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class_linkedin.py
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class_linkedin.py
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#!/usr/bin/env python
# coding: utf-8
# In[4]:
import warnings
warnings.filterwarnings("ignore")
import requests
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import bs4
from urllib.request import urlopen
import time
import re
import time
# In[11]:
class Linkedin:
def __init__(self):
self.x = 'Hello'
@classmethod
def LINKEDIN_Scrapping(clc,job_search):
if job_search == "data analysis":
link1 = 'https://www.linkedin.com/jobs/search?keywords=Data%20Analysis&location=&geoId=&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0'
elif job_search == "machine learning":
link1 = 'https://www.linkedin.com/jobs/search?keywords=machine%20learning&location=&geoId=&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0'
elif job_search == "software testing":
link1 = 'https://www.linkedin.com/jobs/search?keywords=software%20testing&location=&geoId=&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0'
# FIRST get main informations about jobs
title = []
location = []
country = []
company_name = []
post_time = []
Title =[]
company_logo =[]
data = requests.get(link1)
soup = BeautifulSoup(data.text)
Title = soup.find_all('h3' , {'class': 'base-search-card__title'})
for x in range(len(Title)):
t = re.split('[(|-]',Title[x].text)
title.append(t[0].strip())
location.append(soup.find_all('span' , {'class': 'job-search-card__location'})[x].text.replace('\n',' ').strip())
m = soup.find_all('h4' , {'class': 'base-search-card__subtitle'})[x].text
company_name.append(m.replace('\n',' ').strip())
company_logo.append(soup.find_all('img')[x]['data-delayed-url'])
tt = soup.find_all('time')[x]
post_time.append(tt.text.replace('\n',' ').strip())
time.sleep(3)
# function to get jobs' links
def get_links(url):
links_ =[]
data = requests.get(url).text
soup = BeautifulSoup(data)
llinks = soup.find_all('a' , {'class': 'base-card__full-link absolute top-0 right-0 bottom-0 left-0 p-0 z-[2]'})
for l in llinks:
links_.append(l['href'])
return links_
# apply links function
links = get_links(link1)
# get more about jobs
Employment_type = []
Job_function = []
Seniority_level = []
Industries = []
# function to get more about jobs
def other(urls):
frames =[]
for url in urls:
data1 = requests.get(url)
soup1 = BeautifulSoup(data1.content)
j = soup1.find('ul' , {'class': 'description__job-criteria-list'})
time.sleep(3)
jj=j.find_all('h3')
dic ={}
for i in range(len(jj)):
dic[jj[i].text.replace('\n',' ').strip()] = j.find_all('span')[i].text.replace('\n',' ').strip()
output = pd.DataFrame()
output = output.append(dic, ignore_index=True)
frames.append(output)
result = pd.concat(frames)
return result
# apply Other function
df = other(links)
df.fillna('Not_Found',inplace= True)
df.reset_index(inplace=True, drop=True)
# function to get job description
def discription(urls):
nn = pd.DataFrame()
for link in urls:
try:
dict_desc={}
heading =[]
heading_values_final =[]
data1 = requests.get(link)
soup1 = BeautifulSoup(data1.text )
l = soup1.find('div',{'class':'show-more-less-html__markup'})
time.sleep(4)
desc = l.find_all('ul')
# get lists of points req.
if len(desc) != 0:
heading_values =[]
for i in range(len(desc)):
desc1 =desc[i].find_all('li')
time.sleep(3)
head=[]
for ii in range(len(desc1)):
st=""
st='('+str(ii+1) +') '+desc1[ii].text
head.append(st)
heading_values.append(head)
heading_values_final.append(heading_values)
try:
# get heading of points req.
# first get thier location
c1 =[]
for a in range(len(desc)):
count =0
for i1 in l.children:
if type(i1) != bs4.element.NavigableString:
count +=1
if i1.text == desc[a].text:
c1.append(count)
# get the exact heading for each req.
list11=[]
for h in c1:
llist1=[]
flag=0
for i in l.children:
if type(i) != bs4.element.NavigableString:
flag+=1
for i2 in l.find_all(['br' and 'strong']) :
if (i.text == i2.text) and flag<=h:
llist1.append(i.text.strip())
list11.append(llist1[-1])
heading.append(list11)
except: # if heading lines are not highlights -_-
# first get thier req. location
c1 =[]
for a in range(len(desc)):
count =0
for i1 in l.children:
if type(i1) != bs4.element.NavigableString:
count +=1
if i1.text == desc[a].text:
c1.append(count)
if (c1[0] ==1) and (len(c1)==1): # not found any headings
heading.append(["***"])
else:
list11 =[]
for h in c1:
llist1=[]
flag=0
for i in l.children:
if type(i) != bs4.element.NavigableString:
flag+=1
if h == flag+1 :
if i.text == "":
llist1.append("Description and requirements: ")
else:
llist1.append(i.text.strip())
list11.append(llist1)
heading.append(list11)
for j in range(len(heading[0])):
dict_desc[heading[0][j]] = []
f =0
i = len( heading_values_final[0])
for keys in dict_desc.keys():
dict_desc[keys] = heading_values_final[0][f]
f +=1
if f == i :
break
nn = nn.append(dict_desc, ignore_index=True, sort=False)
else: # if we can't get points in description (i will print all the text)
dict_desc['All_About_Job'] = l.text.replace('\n',' ').strip()
nn = nn.append(dict_desc, ignore_index=True, sort=False)
except: # if we can't acess the job link (i will print not found in its description)
dict_desc['All_About_Job'] = "Not_Found"
nn = nn.append(dict_desc, ignore_index=True, sort=False)
return nn
# apply description function to all links
df_desc = discription(links)
# put all together
df_ml = pd.DataFrame({'Title' : title , 'Location' : location ,'Company_Logo':company_logo,'URLs':links ,'Company_Name' : company_name ,'post_time':post_time})
result_with_desc = pd.concat([df_ml, df , df_desc], axis=1)
result_without_desc = pd.concat([df_ml, df ], axis=1)
result_with_desc.to_excel('LINKEDIN_scrapping_with_description.xlsx',index=False,encoding='utf-8')
result_without_desc.to_excel('LINKEDIN_scrapping_without_description.xlsx',index=False,encoding='utf-8')
return result_with_desc , result_without_desc
# In[ ]: