Python Web Scraping Tutorial: Extracting Fast Track 100 Companies with BeautifulSoup

As a data scientist, the first task often involves web scraping; this article demonstrates how to scrape the Fast Track 100 company list using Python.

First, install the BeautifulSoup library with pip , which will be used to parse HTML.

Inspect the target page (https://www.fasttrack.co.uk/league-tables/tech-track-100/league-table/) with the browser's developer tools to understand the table structure, noting that each row is enclosed in a <tr> tag.

In Python, import the required modules: BeautifulSoup for HTML parsing, urllib for fetching the page, and csv (or json ) for saving the data.

Fetch the page content using urllib , store it in a variable (e.g., page ), and create a BeautifulSoup object ( soup ) to process the HTML.

Locate all table rows with soup.find_all('tr') , skip the header row, and iterate over the remaining rows.

For each row, extract the eight columns using find_all('td') and assign them to variables such as rank, company, location, year_end, sales_rise, latest_sales, staff, and comments. Some columns contain extra information like a company description and a link.

Clean the extracted data: split the company cell to separate the name and description using find('span') , remove unwanted characters from the sales field with strip , and handle missing values.

To capture the company website, follow the link in the second column to the detail page, fetch it, parse with BeautifulSoup , and locate the <a> element in the last table row; use a try... except block to set the URL to None if not found.

Append each cleaned row to a list ( rows.append(...) ) and, after processing all rows, optionally print the list for verification.

Finally, write the rows list to an external CSV file using csv.writer , resulting in a file with 100 rows of structured company data ready for further analysis.