data:image/png;base64,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

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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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Team,OddsColorado Avalanche,9.6Calgary Flames,6.9Florida Panthers,7.8Carolina Hurricanes,6.3Dallas Stars,4.1Toronto Maple Leafs,5.8Las Vegas Golden Knights,3.6Pittsburgh Penguins,5.4Tampa Bay Lightning,6Buffalo Sabres,1.100