@PrabhjotSingh Thank you for the helpful reference.

@FaheemMitha dataframe.iloc[::-1, :] might be clearer just a bit. But you still need to know how slices (or at least reversed slices) work in Python.

@AndrasDeak Why would that be clearer?

The answer by Greg Hewgill is quite clear and helpful. The relevant para is

Slice objects also behave slightly differently depending on the number of arguments, similarly to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

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And that's all text, as far as I know.

Debian is now so big that just fetching package information is the size of a small library.

@FaheemMitha because dataframes are 2d objects, and [::-1, :] might make it more obvious that you're operating on rows while keeping column order intact

@AndrasDeak True, it's not obvious that the slicing is operating on rows, in the case of a Pandas dataframe.

it matches the basic behaviour of python and numpy, but yeah

May I ask for a final close vote on

(assuming someone else agrees that it should be closed)

would that I could

@StephenKitt Voted

@Archemar thanks!

Thanks @JeffSchaller

2016 you just helped me with a problem

You're welcome!