data:image/png;base64,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

Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _ Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

1Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

ca:notació textual de _ ( )SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _ ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

ca:multi-mapeja _ sobre _ Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

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 all classesrank finals by difficultyhistory,1,yes,1french,2,yes,7english,3,yes,5computer science,4,no,8math,5,yes,2chemistry,6,yes,8history,Ms Lubalin,yes,1