Job Title,Location,Salary,Company Name

SEN Tutor,"SW1, South West London, SW1A 2DD",Recently,Deckers

"SW1, South West London, SW1A 2DD",Recently,�28 - �33 per hour,Deckers

Recently,�28 - �33 per hour,SEN Tutor,Targeted Provision Ltd

�28 - �33 per hour,SEN Tutor,"SW1, South West London, SW1A 2DD",Deckers

SEN Tutor,"SW1, South West London, SW1A 2DD",Recently,Deckers

"SW1, South West London, SW1A 2DD",Recently,�28 - �33 per hour,Deckers

Recently,�28 - �33 per hour,Supply Chain Administrator,Deckers

�28 - �33 per hour,Supply Chain Administrator,"WC2, Central London, WC2N 5DU",EMBS

Supply Chain Administrator,"WC2, Central London, WC2N 5DU",Recently,Deckers

"WC2, Central London, WC2N 5DU",Recently,Unspecified,CV Screen Ltd

Recently,Unspecified,Accounts Payable Assistant,Deckers

Unspecified,Accounts Payable Assistant,"St James, WC2N 5DU",Deckers

Accounts Payable Assistant,"St James, WC2N 5DU",Recently,Webhelp UK

"St James, WC2N 5DU",Recently,Unspecified,Applause IT Limited

Recently,Unspecified,Total Rewards Analyst,Johnson & Associates Rec Specialists Ltd

Unspecified,Total Rewards Analyst,"WC2, Central London, WC2N 5DU",Johnson & Associates Rec Specialists Ltd

Total Rewards Analyst,"WC2, Central London, WC2N 5DU",Recently,Johnson & Associates Rec Specialists Ltd

"WC2, Central London, WC2N 5DU",Recently,Unspecified,Johnson & Associates Rec Specialists Ltd

Recently,Unspecified,SEN Tutor,Elliot Marsh

Unspecified,SEN Tutor,"WC2, Central London, WC2N 5DU",Elliot Marsh

SEN Tutor,"WC2, Central London, WC2N 5DU",Recently,Get Recruited (UK) Ltd

"WC2, Central London, WC2N 5DU",Recently,�28 - �33 per hour,Elliot Marsh

import csv

import requests

from bs4 import BeautifulSoup

url = 'https://www.totaljobs.com/jobs/in-london'

r = requests.get(url)

html_soup= BeautifulSoup(r.content, 'html.parser')

job_details = html_soup.find('div', class_='ResultsContainer-sc-1rtv0xy-2')

job_titles =job_details.find_all(['h2','li','dl'])

company_name =job_details.find_all('div', class_='sc-fzoiQi')

total_job_info = job_titles + company_name

with open('job_data_2.csv', mode='w', newline='') as file:

writer = csv.writer(file)

writer.writerow(['Job Title', 'Location', 'Salary', 'Company Name']) # header row

min_length = min(len(job_titles), len(company_name))

for i in range(0, min_length - 3):

job_title = job_titles[i].text.strip()

location = job_titles[i+1].text.strip()

salary = job_titles[i+2].text.strip()

company = company_name[i+3].text.strip()

writer.writerow([job_title, location, salary, company])

# print(job_title)

In this code we are fetching information from a job website named totaljobs about job listing available, filters them out according to skills and saves the output

in a local file

This program is able to fetch the:

* Job Title/Role needed

* Company name

* location

* salary

As a data analyst I want to be able to get web large information in csv file.

Acceptance Criteria

- It is done when I can make a request to a specified url.

- It is done when I get response from that url.

- It is done when I get the target content from the url.

- It is done when that content is saved in csv file.

- BeautifulSoup

- requests

- csv file

- The only real difficulty was trying to locate the precise ID and passing robots elements (such as find element by ID, x-path, class, and find_all) that would appropriately transmit the information back.

- In overall our team was succussful to apply python on web scraping to complete our assignment.

- Fork this repository and navigate to the WebScraping-Data-Analytics folder

- Execute the program by running the pydatanalytics.py file using `$ python pydatanalytics.py`

- The program will then fetch the information and put the information into a csv file.

- [@gideonclottey](https://github.com/gideonclottey)

- [@Dev-Godswill](https://github.com/Dev-Godswill)

- [@ozomata](https://github.com/ozomata)

- [@narinder-bit](https://github.com/narinder-bit)

- [@Sonia-devi](https://github.com/Sonia-devi)