Now we can use that custom styler. New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already existing way of having the min value at the left side of the cell), and you can pass a list of [color_negative, color_positive]. These are styles that apply to the table as a whole, but don’t look at the data. You might want to consider a package for styling Excel files after they’re created. We’ll show an example of extending the default template to insert a custom header before each table. df. We’ve also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page (you can set the uuid if you’d like to tie together the styling of two DataFrames). If we wanted to hide the index, we could write: Similarly, if we wanted to hide a column, we could write: I mentioned earlier in the article that the Style API is Pandas is still experimental. Pandas developed the styling API in 2019 and it’s gone through active development since then. An example of converting a Pandas dataframe to an Excel file with column formats using Pandas and XlsxWriter. We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain. The end styling is accomplished with CSS, through style-functions that are applied to scalars, series, or entire dataframes, via attribute:value pairs. In our dataframe pivot, the columns Sales represents the total number of sales in dollars. These are placed in a ``