![]() Do this on both the left and the right side (i.e. (See below for further explanation.)Īlso, when you see "naked" commas, pretend there's a top-level tuple. If you find that a value doesn't get unpacked, then you'll undo the substitution. Basically, you might find it easier to understand an expression if all the iterables are formatted in the same way.įor the purposes of unpacking only, the following substitutions are valid on the right side of the = (i.e. Since you're talking about evaluating these "by hand," I'll suggest some simple substitution rules. I'll do my best to explain with a few examples. Once you know a few basic rules, it's not hard to generalize them. My apologies for the length of this post, but I decided to opt for completeness. How to correctly deduce the result of such expressions by hand? (a,b), c = 'XY', 3, 4 # ERROR - too many values to unpack *(a,b), c = 'XY', 3 # ERROR - need more than 1 value to unpack *(a, b), = 'this' # ERROR - too many values to unpack *(a, b) = 'this' # ERROR - target must be in a list or tuple ![]() *(a,b) = 'XY' # ERROR - target must be in a list or tuple *(a,b) = 1,2 # ERROR - target must be in a list or tuple (a,b), *c = 1,2,3 # ERROR - 'int' object is not iterable (a,b),c = 1,2,3 # ERROR - too many values to unpack *a, = (1) # ERROR - 'int' object is not iterable *a = # ERROR - target must be in a list or tuple *a, = 1 # ERROR - 'int' object is not iterable *a = (1,2) # ERROR - target must be in a list or tuple *a = 1 # ERROR - target must be in a list or tuple (a,b), c = "XY" # ERROR - need more than 1 value to unpack (a,b), c = "XYZ" # ERROR - too many values to unpack (this is a long list) a, b = 1, 2 # simple sequence assignmentĪ, b = # list asqignmentĪ, b = range(1,5,2) # any iterable will do Note that some expressions are repeated to present the "context". Different postulated effect sizes (multipliers) for the strength of association of e-cigarette use with uptake of cigarettes is provided as odds ratios in brackets (x) the base model assumes no effect of e-cigarettes (solid line), broken lines indicate either a postulated positive (OR > 1) or negative association (OR < 1) between e-cigarette use and smoking uptake.Consider the following expressions. bThe model, data, and description can be found online ( ) briefly, the microsimulation consists of 50 000 agents, each of which represents an individual as defined by the characteristics relevant for the question (ie, age, smoking status, vaping status), who at monthly intervals decide to take up smoking and/or vaping the probabilities that govern these decisions are determined by the user (ie, a multiplier that adjusts the probability of smoking uptake for agents that already vape and vice versa). aObserved values (filled circles) come from the National Youth Tobacco survey 2011–2017. Observed a and modeled past 30-day youth smoking prevalence in the United States 2011–2017, using the youth e-cigarette microsimulation model b.
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