What are the Steps to Get Website Data using Python?

Lovely
Jun 30, 2022

This is the world wherein websites have every detail of your competitors, such as what products he sells at what price, what keywords he uses, and who the target audience is. These questions do not end here. You can have as many details or points about trends, competition, marketplace, or more in a few minutes.

Do you want to know how?

It’s through WEB SCRAPING, which is also called web data extraction.

Steps to Getting Web Data Extracted Using Python

The scraping of a website involves a code-run process, which involves sending a request to the URL, the server responds by sending the data and permitting it to go through its HTML or XML pages. The written code or script parses those pages, finds data, and finally extracts it.

Step 1: Arrange libraries & resources

For running through the aforementioned steps, you need to have some valuable libraries used in Python. Web scraping using python makes it easier to have desirable datasets.

Selenium: This library allows web testing while automating browser activities.
BeautifulSoup: It’s a package that allows parsing HTML and XML documents while creating parse trees for defining the entire flow of extraction.
Pandas: This is a library defining all controls to manipulate data for analysis. Besides, you may extract and save data herein in the desired format.
Installation required: Install Python 2.x or Python 3.x with the aforesaid libraries and Ubuntu operating system

Let’s get started to discover how Python language can help with it.

Step 2: Figure out the URL of the website that you want to scrape.

This can be any website’s URL.

Step 3: Examine the page

It’s noteworthy that most web content is put inside tags. So, we have to go through them to see under which tag the desirable information is nested.

For this purpose, just right click the element that you want to discover and select “Inspect” from the dropdown options. This action will open a “Browser Inspector Box”. It carries all codes.

Step 4: Decide the content you want to extract

Before moving to the next technical step, you should categorize the details that you want to get from the web page. It would certainly be available in the “div” tag, which defines every content title.

It can be a product, price, stars, or more.

Step 5: Write a script or code.

Now, you need to create a Python file. For this, move to Ubuntu operating system. Open and type gedit

It would look like this:

1| gedit

Then, start importing libraries this way:

1| from selenium import webdriver

2| from BeautifulSoup import BeautifulSoup

3| from pandas as pd

Now, you need to configure Google Chrome. It requires setting the path to chromedriver like this:

1| driver = webdriver. Chrome (/usr/lib/chromium-browser/chromedriver")

Once done, go ahead by opening the URL as below:

1| Product = [] #list to the name of the product

2| price = [] #list to the price of the product

3| driver.get (“https://www.???????.com/”> https://…………..”)

This is how your URL will be accessible through this code.

Let’s move to extracting the data from the website. We need to get to

tags to extract any required information.

For this, we have to run a search for these tags with the respective class names. Then, extract desirable information

1| content = driver.page_source

2| soup = BeautifulSoup(content)

3| for a in soup. findAll(‘a’,href=True, attrs={‘class’:’_31qSD5′}):

4| product name=a.find(‘div’, attrs={‘class’:’_3wU53n’})

5| price=a.find(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})

6| products.append(name.text)

7| prices.append(price.text)

Step 6: Run it and get data

Now, you need to run the aforesaid code or script. For this purpose, type this syntax:

1| python xxx.py

Your code will start functioning. The data will be extracted.

Step 7: Save and store data in a requested format

This is the last step for scraping web content using python, which pushes to store data in a desirable format. You may think about the format, as CSV or PDF, or any other file. Separate this extension with the comma.

It should be like this:

1| df = pd.DataFrame ({‘Product Name’: products, ‘Price’: prices})

2| df. To _csv(‘product.csv’, index= False, encoding= ‘utf-8’ )

This is how the entire scraping will take place.

Wrap Up

Web scraping through python involves a few steps and resources, which can be libraries & Chrome browser. The process begins with calling the resources. Call the URL, examine what you need to extract, decide, write the scraping code, run, and then, store the data.