Filter by value wise callback

Prelude:

cb = lambda x: 'Europa' in str(x)

pandas:

mtcars.loc[[cb(x) for x in mtcars.name]]

plydata:

mtcars.query('[cb(x) for x in mtcars.name]') -> 'PandasExprVisitor' object has no attribute 'visit_ListComp'

plydata:

mtcars >> dp.define(mf='[cb(x) for x in name]') >> dp.query('mf') > cb is not defined

mtcars >> dp.define(mf=[cb(x) for x in mtcars.name]) >> dp.query('mf') #note repetition

mtcars >> dp.call('.__getitem__', [cb(x) for x in mtcars.name]) # note repetition

dpylython:

dp.DplyFrame(mtcars) >> dp.sift([cb(x) for x in X.name]) >  does not return

dp.DplyFrame(mtcars) >> dp.sift([cb(x) for x in mtcars.name]) -> list object has no attr evaluate # bool array won't work either

mutate + list comprehension -> no return

dp.DplyFrame(mtcars) >> dp.mutate(mf= [cb(x) for x in mtcars.name]) >> dp.sift(X.mf) # note reference to actual DF

dfply:

mtcars >> dp.filter_by([cb(x) for x in X.name]) _> iter() returned non-iterator of type Intention

mtcars >> dp.mutate(mf=[cb(x) for x in mtcars.name]) >> dp.filter_by(X.mf) # note repetition

mtcars >> dp.mutate(mf=dfply.make_symbolic(cb)(X.name)) >> dp.filter_by(X.mf) -> always True!

Working:

mtcars >> dp.mutate(mf=dfply.make_symbolic(lambda series: [cb(x) for x in series])(X.name)) >> dp.filter_by(X.mf)

mtcars >> dp.filter_by(dfply.make_symbolic(lambda series: pd.Series([cb(x) for x in
series]))(X.name))

Functions have to be symbol aware, and return series for filtering to work

dppd:

dp(mtcars).filter_by([cb(x) for x in X.name]).pd()