One common case where this is attractive is where you have a bunch of "public" functions built on top of each other, with the higher ones passing that parameter on and not otherwise caring what it is:

  high_level_function(src, ...)
      ...
      mid_level_function(src, ...)
      ...

  mid_level_function(src, ...)
      low_level_function(src, ...)
      ...

  low_level_function(src, ...)
      ...

If you can make the low-level function generic one way or another, you avoid having to duplicate the higher-level functions.

Another case is where there are two or more parameters that you want to be generic in this sense: if your only option is duplicating the function you can end up having to define inconveniently many variants.

Yeah, that is convenient and I do that often. But I try to wrap the variables in different types depending on their source (e.g. whether it's a filename or file content) so that functions further down can disambiguate between variables explicitly.

An example: pandas.read_csv

https://pandas.pydata.org/pandas-docs/stable/reference/api/p...

Why not have clearer file, filename or data keyword arguments ? Or just file and call open or stringio if you have the file name or data ? Plenty of more explicit solutions.

Lots of public APIs support this behaviour, it's convenient for users. For example numpy.fromfile will accept a file object or a string.