The index can be hidden from rendering by calling Styler.hide_index. This is where color scales come into play. These are styles that apply to the table as a whole, but don’t look at the data. Consider following us on social media! In addition there was a subtle bug in prior pandas versions that would not allow the formatting to work correctly when using XlsxWriter as shown below. Check out my ebook for as little as $10! Tables allow your data consumers to gather insight by reading the underlying data. If formatter is None, the default formatter is used. The above output looks very similar to the standard DataFrame HTML representation. We can’t export all of these methods currently, but can currently export background-color and color. Thankfully, Pandas makes it easy without having to duplicate the code you meticulously created. selector is the CSS selector that props will apply to. ExcelWriter ('pandas_conditional.xlsx', engine = 'xlsxwriter') # Convert the dataframe to an XlsxWriter Excel object. Here’s a boring example of rendering a DataFrame, without any (visible) styles: Note: The DataFrame.style attribute is a property that returns a Styler object. In this article, we will focus on the same. There’s also .highlight_min and .highlight_max. But we’ve done some work behind the scenes to attach CSS classes to each cell. to_excel (writer, sheet_name = 'Sheet1') # Get the xlsxwriter workbook and worksheet objects. Certain CSS classes are attached to cells. So it’s certainly a bit limited. Pandas matches those up with the CSS classes that identify each cell. While we could accomplish this using functions and the applymap method, Pandas thankfully has methods built-in directly to highlight the maximum and minimum values. These are placed in a ``