Power Query: Is there a workaround for a website that limits its data to 30 rows?

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Summary

The issue at hand is web scraping a betting website that limits its data to 30 rows. The user is trying to use Power Query to scrape the data into Excel, but is running into the website’s restriction. The key takeaway is that Power Query has limitations when dealing with websites that impose row limits.

Root Cause

The root cause of this issue is:

  • The website’s restriction on data retrieval, which limits the number of rows that can be retrieved at one time
  • Power Query’s inability to bypass this restriction without additional coding or workarounds

Why This Happens in Real Systems

This issue occurs in real systems because:

  • Websites often impose restrictions on data retrieval to prevent abuse or overload
  • Web scraping tools like Power Query are designed to work within these restrictions, but may not always be able to bypass them

Real-World Impact

The real-world impact of this issue is:

  • Limited data retrieval, which can hinder analysis or reporting efforts
  • Inability to automate data retrieval processes, which can lead to manual workarounds or inefficiencies

Example or Code (if necessary and relevant)

import requests
from bs4 import BeautifulSoup

url = "https://sports.williamhill.com/betting/en-gb/football/matches/date/today/match-betting"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Parse the HTML content to retrieve the data
data = []
for row in soup.find_all('tr'):
    data.append([cell.text.strip() for cell in row.find_all('td')])

# Print the retrieved data
for row in data:
    print(row)

How Senior Engineers Fix It

Senior engineers fix this issue by:

  • Using coding languages like Python to scrape the website and bypass the row limit restriction
  • Implementing workarounds, such as pagination or API calls, to retrieve the data in chunks
  • Utilizing web scraping libraries like BeautifulSoup or Scrapy to parse the HTML content and extract the data

Why Juniors Miss It

Juniors may miss this issue because:

  • Lack of experience with web scraping and data retrieval techniques
  • Unfamiliarity with coding languages like Python or web scraping libraries like BeautifulSoup
  • Overreliance on Power Query or other GUI-based tools, which may not always be able to bypass website restrictions