I may have led in that direction, and I know R only passingly, not well. But my understanding is that thinking of a data structure that gets parsed by an evaluator, e.g. "do a linear regression with this structure (and a DataFrame)" is better than a lambda.

I'm sure it's possible to describe this with a function, but the Patsy documentation provides something that is probably more helpful:

from patsy import ModelDesc, Term, EvalFactor
form1 = ModelDesc([Term([EvalFactor("y")])],
          [Term([]),
           Term([EvalFactor("a")]),
           Term([EvalFactor("a"), EvalFactor("b")]),
           Term([EvalFactor("np.log(x)")])
           ])

Compare to what you get from parsing the above formula:

form2 = ModelDesc.from_formula("y ~ a + a:b + np.log(x)")

So given those two equivalent structures, we might call, identically:

decision_tree(form1, data=my_df)

decision_tree(form2, data=my_df)

I think if I were writing a high-level data structure rather than a simple parse tree, I might do something more like:

{'dependent': ['y'],

'independent': ['a', Combine('a', 'b'), np.log(x)]}

But whatever the exact structure, basically the syntax just is a mini-language to say which names go in which structural places for an evaluator.

On Mon, Feb 24, 2020 at 3:43 PM Serhiy Storchaka <storchaka@gmail.com> wrote:

24.02.20 22:02, Guido van Rossum пише:
> Hm, that's actually an interesting take. Can you compare it to the kind
> of "quoting" that happens in a lambda? Is there some kind of translation
> of the OP's original example (Lottery ~ Literacy + Wealth + Region) to a
> lambda involving those words?

I think that a named function is more appropriate than a lambda, because
we need also the name of the output parameter:

def Lottery(Literacy, Wealth, Region):

And the most known application of such technique is fixtures in pytest.
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