ok so is the plan to deprecate header? why are you adding this rather than we just use the given labels?

ok so is the plan to deprecate header? why are you adding this rather than we just use the given labels?

3 small arguments for use case:

First, progressing Styler as a visualisation tool allowing full customisation of the display, independent of the data and label values. This is already possible where you can now apply a formatting function (1 variable input -> str) to indexes, which is very useful, for example, when formatting dates. But a generic function cannot just overwrite the (visible) labels with user specified values, but this aliases arg will allow that.

Second, the existing IO methods have this headers arg, which allows overwriting column labels with aliases. Personally I think its not sensible to try and squish all the Styler functionality (via chaining) into a single DataFrame.to_html and to_latex call, but the path of least resistance (community approvals wise) seems to be to not take away functionality from existing kwargs. I.e we would likely keep headers=xx and use the styler implementation just refactor as a call to format_index(aliases=xx).

Finally, in a more specialised scenario you might want a formatter that uses a loop index, or combines all level values simulatneously to determine an alias, which you then pass directly here. Note that the indexing of the data can still be maintained underneath via the MultiIndex.

multiindex = MultiIndex.from_product([["Mac", "Windows"],  ["2GB", "4GB"]])
def relabel(os, ram, loop):
    return f"{loop}_{os}_{ram}"
aliases = [relabel(idx[0], idx[1], i) for i, idx in enumerate(multiindex)]
styler.hide(level=0).format_index(aliases=aliases)

      level 1           data
0_Mac_2GB                  x
1_Mac_4GB                  x
2_Windows_2GB              x
3_Windows_4GB              x

not objecting to the need to do this, i get it. but we don't use alias anywhere; why isn't this just .rename()?

rename loosely suggests the data is modified, when it is really just displayed. Therefore, included within the format_index method.

Didn't really feel this warranted its own method when all other index formatting is done within format_index.
(and its not possible to combine the two so there was that also)

But im not tied to anything; you tell me which you prefer and ill make changes...

@jreback what about a different kwarg other that alias, how about names or labels?

@jreback feels like this one has stalled and getting push back anyway. If poss please take one last look since it factors into the PRs where I convert the DataFrame.to_latex and DataFrame.to_html to use the Styler implementation. And if this one is closed then I have to come up with another way of refactoring them. thx

@attack68 circling back around on this.

In order to be able to completely re-write DataFrame.to_latex with the Styler implementation, this is the last piece of functionality needed on the Styler side. Then issues being faced by users, such as stackoverflow q, can be automatically addressed.

@attack68 i am sympathetic here as i know you want to make this fully compatible with the existing formaters. The issues i have here are:

• adding yet another keyword to the api, this bloating the api even more.
• we are merging the concerns here. formatting output data and replacing the data, which we already have a good way of doing via .rename_axis / .set_axis. This leads to more than one way to do something.

I agree that this could be viewed as on the line, but its much nicer to keep this api nice and tight and use the existing fluent api to solve this problem.

I see that we already have styler methods to do some index manipulation e.g. https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.io.formats.style.Styler.format_index.html?highlight=styler (.apply_index, format_index). I guess ok to add a .rename_index as a method here might be ok. (this preserves the fluent api and doesn't add yet another keyword).

@jreback, OK, yes I see your point, rename_index could also serve an additional purpose, which I will explain in the docs write up. Will look to do that instead.