<jupyter_start><jupyter_text>7. Scrape the details of Data science recruiters from naukri.com.<jupyter_code>import pandas as pd
from bs4 import BeautifulSoup
import requests
import selenium
from selenium import webdriver
import time
driver=webdriver.Chrome("chromedriver.exe")
driver.get("https://www.naukri.com/")
search_btn1=driver.find_element_by_xpath("/html/body/div[1]/div[1]/div/ul[1]/li[2]/a/div")
search_btn1.click()
url="https://www.naukri.com/hr-recruiters-consultants"
driver.get(url)
search_job=driver.find_element_by_xpath("//input[@class='sugInp']")
search_job.send_keys("<NAME>")
search_btn2=driver.find_element_by_xpath("//*[@id='qsbFormBtn']")
search_btn2.click()
name=[]
des=[]
company1=[]
skills=[]
loc=[]
titles_tag=driver.find_elements_by_xpath("//span[@class='fl ellipsis']")
for i in titles_tag:
    name.append(i.text)
name[0:5]
titles_tag1=driver.find_elements_by_xpath("//span[@class='ellipsis clr']")
for i in titles_tag1:
    des.append(i.text)
des[0:5]
titles_tag2=driver.find_elements_by_xpath("//*[@id='tabP-1']/div/div/div[1]/div[1]/p/a[2]")
for i in titles_tag2:
    company1.append(i.text)
company1[0:5]
titles_tag3=driver.find_elements_by_xpath("//div[@class='hireSec highlightable']")
for i in titles_tag3:
    skills.append(i.text)
skills[0:5]
titles_tag4=driver.find_elements_by_xpath("//small[@class='ellipsis']")
for i in titles_tag4:
    loc.append(i.text)
loc[0:5]
import pandas as pd
Job=pd.DataFrame()
Job['Name']=name[0:49]
Job['Designation']=des[0:49]
Job['Company']=company1[0:49]
Job['Skills_they_hire_for']=skills[0:49]
Job['Location']=loc[0:49]
Job<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17986494782074894, 'fraction_numerical': 0.03130755064456722, 'mean_word_length': 7.956043956043956, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 2, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '472671', 'license': 'Apache-2.0', 'n_tokens_mistral': 685, 'n_tokens_neox': 672, 'n_words': 91, 'path': '7 question WEB4.ipynb', 'repo_name': 'ramyuva/WEB-4', 'size': '1635'}