004 - wuzzuf_jobs-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping wuzzuf for top skills in a specific job profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "from selenium.webdriver.common.by import By\n",
    "import time\n",
    "import csv\n",
    "import random\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#chrome deiver path\n",
    "chromepath = \"C:\\chromedriver_win32\\chromedriver.exe\"\n",
    "\n",
    "s = Service(chromepath)\n",
    "\n",
    "driver = webdriver.Chrome(service=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-52-be5f0f218ed0>:3: DeprecationWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  driver.find_element_by_css_selector('input[class=\"search-bar-input\"]').send_keys(\"data scientist\")\n",
      "<ipython-input-52-be5f0f218ed0>:5: DeprecationWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  driver.find_element_by_css_selector('button[class=\"btn btn-primary search-btn\"]').click()\n",
      "<ipython-input-52-be5f0f218ed0>:7: DeprecationWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  num_jobs = int(driver.find_element_by_css_selector('span[class=\"css-xkh9ud\"]').find_element_by_tag_name('strong').text)\n",
      "C:\\Users\\ahmed eltabakh\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\webelement.py:340: UserWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  warnings.warn(\"find_element_by_* commands are deprecated. Please use find_element() instead\")\n",
      "<ipython-input-52-be5f0f218ed0>:27: DeprecationWarning: find_elements_by_* commands are deprecated. Please use find_elements() instead\n",
      "  job_divs = driver.find_elements_by_css_selector('div[class=\"css-1gatmva e1v1l3u10\"]')\n",
      "C:\\Users\\ahmed eltabakh\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\webelement.py:483: UserWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  warnings.warn(\"find_element_by_* commands are deprecated. Please use find_element() instead\")\n",
      "<ipython-input-52-be5f0f218ed0>:35: DeprecationWarning: find_element_by_* commands are deprecated. Please use find_element() instead\n",
      "  skills_div = driver.find_element_by_css_selector('div[class=\"css-s2o0yh\"]')\n",
      "C:\\Users\\ahmed eltabakh\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\webelement.py:501: UserWarning: find_elements_by_* commands are deprecated. Please use find_elements() instead\n",
      "  warnings.warn(\"find_elements_by_* commands are deprecated. Please use find_elements() instead\")\n"
     ]
    }
   ],
   "source": [
    "driver.get(\"https://wuzzuf.net/jobs/egypt\")\n",
    "\n",
    "driver.find_element_by_css_selector('input[class=\"search-bar-input\"]').send_keys(\"data scientist\")\n",
    "\n",
    "driver.find_element_by_css_selector('button[class=\"btn btn-primary search-btn\"]').click()\n",
    "\n",
    "num_jobs = int(driver.find_element_by_css_selector('span[class=\"css-xkh9ud\"]').find_element_by_tag_name('strong').text)\n",
    "\n",
    "num_pages = math.ceil(num_jobs / 15)\n",
    "\n",
    "source_link = \"https://wuzzuf.net/search/jobs/?a=hpb&q=data%20scientist&start=\"\n",
    "\n",
    "pages_links = []\n",
    "\n",
    "for i in range(num_pages):\n",
    "    link = source_link + str(i)\n",
    "    pages_links.append(link)\n",
    "    \n",
    "job_profiles = []\n",
    "skills = []\n",
    "\n",
    "for page in pages_links:\n",
    "    driver.get(page)\n",
    "    time.sleep(2)\n",
    "    \n",
    "    job_links = []\n",
    "    job_divs = driver.find_elements_by_css_selector('div[class=\"css-1gatmva e1v1l3u10\"]')\n",
    "    for div in job_divs:\n",
    "        job_link = div.find_element_by_css_selector('a[class=\"css-o171kl\"]').get_attribute('href')\n",
    "        job_links.append(job_link)\n",
    "        \n",
    "    for job_link in job_links:\n",
    "        driver.get(job_link)\n",
    "        time.sleep(2)\n",
    "        skills_div = driver.find_element_by_css_selector('div[class=\"css-s2o0yh\"]')\n",
    "        for skill in skills_div.find_elements_by_css_selector('span[class=\"css-158icaa\"]'):\n",
    "            skills.append(skill.text)\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "358"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(skills)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unique skills\n",
    "unique_skills = list(set(skills))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skill_counts = []\n",
    "for skill in unique_skills:\n",
    "    count = skills.count(skill)\n",
    "    skill_counts.append(count)\n",
    "len(skill_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>skill</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Development</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TensorFlow</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Git</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Science</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCP</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         skill  count\n",
       "0  Development      1\n",
       "1   TensorFlow      2\n",
       "2          Git      4\n",
       "3      Science      2\n",
       "4          TCP      2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skills_dict = {'skill': unique_skills, 'count': skill_counts}\n",
    "skill_df = pd.DataFrame(skills_dict)\n",
    "skill_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_skills = skill_df.sort_values(by='count', ascending=False).iloc[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(10, 7))\n",
    "\n",
    "# Plot the total crashes\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=\"count\", y=\"skill\", data=top_10_skills,\n",
    "            label=\"skill\", color=\"b\")\n",
    "\n",
    "#Add a legend and informative axis label\n",
    "#ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 25), ylabel=\"Skill\",\n",
    "       xlabel=\"Skill Count\", title=\"Top Skills For Data Scientist\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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