World Population Scrape
A Beautiful Soup workflow that turns a live country population table into a clean Pandas dataset and reusable CSV.
The question
Collect a full country-level population table from the web without copying rows manually, while preserving consistent headers and structure.
The approach
I requested the source page, parsed its HTML with Beautiful Soup, located the population table, extracted its header cells and rows, assembled a Pandas DataFrame, and exported the result to CSV.
The outcome
The notebook creates a repeatable route from live web content to analysis-ready population data that can be refreshed when the source changes.
Analysis questions
What the work needed to answer.
- 01
How can the live table be collected in a repeatable way?
- 02
How should table headers and rows be mapped into a DataFrame?
- 03
How can the result be stored for future analysis?
Method
From raw data to a useful answer.
Fetch
Used Requests to retrieve the live Worldometer population page.
Parse
Loaded the page into Beautiful Soup and located the country table and header cells.
Structure
Looped through table rows and cells, appending consistent records to a Pandas DataFrame.
Export
Saved the structured table as CSV so it can be reused in notebooks, dashboards, or reporting.
Results
What the analysis revealed.
The page is consistently structured
HTML table headers and data rows can be mapped directly into an analysis-ready schema.
The workflow is refreshable
Because the collection steps are scripted, the dataset can be regenerated instead of maintained by hand.
Separation keeps it reusable
Web retrieval, HTML parsing, tabular construction, and export are distinct steps that are easy to test or replace.
CSV broadens access
The exported file can be opened by spreadsheets, visualization tools, databases, and other Python workflows.