Get the latest government job information using Python

Government jobs are in high demand across the globe as they offer job stability, decent salary, and several other advantages. However, finding and managing these notifications can be a difficult process. This article will teach you how to use Python to crawl the latest government employment announcements.

Installation and syntax

Before we begin, we need to install the required Python packages. The two packages we will use are requests and BeautifulSoup. We can install these packages using pip.

Here are the commands to install them:

pip install requests
pip install beautifulsoup4

Once we have the required packages installed, we can start importing them into our Python code:

import requests
from bs4 import BeautifulSoup

algorithm

• First of all, we need to find the website that lists government job notifications.

• We will then use the requests package in Python to send requests to the website.

• Next, we will extract the HTML content of the website using the content attribute of the response object.

• We will use the BeautifulSoup package to parse HTML content.

• Finally, we will extract relevant job notification details from the parsed HTML content.

Example

Now let us use the above algorithm by crawling the job announcement information from the Indian government website (https://www.sarkariresult.com/latestjob).

import requests
from bs4 import BeautifulSoup

# Define the URL to scrape
url = "https://www.sarkariresult.com/latestjob.php"

# Function to get the HTML content of the website
def get_html(url):
   response = requests.get(url)
   return response.text

# Get the HTML content of the website
html_data = get_html(url)

# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(html_data, 'html.parser')

# Find the job notification details
job_details = soup.find_all("div", id="post")

# to store the scraped data
job_notifications = []

# Loop through each job notification and extract the details
for job in job_details:
   job_notification = job.get_text()
   job_notifications.append(job_notification)

# Print the job notifications
for notification in job_notifications:
   print(notification)

Output

UKPSC Jail Warden Online Form 2022 Last Date : 18/01/2023
NTA UGC NET December 2022 Online Form Last Date : 17/01/2023
Central Silk Board Various Post Online Form 2023 Last Date : 16/01/2023
MPESB High School TET Online Form 2023 Last Date : 27/01/2023
DSSSB PGT Economics Online Form 2023 Last Date : 01/02/2023
CRPF HC Ministerial and ASI Steno Online Form 2023 Last Date : 25/01/2023
AAI Junior Executives Online Form 2022  Last Date : 21/01/2023

illustrate

• Import the requests module to make HTTP requests to the given URL.

• Import the BeautifulSoup module to parse the HTML content of web pages.

• The URL of the website to be crawled is defined as https://www.sarkariresult.com/latestjob.php.

• The get html function was developed to get the HTML content of the website by sending an HTTP request using the requests.get() method and sending the result as text.

• Use the URL as input when calling the get html method to obtain the HTML content of the website.

• Use BeautifulSoup and the specified parser html.parser to parse HTML content.

• Get job notification details by finding all div tags with id="post".

• Initialize an empty list job_notifications to store the captured data.

• The loop is used to extract the text from each job notification by calling the get_text() method on each div tag and appending it to the job_notifications list.

• Finally, print job notifications by looping through the job_notifications list and printing each notification.

app

It can be further extended to get job notifications from other government job portals. Additionally, the scraped data can be stored in a database or CSV file for future reference or monetized by adding brokerage fees to make the aggregated data into a job portal.

in conclusion

In this tutorial, we learned how to scrape government job notifications from the web using Python. We first installed the necessary packages and then introduced the algorithm in detail. We then put the algorithm into practice by scraping job notification details from the Indian government’s job portal. We also discuss possible applications of the code.

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