Scraping a Table From a Website: Three Approaches

A table is the simplest object to collect and simultaneously a source of non-obvious errors. Here are three approaches and the traps.

Approach 1: by hand

Select, copy, paste into a spreadsheet. The browser usually preserves rows and columns.

When it works: a one-off task, one page, not many rows.

When it stops: the table is paginated, there are thousands of rows, or the task repeats regularly. Copying becomes more expensive than a script quickly - and switching earlier is better, because manual work also makes mistakes.

The trap: not everything copied. If rows load on scroll, only what is visible reaches the clipboard.

Approach 2: ready tools

Spreadsheet applications can import tables from a page by URL, browser extensions exist, and there are visual tools.

When it works: the table is marked up as a table, is available without sign-in, updates regularly and updating is what you need.

Limits:

• Only works with genuine table markup. A set of blocks will not be recognised.
• Cannot handle content drawn by scripts.
• Pagination is not followed.
• Authentication is usually unavailable.

A useful property: some tools refresh the data automatically. For a regularly changing table that is a complete solution with no code at all.

Approach 3: in code

Full control and the only option for complex cases.

The order is the same as in any collection: first check whether the data is in the raw response. Often the table arrives as a structured payload in a background request - then there is no markup to parse at all, which is the best case.

If the data is in the markup, the task reduces to walking rows and cells. Parsing libraries handle that out of the box, and some tools convert a table into a data structure in one call.

On stack and reliability: scraping in Python.

Common traps

Merged cells. The leading cause of wrong data. A merged cell spans several rows or columns, and without expansion the subsequent values shift. The correct handling is repeating the value in every row it covers.

Multi-level headers. A two-row header requires deciding what to call the columns. Better decided at collection time than untangled in the export.

Pagination. A five-page table is five requests, and the last page is usually partial. Check the total row count if the page states one.

Loading on scroll. Rows appear as you scroll. Not collectable without browser automation.

Not a table. Visually a table, in the markup a set of blocks. Row iteration will not work; you have to parse the block structure.

Numbers as text. Spaces as thousand separators, commas instead of points, a currency symbol in the same cell. Convert at collection time rather than later in the editor.

Hidden rows and columns. Present in the markup, invisible on screen. They land in the export unless filtered out.

Verifying the result

Three quick checks that catch nearly everything:

1. The row count matches what the page states.
2. Columns have not shifted: look at several random rows and compare with the page.
3. Types are right: numbers as numbers, dates as dates.

Exporting the result: scraping to Excel. The overview is in the scraping guide.