To preserve merged cells when converting an HTML table to another HTML format, retain the original <th> and <td> elements and their rowspan and colspan attributes. If the destination is a rectangular grid or DataFrame, expand each cell across the rows and columns it occupies—and keep the original span metadata if you may need to rebuild the merged layout.
What colspan and rowspan preserve
colspan specifies how many columns a cell occupies; rowspan specifies how many rows it occupies. Both default to one. These attributes describe the table’s structure, not just its displayed text: a spanning cell reserves positions, so later cells in the same row or subsequent rows move to the next available position. Counting cell tags in each row therefore does not reliably reveal the table’s logical columns.
The W3C’s HTML 4.01 table specification also assigns special meanings to a span value of zero in the context of row or column groups. Its examples show how spanning cells affect placement, and warn: “Defining overlapping cells is an error. User agents may vary in how they handle this error (e.g., rendering may vary).” If spans conflict, do not silently repair the table and present the result as definitive; flag the issue or document an explicit handling policy.
Choose the output you need
There are two different goals: retaining the source’s merged-cell structure, or representing its contents as a rectangular grid. A grid records values by row and column; the original span attributes record which source cell covered those positions. Choose before transforming the table.
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| Approach | Preserves merged-cell structure | Produces rectangular values | Main tradeoff |
|---|---|---|---|
| Transform the HTML DOM and retain span attributes | Yes, if the transformation keeps the original cells and attributes | No, unless you expand it separately | Best when HTML structure is the deliverable |
| Expand cells into a grid and keep source mapping | Can be reconstructed if mapping and span metadata are retained | Yes | Requires a rule for covered positions and malformed spans |
pandas.read_html |
No source-markup round trip is promised | Yes | Convenient extraction, but cleanup and validation may be needed |
This is a comparison of output behavior, not a benchmark.
Keep the spans when the result is still HTML
- Parse the document into a tree and select the intended table.
- Make changes to the retained cells rather than replacing a spanning cell with multiple independent cells.
- Serialize the table and verify that each original
rowspanandcolspanremains on the correct<th>or<td>.
Beautiful Soup supports modifying and writing a parsed tree. The important preservation step is to avoid deleting or overwriting the span attributes during your transformation. An expanded grid by itself is not enough to recover which positions originated in a single merged cell.
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Expand spans for a rectangular grid
To create a matrix or DataFrame, track which row-and-column coordinates are already occupied. For each source cell, find the next unoccupied position in its row, read its span values (using one when an attribute is absent), and mark the rectangle it covers. Place the cell’s value at its anchor coordinate.
- Maintain an occupancy map for the rows and columns being converted.
- For each cell, skip columns already occupied by cells spanning from an earlier position or row.
- Read
rowspanandcolspan, applying the HTML defaults when the attributes are absent. - Mark every coordinate covered by that rectangle before placing the next cell.
- Choose whether covered positions repeat the anchor value or remain blank. Record that choice because the two layouts mean different things to downstream consumers.
- Keep each source cell’s anchor and span dimensions if the grid may later need to be converted back to merged HTML.
This placement rule follows the specification’s occupancy model and is reflected in pandas’ implementation. Do not assume that repeating a value or leaving covered positions blank is universally correct; select the representation that fits the consumer of the grid.
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Use pandas for extraction, then inspect the result
pandas.read_html searches for tables and returns a list of DataFrames. Its documentation says it attempts to handle colspan and rowspan, while noting that cleanup may still be needed. See the pandas.read_html API reference. Inspect the returned DataFrame for your particular input rather than assuming headers, blank cells, or irregular markup have mapped to the intended schema.
In the development API documentation accessed on October 4, 2026, read_html supports lxml, html5lib, and Beautiful Soup. Without a selected flavor, it tries lxml and falls back to Beautiful Soup plus html5lib if that parse fails. Development documentation may change, so check the documentation and behavior for the pandas release you actually deploy.
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Make parsing reproducible
Malformed markup can produce different parse trees depending on the parser. Beautiful Soup documents this difference and supports multiple parser backends. When consistent results matter, specify the parser explicitly, especially for jobs running on multiple machines. For conversions that must be audited, record relevant library versions alongside the output.
Validate the converted table
- Compare the output’s row and column occupancy with the intended source layout.
- Check that a row-spanning cell blocks its column in later rows before another cell is placed there.
- Check that a column-spanning cell reserves all of its columns before subsequent cells are placed in the same row.
- Inspect
<thead>,<tbody>, and<tfoot>separately. Pandas’ implementation expands these sections and carries remaining rowspan state across them. - Flag spans that overlap other cells or exceed the available structure instead of silently claiming an exact conversion.
- If the output must return to HTML, confirm that the original span attributes remain attached to their source cells; a grid alone does not preserve merged-cell provenance.
For the last check, the distinction between source attributes and expanded grid values is an implementation inference from the HTML occupancy model and pandas’ expanded output behavior, not a promise of automatic round-trip reconstruction.
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