Potential Optimization of `xlsx` Converter Using `pandas` `to_markdown` Method

Hello,

I have observed that the current implementation of the xlsx converter processes sheets by converting them to HTML and then parsing that HTML to Markdown:

for s in sheets:
          md_content += f"## {s}\n"
          html_content = sheets[s].to_html(index=False)
          md_content += (
              self._html_converter.convert_string(
                  html_content, **kwargs
              ).markdown.strip()
              + "\n\n"
          )

I am wondering if this approach is necessary or if it could be optimized by directly using pandas' to_markdown method:

 for s in sheets:
            md_content += f"## {s}\n"
            sheet_md_content = sheets[s].to_markdown(index=False)
            md_content += (
                   sheet_md_content.strip()
                    + "\n\n"
            )

• Is there a specific reason why the implementation relies on converting to HTML and then parsing back to Markdown? For example, does the HTML conversion preserve certain formatting or features that to_markdown does not handle?
• Are there any limitations or edge cases where to_markdown might not be sufficient or could produce different results?
• Would switching to to_markdown impact the fidelity or formatting of the output?

Could you please clarify if the current approach is intentional due to specific requirements? If not, would it be possible to consider this optimization to improve performance ?

Thank you for your insights.

Best regards

Start with the xlsx converter loop shown in the issue and compare its HTML-to-Markdown path with pandas DataFrame.to_markdown(index=False). Investigate whether the two approaches differ for formatting or edge cases, then determine whether the proposed replacement preserves output fidelity and improves performance. Done means the approach is supported by evidence and any resulting behavior is validated.