1. Determine what web page data you want to scrape.

2. Identify the web page elements that you need for web scraping

3. Check if the web page has an API (Application Programming Interface)

4. Find the HTML table headers for your web scraping

5. Save web content as .txt file with headers for each web scraping column

6. Use web scraping programming language to extract web page elements

7. Inspect web scraping code for errors and troubleshoot

8. Set web scraping on a schedule with web scraping automation software

9. Save web content as .CSV (comma separated value) file for analysis

10. Look up automated web scraping tools that simplify the process

1 . Determine what data you want to scrape from a web page:

For example, the web page contains some sort of list of items, such as menu items or top-selling items at your favorite retailer's website, etc. In this case, each item is displayed as a hyperlink with an HTML table element surrounding it. This makes it easy to determine which web page elements are needed for web scraping.

2 . Identify web page elements that you need for web scraping:

The web page will contain hyperlinks with HTML table elements surrounding them, which are easy to identify as web page elements needed for web scraping. Some web pages will require you to search the site's menu structure or other parts of the website. Using "inurl:" command in Google (News - Alert) can be helpful here because it will show results containing specific phrases within web addresses (URLs). Also using web browser developer tools can help you find relevant web page elements quickly.

3 . Check if the web page has an API (Application Programming Interface):

If your required data is available through an API, then try to use it instead of web scraping, because web scraping can be an overkill when you just want access to web page data via API.

4 . Find web page elements that contain headers for web scraping:

HTML table headers are also web page elements needed for web scraping, which shows the name of each column in a table. If your required web page data is in HTML table format, then you need to find out the web page element containing the header in order to import it into Excel or scrape it properly. You can use "inspector" feature of Google Chrome browser developer tool, by right-clicking on a web table and selecting "Inspect Element", so that you can easily see which web elements surround a given area on a web page containing the information you require.

5. Save web content as txt file with headers for each web scraping column:

If your required web page data is in HTML table format, then you need to save web table web page elements in a .txt file with proper web page headers for all columns used in web scraping. It would look like this (just an example and not real web page data): "name","description","price","category" "widget 1","hello world!","10$", "Widget Maker" "widget 2","life of a widget maker.","15$", "Widget Maker" ...etc. Make sure that each line of the web page data .txt file matches the web page element order on the web page itself. You can name this web page data file anything you want, but it is recommended to use the name of the web page itself + "_data.txt" extension (e.g. "example_web_page.html" becomes "example_web_page_data.txt").

6. Use web scraping programming language to extract web page elements:

After saving web content as a txt file with web page headers, the next step is to use a web scraping programming language to extract web page elements into respective columns in the txt file. In this example, Python can be used because it has libraries that make web scraping easy (see below for more information).

7 . Inspect web scraping code for errors and troubleshoot:

Now that you have written your web scraping code, it is a good idea to inspect it for errors and troubleshoot any problems. You can use online web scraping tools such as ScrapeStorm or Web scraper for this purpose.

8. Save extracted web page data into Excel:

The next step is to save the extracted web page data from the txt file into Excel. This can be easily done using Python libraries such as Pandas and XlsxWriter.

9. Clean up the Excel spreadsheet data:

After extracting web page data into Excel, you may need to clean it up by removing any unwanted columns or rows, or by converting text values into numbers or dates. This can be easily done in Excel itself.

10. Visualize web page data in Excel:

Now that you have web page data extracted into Excel, you can visualize it by using the Pivot Table feature of Excel or other web visualization tools such as Google Data Studio and Microsoft (News - Alert) Power BI.

What are web scraping libraries?

A web scraping library is a collection of web scraping code written in Python (or any other web programming language). Libraries make web scraping easy because all the hard work has already been done for you; this means less time writing web scraping code which means more time analyzing web scraped data. Example Python libraries for web scraping include Scrapy , Beautiful Soup , Selenium Webdriver , etc.; while Google Chrome browser developer tool features an inbuilt HTML parser that makes web scraping easier.

Examples of web scraping with Python: web scraping web page elements with Scrapy, web crawling web pages using Scrapy, web crawling web pages with Selenium Webdriver, web scraping images with Scrapy, and web scraping social media data using Beautiful Soup.

Why do I need to know about web page headers?

To be able to scrape specific web page elements by their names, you must first find out which are the header names used in a table on a given web page. This is because every HTML table has its unique set of headers for each column so that browsers or programs can identify what data goes into which cell when parsing/extracting it. By importing only the required table columns into Excel, you will save time and avoid the need to further clean up the data. In Python, you can use the ‘lxml’ library to find table headers on web pages.

So what is web scraping?

Web scraping is a technique used to extract data from websites into a tabular/spreadsheet format for further analysis. It involves using web programming languages (such as Python) to write code that extracts specific data from web pages into a text file, which can then be easily imported into Excel for further analysis. Web scraping is also often referred to as web crawling or web harvesting, but they are not exactly the same thing.

Web scraping is an essential skill for anyone working with data - whether it's analyzing trends on the web or simply downloading a list of products from a website to your accounting software. It's a very important web skill to have, and it doesn't take a lot of time or effort to learn.