Scraping Numbers From Using BeautifulSoup In This Assignment ✓ Solved

Scraping Numbers from HTML using BeautifulSoup In this assig

Scraping Numbers from HTML using BeautifulSoup In this assignment you will write a Python program that uses urllib to read the HTML from the data files provided, parse the data, extract numbers from span tags, and compute their sum.

You will be given two data URLs: a sample data URL (Sum=2553) for testing and the actual data URL (sum ends with 25) for submission. You do not need to save the files locally; your program should read the HTML directly from the URLs. Each student has a distinct data URL, so use only your own URL.

Data format: the file is a table containing names and comment counts; ignore most data and extract only the numbers found in span tags, convert them to integers, and add them up.

Implement a Python script (for example solution.py) that fetches the HTML with urllib, locates all span tags, obtains their text content, converts to integers, and prints the total sum. Sample execution prints a sum for the sample data; for the actual data, print the computed sum as well.

Paper For Above Instructions

Problem understanding and data provenance. The assignment provides two data URLs: a sample dataset with a known sum and an actual dataset whose sum ends with a specific digit. This setup enables both development-time validation and end-user verification. The data file is described as a table of names and comment counts, but the essential operation is to locate numeric values embedded within span tags, extract those numbers, and accumulate them. The emphasis on span tags is meaningful: in many HTML documents, numeric data is wrapped in span elements to allow styling and precise targeting by scripts. The need to use the URL-based data sources prevents reliance on local files and mirrors real-world scraping scenarios where data is retrieved from remote servers (Richardson & Ruby, 2013; McKinney, 2018).

Methodology and technical design. The core of the solution involves three layers: (i) network I/O to fetch HTML content from a given URL using urllib.request, (ii) HTML parsing to locate span elements, and (iii) data conversion and aggregation to sum the numeric contents. The urllib module provides a straightforward interface to read remote resources, with error handling for network issues and HTTP status codes (Python Software Foundation, 2023). Beautiful Soup, a robust HTML parsing library, provides an ergonomic API for traversing the DOM and filtering elements by tag name. The standard workflow is to parse the fetched HTML with Beautiful Soup, collect all span elements via soup.find_all('span'), iterate over these elements, extract the textual content with .get_text(strip=True) or .text, attempt to convert to int, and accumulate the results, while gracefully handling non-numeric values (Mitchell, 2015; Richardson & Ruby, 2013).

Implementation plan and pseudocode. A practical implementation would follow these steps: 1) import urllib.request and bs4 (BeautifulSoup); 2) fetch the HTML using urllib.request.urlopen(data_url) and read the bytes; 3) decode to UTF-8 (if necessary); 4) parse the HTML with BeautifulSoup(html, 'html.parser'); 5) locate all span tags with soup.find_all('span'); 6) initialize a running total; 7) for each span, extract text, clean it, and attempt to cast to int; 8) if conversion succeeds, add to total; 9) print the final sum. In real-world code, it is prudent to implement error handling for network failures, decoding issues, and non-integer content. This approach mirrors common patterns described in tutorials and reference manuals (Bader, 2020; VanderPlas, 2016; Beazley & Jones, 2013).

Validation, testing, and interpretation. With the sample data URL, the program should yield the known sum (2553 in the example). When applied to the actual data URL, the program should produce a sum that ends with 25, as described in the assignment prompt. Validation can be performed by manually inspecting a subset of the span contents to confirm that numeric strings are properly extracted and not contaminated by surrounding whitespace or non-numeric characters. It is also important to verify that the solution behaves gracefully when no span tags exist or when numbers are missing from some spans. Lessons from this validation process align with best practices in data extraction and software testing (Grinberg, 2018; Lutz, 2013).

Reproducibility and reliability considerations. The requirement to read from a data URL per student is a deliberate design choice to promote reproducibility. Each student’s environment should provide a unique URL, ensuring that results are personally verifiable. To improve reliability, one could implement retries on transient network errors, set reasonable timeouts for requests, and log exceptions with contextual information. In more advanced workflows, unit tests could be added to verify that a sample HTML snippet containing several span elements yields the expected numeric sum, reinforcing robust software development practices (Beazley & Jones, 2013; McKinney, 2018).

Potential enhancements and learning outcomes. Beyond the basic task, students can extend the approach to handle more complex HTML structures, extract numbers from various tag types, or apply regular expressions to identify numeric patterns. They can also generalize the solution to aggregate multiple datasets or export results to a file or database. The exercise reinforces essential competencies in Python programming, HTML parsing, data cleaning, and result verification, all of which are foundational for data journalism, data science, and software automation (Mitchell, 2015; VanderPlas, 2016; Ramalho, 2019).

References

1. Mitchell, R. (2015). Web Scraping with Python. O'Reilly Media.
2. Richardson, L., & Ruby, M. (2013). Beautiful Soup Documentation. Retrieved from https://www.crummy.com/software/BeautifulSoup/ (Version 4.x).
3. Python Software Foundation. (2023). urllib.request — Open arbitrary resources. Python 3.x Documentation. https://docs.python.org/3/library/urllib.request.html
4. VanderPlas, R. (2016). Python Data Science Handbook. O'Reilly Media.
5. Ramalho, L. (2019). Fluent Python. O'Reilly Media.
6. Beazley, D., & Jones, B. (2013). Python Cookbook (3rd ed.). O'Reilly Media.
7. Lutz, M. (2013). Learning Python (5th ed.). O'Reilly Media.
8. Bader, D. (2020). Web Scraping with BeautifulSoup: Getting Started. Real Python. https://realpython.com/beautiful-soup-web-scraper-python/
9. McKinney, W. (2018). Python for Data Analysis (2nd ed.). O'Reilly Media.
10. W3C. (2014). HTML5: A vocabulary and associated APIs. W3C Recommendation.