.com/organization/airbnb | Nathan Blecharczyk\\nNone\\nhttps://twitter.com/... | Brian Chesky\\nNone\\nhttps://twitter.com/bchesky\\n | Joe Gebbia\\nNone\\nhttps://twitter.com/jgebbia\\n,```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196d4d0b",
   "metadata": {
    "id": "196d4d0b"
   },
   "source": [
    "## Import necessary libraries\n",
    "\n",
    "- use **selenium** to downlaod the page\n",
    "- use **BS4** to parse and extract information\n",
    "- convert to a Pandas dataframe\n",
    "\n",
    "lets import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25dc5d55",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "25dc5d55",
    "outputId": "1ca0ffd6-d03e-4566-c1e3-58a64d2b1c21"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    }
   ],
   "source": [
    "# import necessary modules\n",
    "import time\n",
    "from datetime import datetime as dt\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "\n",
    "import requests\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2454d443",
   "metadata": {
    "id": "2454d443"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14e46330",
   "metadata": {
    "id": "14e46330"
   },
   "outputs": [],
   "source": [
    "def chrome(mode='h'):\n",
    "\n",
    "    ''' A function to instantiate chrome driver\n",
    "\n",
    "        :arguments: \n",
    "            mode - representing either headless (preferred) or browser mode.\n",
    "        :returns: \n",
    "            driver - the driver object instantiated.\n",
    "    '''\n",
    "\n",
    "    if mode == 'h':\n",
    "        #  Headless mode\n",
    "        chrome_option = Options()\n",
    "        chrome_option.add_argument(\"--headless\")\n",
    "        chrome_option.add_argument(\"--log-level=3\")     # disabling unwanted messages printed while running with am headless browser\n",
    "        driver = webdriver.Chrome(options=chrome_option)\n",
    "\n",
    "    elif mode == 'b':\n",
    "        # Browser mode\n",
    "        driver = webdriver.Chrome()\n",
    "\n",
    "    else:\n",
    "        print(\"Mode is invalid\")\n",
    "        return None\n",
    "\n",
    "    return driver\n",
    "\n",
    "\n",
    "\n",
    "def get_ycombinator_page_source(page_url=None, browser=None):\n",
    "    \"\"\"\n",
    "    A function to get the page source codes and contents\n",
    "\n",
    "        :arguments: \n",
    "            page_url - the url for of the page\n",
    "            browser - the webdriver object\n",
    "        :returns: \n",
    "            page_dom - a beautiful soup object of the page contents.\n",
    "    \"\"\"\n",
    "    \n",
    "    time.sleep(5)\n",
    "    browser.get(page_url)\n",
    "    time.sleep(20) # allow the page to load \n",
    "    \n",
    "    \n",
    "    ################ implement infinite scrolling ######################################\n",
    "    try:\n",
    "        \n",
    "        previous_height = browser.execute_script('return document.body.scrollHeight')\n",
    "\n",
    "        i = 0\n",
    "        while True:\n",
    "            i+=1\n",
    "            print(\"scroll: \", i)\n",
    "\n",
    "            browser.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n",
    "\n",
    "            time.sleep(1)\n",
    "\n",
    "            new_height = browser.execute_script('return document.body.scrollHeight')\n",
    "\n",
    "            if previous_height == new_height:\n",
    "                print(\"End of page reached\")\n",
    "                break\n",
    "\n",
    "            previous_height = new_height\n",
    "    \n",
    "    except:\n",
    "        browser.close()\n",
    "    ################################################################################\n",
    "    \n",
    "    time.sleep(2)\n",
    "\n",
    "    page_dom = BeautifulSoup(browser.page_source,\"html.parser\")\n",
    "    \n",
    "    return page_dom\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def get_company_page_info(doc):\n",
    "    \"\"\"\n",
    "    A function to get the names of startup companies, location and url of company pages on ycombinator\n",
    "\n",
    "        :arguments: \n",
    "            doc - the bs4 object of the ycombinator page\n",
    "        :returns: \n",
    "            DataFrame object of the data collected\n",
    "    \"\"\"\n",
    "    page_dict = { 'Company_Name': [], 'Company_Page_URL': [], 'Company_Location': [], 'Description_Head': []}\n",
    "\n",
    "    # find all a tags with class name \"styles-module__company___1UVnl no-hovercard\" which points to individual company segment\n",
    "    item = doc.find_all(\"a\",{\"class\":\"styles-module__company___1UVnl no-hovercard\"})\n",
    "    \n",
    "    \n",
    "    # company ycompany's page url\n",
    "    url_list = []\n",
    "    \n",
    "    # get page informations\n",
    "    for i in range(len(item)):\n",
    "        # company name\n",
    "        company_name = item[i].find('span', {\"class\":\"styles-module__coName___3zz21\"})\n",
    "        page_dict[\"Company_Name\"].append(company_name.text)\n",
    "        \n",
    "        # company page url\n",
    "        company_page_url = item[i]['href']\n",
    "        page_dict[\"Company_Page_URL\"].append(\"https://www.ycombinator.com\"+company_page_url)\n",
    "        \n",
    "        # company ycompany's page url\n",
    "        url_list.append(\"https://www.ycombinator.com\"+company_page_url)\n",
    "\n",
    "        # company location\n",
    "        company_location = item[i].find('span', {\"class\":\"styles-module__coLocation___yhKam\"})\n",
    "        page_dict[\"Company_Location\"].append(company_location.text)\n",
    "        \n",
    "        # Description_Head\n",
    "        description_head = item[i].find('span', {\"class\":\"styles-module__coDescription___1b_yd\"})\n",
    "        page_dict[\"Description_Head\"].append(description_head.text)\n",
    "\n",
    "        \n",
    "    return pd.DataFrame(page_dict), url_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1b7033",
   "metadata": {
    "id": "9b1b7033"
   },
   "source": [
    "\n",
    "## Now, I will write functions to :\n",
    "\n",
    "\n",
    "1. Browse each Company_Page_URL \n",
    "2. Get actual company website address\n",
    "3. Get the company description as appeared on ycombinator\n",
    "4. Get the company year founded and team size,\n",
    "5. Get the company social media urls\n",
    "6. Get the company founder infos such as there name, position and social media urls\n",
    "7. Then after collecting the above info for all Company_Page_URL, create a CSV from concatenating the earlier scraped df with the dataframe of the newly collected infos\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "289dfd5f",
   "metadata": {
    "id": "289dfd5f"
   },
   "outputs": [],
   "source": [
    "def get_company_website(doc):\n",
    "    # scrape the websites\n",
    "    try:\n",
    "        company_websites_tags = doc.find(\"a\",{\"target\":\"_blank\"})\n",
    "        try:\n",
    "            company_websites = company_websites_tags.text\n",
    "        except:\n",
    "            company_websites = company_websites_tags\n",
    "    except:\n",
    "        company_websites = np.nan\n",
    "    return company_websites\n",
    "\n",
    "\n",
    "\n",
    "def get_company_description(doc):\n",
    "    # scrape the descriptions\n",
    "    try:\n",
    "        company_description_tags = doc.find(\"p\",{\"class\":\"whitespace-pre-line\"})\n",
    "        try:\n",
    "            company_description = company_description_tags.text\n",
    "        except:\n",
    "            company_description = company_description_tags\n",
    "    except:\n",
    "        company_description = np.nan\n",
    "    return company_description\n",
    "\n",
    "\n",
    "\n",
    "def get_company_year_founded_and_team_size(doc):\n",
    "\n",
    "    # scrape\n",
    "    try:\n",
    "        ppty = doc.find(\"div\",{\"class\":\"space-y-0.5\"}).find_all(\"div\",{\"class\":\"flex flex-row justify-between\"})\n",
    "        year_founded = ppty[0].text.split(\":\")[1]\n",
    "        team_size = ppty[1].text.split(\":\")[1]\n",
    "        \n",
    "        company_year = year_founded\n",
    "        company_size = team_size\n",
    "    except:\n",
    "        company_year = np.nan\n",
    "        company_size = np.nan\n",
    "\n",
    "    return company_year, company_size\n",
    "\n",
    "\n",
    "        \n",
    "def get_company_social_media_urls(doc):\n",
    "\n",
    "    # scrape the social network urls\n",
    "    try:\n",
    "        \n",
    "        sm_info = doc.find(\"div\",{\"class\":\"space-x-2\"})\n",
    "        \n",
    "        try:\n",
    "            linkedin_url = sm_info.find(\"a\",{\"title\":\"LinkedIn profile\"})[\"href\"]\n",
    "        except:\n",
    "            linkedin_url = np.NaN     \n",
    "        try:\n",
    "            twitter_url = sm_info.find(\"a\",{\"title\":\"Twitter account\"})[\"href\"]\n",
    "        except:\n",
    "            twitter_url = np.NaN    \n",
    "        try:\n",
    "            facebook_url = sm_info.find(\"a\",{\"title\":\"Facebook profile\"})[\"href\"]\n",
    "        except:\n",
    "            facebook_url = np.NaN\n",
    "        try:\n",
    "            crunchbase_url = sm_info.find(\"a\",{\"title\":\"Crunchbase profile\"})[\"href\"]\n",
    "        except:\n",
    "            crunchbase_url = np.NaN\n",
    "\n",
    "    except:\n",
    "        linkedin_url = np.NaN\n",
    "        twitter_url = np.NaN    \n",
    "        facebook_url = np.NaN\n",
    "        crunchbase_url = np.NaN \n",
    "\n",
    "    return linkedin_url, twitter_url, facebook_url, crunchbase_url\n",
    "\n",
    "        \n",
    "def get_founder_info(doc):\n",
    "    founder_dict = {}\n",
    "\n",
    "    try:\n",
    "        founder_info = doc.find_all(\"div\",{\"class\":\"leading-snug\"})\n",
    "    except:\n",
    "        founder_info = \"\"\n",
    "    \n",
    "\n",
    "    if len(founder_info)>0:\n",
    "        \n",
    "        for i in range(len(founder_info)):\n",
    "\n",
    "            founder_name = founder_info[i].find(\"div\",{\"class\":\"font-bold\"}).text\n",
    "\n",
    "\n",
    "            try:\n",
    "                founder_post = founder_info[i].find(\"div\",{\"class\":\"\"}).text\n",
    "            except:\n",
    "                founder_post = founder_info[i].find(\"div\",{\"class\":\"\"})\n",
    "\n",
    "            try:\n",
    "                founder_sm = founder_info[i].find(\"div\",{\"class\":\"mt-1 space-x-2\"}).find_all(\"a\")\n",
    "                founder_sm_links = \"\"\n",
    "                for j in founder_sm:\n",
    "                    founder_sm_links = founder_sm_links + str(j[\"href\"]) + \"\\n\"\n",
    "            except:\n",
    "                founder_sm_links = \"None\"\n",
    "\n",
    "            founder = founder_name + \"\\n\" + str(founder_post) + \"\\n\"  + founder_sm_links # + \"\\n\" + str(founder_descr) \n",
    "\n",
    "            founder_dict[\"Active_Founder\"+str(i+1)] = founder\n",
    "\n",
    "        return founder_dict\n",
    "    \n",
    "    \n",
    "    else:\n",
    "        return founder_dict\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "def scrape_all(url=None):\n",
    "    \n",
    "    response = requests.get(url)\n",
    "#     if response.status_code != 200:\n",
    "#         raise Exception('Failed to load page {}'.format(url))\n",
    "        \n",
    "    soup_other = BeautifulSoup(response.text,\"html.parser\")\n",
    "\n",
    "    c_web = get_company_website(soup_other)\n",
    "    c_description = get_company_description(soup_other)\n",
    "    company_year, company_size = get_company_year_founded_and_team_size(soup_other)\n",
    "    linkedin_url, twitter_url, facebook_url, crunchbase_url = get_company_social_media_urls(soup_other)\n",
    "    founder_details = get_founder_info(soup_other)\n",
    "\n",
    "\n",
    "    d = {'Website': c_web,\n",
    "         'Description': c_description,\n",
    "         'Founded': company_year,\n",
    "         'Team_Size': company_size,\n",
    "         'Linkedin_Profile': linkedin_url,\n",
    "         'Twitter_Profile': twitter_url,\n",
    "         'Facebook_Profile': facebook_url,\n",
    "         'Crunchbase_Profile': crunchbase_url,             \n",
    "         }     \n",
    "\n",
    "    d.update(founder_details)\n",
    "\n",
    "    \n",
    "\n",
    "    return d\n",
    "        \n",
    "\n",
    "        \n",
    "        \n",
    "def make_dataframe_and_save(df1 = None, l=None):\n",
    "    final_df = pd.concat([df,pd.DataFrame(l)], axis=1)\n",
    "    \n",
    "    final_df.to_csv(\"ycombinator_data.csv\", index = False)\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980019a6",
   "metadata": {
    "id": "980019a6"
   },
   "source": [
    "# Scrape the list of companies link, name, location and short description(head) from ycomobinator\n",
    "\n",
    "`chrome`: used to instantiate the webdriver as either a headless browser or not\n",
    "\n",
    "`get_ycombinator_page_source`: Used to handle the dynamic scraping of the project. It scrolls the website till it reaches the end of the page. Afterward, beautifulsoup is used to parse the page source which is used as input to:\n",
    "\n",
    "`get_company_page_info`: extracted the COmpany name, links, short description/description head and location of the companies. returns url_list and a dataframe for the already scraped information.\n",
    "\n",
    "\n",
    "**Note:** _They in total take `~2 minutes` to run_ and the lenght of url_list must be 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb64d36f",
   "metadata": {
    "id": "fb64d36f",
    "outputId": "7dbf35c0-b7b4-419b-e03a-ba0c661f693f",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scroll:  1\n",
      "scroll:  2\n",
      "scroll:  3\n",
      "scroll:  4\n",
      "scroll:  5\n",
      "scroll:  6\n",
      "scroll:  7\n",
      "scroll:  8\n",
      "scroll:  9\n",
      "scroll:  10\n",
      "scroll:  11\n",
      "scroll:  12\n",
      "scroll:  13\n",
      "scroll:  14\n",
      "scroll:  15\n",
      "scroll:  16\n",
      "scroll:  17\n",
      "scroll:  18\n",
      "scroll:  19\n",
      "scroll:  20\n",
      "scroll:  21\n",
      "scroll:  22\n",
      "scroll:  23\n",
      "scroll:  24\n",
      "scroll:  25\n",
      "scroll:  26\n",
      "scroll:  27\n",
      "scroll:  28\n",
      "scroll:  29\n",
      "scroll:  30\n",
      "scroll:  31\n",
      "scroll:  32\n",
      "scroll:  33\n",
      "scroll:  34\n",
      "scroll:  35\n",
      "scroll:  36\n",
      "scroll:  37\n",
      "scroll:  38\n",
      "scroll:  39\n",
      "scroll:  40\n",
      "scroll:  41\n",
      "scroll:  42\n",
      "scroll:  43\n",
      "scroll:  44\n",
      "scroll:  45\n",
      "scroll:  46\n",
      "scroll:  47\n",
      "scroll:  48\n",
      "scroll:  49\n",
      "End of page reached\n",
      "The time used is 117.5173134803772 seconds\n",
      "The length of url_list is 1000 \n"
     ]
    }
   ],
   "source": [
    "root_url = \" https://ycombinator.com/companies\"\n",
    "\n",
    "driver = chrome(\"h\")  # instantiate the webdriver\n",
    "\n",
    "time_start = time.time()\n",
    "\n",
    "doc = get_ycombinator_page_source(page_url=root_url, browser = driver)\n",
    "\n",
    "\n",
    "df, url_list = get_company_page_info(doc)\n",
    "\n",
    "time_used = time.time() - time_start\n",
    "print(\"The time used is %s seconds\"%(time_used))\n",
    "\n",
    "url_list_len = len(url_list)\n",
    "print(\"The length of url_list is %s \"%(url_list_len))\n",
    "# close browser\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9752d888",
   "metadata": {
    "id": "9752d888"
   },
   "source": [
    "# B1. Scraping Companies' information  without multi-threading\n",
    "Runs for ~ 4 minutes with good network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fb0b21b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9fb0b21b",
    "outputId": "a48c83ea-bb12-4391-a647-409506bc204d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time used is 238.06634640693665 seconds\n"
     ]
    }
   ],
   "source": [
    "l = []\n",
    "retries = []\n",
    "count = 1\n",
    "\n",
    "# start_no_thread = dt.now()\n",
    "time_start = time.time()\n",
    "\n",
    "run = True\n",
    "while run:\n",
    "    for link in url_list:\n",
    "        try:\n",
    "            print(f\"{count}/{url_list_len}\", end='\\r')\n",
    "            d = scrape_all(url=link)\n",
    "            l.append(d)\n",
    "\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            retries.append(link)\n",
    "    \n",
    "    if retries != []:\n",
    "        url_list = retries\n",
    "        retries = []\n",
    "    else:\n",
    "        run = False\n",
    "        \n",
    "\n",
    "# runtime_no_thread = (dt.now() - start_no_thread).total_seconds()\n",
    "# print(f'Total runtime - {runtime_no_thread}')\n",
    "time_used = time.time() - time_start\n",
    "print(\"The time used is %s seconds\"%(time_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4177bb76",
   "metadata": {
    "id": "4177bb76"
   },
   "source": [
    "#### View and save  the company information DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a6678ce",
   "metadata": {
    "id": "8a6678ce"
   },
   "outputs": [],
   "source": [
    "df_final = make_dataframe_and_save(df1 = df, l=l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "NW7tYyUMxAvb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NW7tYyUMxAvb",
    "outputId": "38cd7f49-b175-4bd1-9c21-e8f73a4447c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company_Name            0\n",
       "Company_Page_URL        0\n",
       "Company_Location       16\n",
       "Description_Head        4\n",
       "Website                 0\n",
       "Description             0\n",
       "Founded                 0\n",
       "Team_Size               0\n",
       "Linkedin_Profile      272\n",
       "Twitter_Profile       350\n",
       "Facebook_Profile      613\n",
       "Crunchbase_Profile    357\n",
       "Active_Founder1        13\n",
       "Active_Founder2       211\n",
       "Active_Founder3       758\n",
       "Active_Founder4       949\n",
       "Active_Founder5       993\n",
       "Active_Founder6       999\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df96543",
   "metadata": {
    "id": "5df96543"
   },
   "source": [
    "# B2. Scraping Companies' information  WITH multi-threading\n",
    "\n",
    "Runs for 36 seconds with good network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "867cc8cf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "867cc8cf",
    "outputId": "794029ac-8f80-436b-cd86-6f3581e71e3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total runtime - 35.944235\n"
     ]
    }
   ],
   "source": [
    "import concurrent.futures as cf\n",
    "\n",
    "\n",
    "start_thread = dt.now()\n",
    "l1 = []\n",
    "with cf.ThreadPoolExecutor() as exc:\n",
    "    results = exc.map(scrape_all, url_list)\n",
    "\n",
    "    for result in results:\n",
    "        l1.append(result)\n",
    "\n",
    "runtime_thread = (dt.now() - start_thread).total_seconds()\n",
    "print(f'Total runtime - {runtime_thread}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a7369e",
   "metadata": {},
   "source": [
    "#### View and safe  the company information DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "-XCVMQAEzzWf",
   "metadata": {
    "id": "-XCVMQAEzzWf"
   },
   "outputs": [],
   "source": [
    "df_final_threaded = make_dataframe_and_save(df1 = df, l=l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "VpBTX3zFz6J_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VpBTX3zFz6J_",
    "outputId": "667ff099-8037-48df-b5ff-f33e36c5437e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company_Name            0\n",
       "Company_Page_URL        0\n",
       "Company_Location       16\n",
       "Description_Head        4\n",
       "Website                 0\n",
       "Description             0\n",
       "Founded                 0\n",
       "Team_Size               0\n",
       "Linkedin_Profile      272\n",
       "Twitter_Profile       350\n",
       "Facebook_Profile      613\n",
       "Crunchbase_Profile    357\n",
       "Active_Founder1        13\n",
       "Active_Founder2       211\n",
       "Active_Founder3       758\n",
       "Active_Founder4       949\n",
       "Active_Founder5       993\n",
       "Active_Founder6       999\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_threaded.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e0a626",
   "metadata": {
    "id": "77e0a626"
   },
   "source": [
    "# References and Future Work\n",
    "\n",
    "### Summary of what I did / Issues\n",
    "- I have just succesfully done all the outlined procedures from the begiining of the project.\n",
    "- The script runs on the assumption that the Internet would be available for the script to finish running.\n",
    "\n",
    "### References\n",
    "\n",
    "- How to make infinite scrolling with selenium [https://www.youtube.com/watch?v=qhJ_gMB772U]\n",
    " \n",
    "### Ideas for future work\n",
    "\n",
    "- Analyse the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "641a2d17",
   "metadata": {
    "id": "641a2d17"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "ycombinator_scraper.ipynb",
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